Rigor ous Analysis of Reacti ve Micr oemulsion
Systems f or Pr ocess Design and Operation
v or gele gt von
M.Sc.
Markus Illner
ORCID: 0000-0003-1500-9588
an der F akultät III – Prozesswissenschaften
der T echnischen Uni v ersität Berlin
zur Erlangung des akademischen Grades
Doktor der Ingenieurwissenschaften
- Dr .-Ing. -
genehmigte Dissertation
Promotionsausschuss:
V orsitzender: Prof. Dr .-Ing. habil. Harald Kruggel-Emden
Gutachter: Prof. Dr .-Ing. habil. Jens-Uwe Repke
Gutachter: Prof. Dr . rer . nat. habil. Reinhard Schomäcker
Gutachter: Prof. Dr .-Ing. habil. Kai Sundmacher
T ag der wissenschaftlichen Aussprache: 07. Mai 2020
Berlin 2020

T o my par ents

A ckno wledgements
A Ph.D. thesis is seldom the work of a single person. For my research I had the pleasure of
working with numerous people, helping me to adv ance both, professionally and personally .
First of all, I would lik e to thank Prof. Dr .-Ing. Jens-Uwe Repk e for letting me finish my Ph.D.
at his department DBT A. I gratefully ackno wledge the opportunities and freedom he provided to
follo w v arious research directions, as well as the possibility to attend international conferences
to broaden my scientific horizon. I am very thankful for his continuous assistance, critical b ut
constructi ve feedback, and nudging in the right direction. Secondly , I would like to thank Prof.
Dr . rer . nat. Reinhard Schomäcker for his co-supervision within the Collabor ative Resear ch
Center TR63 InPR OMPT and the close collaboration, marked by very fruitful discussions and
hints. I am thankful, that Prof. Dr .-Ing. Kai Sundmacher accepted to re view my Ph.D. thesis,
completing the scientific committee. At this point, I would also like to e xpress my gratitude
to wards Prof. Dr .-Ing. Günter W ozny , under whom I initially entered the research project. His
useful advice and inspiring ideas were alw ays helpful to de velop research-wise and encouraging
to look beyond kno wn horizons.
Next, I would like to e xpress my sincere thanks to all the students assisting me throughout
the last years and putting up with endless phase separation experiments and abysses of my
research ideas. In particular I thank: Joris W eigert for assisting greatly on the state estimation
frame work; Jan-Paul Ruik en for bringing Raman spectroscopy into microemulsions; Angela
Alzate for tackling endless hours of phase separation experiments; Peter Köster , the jack of all
trades reg arding plant construction who also assisted on the phase separation model. W ith all
their ideas and ef fort, they ha ve contrib uted to the great success of the project. My deepest
gratitude goes out to the DBT A staf f with special thanks to with Marita, Uta, Ew a, Dietmar ,
Martin, Andreas, Marlos, Philipp, and T im, making practical science possible. Cheers to all
colleagues for fruitful discussions, helping out on numerous mini-plant operations, and making
work jo yful. V ery special thanks go out to my of fice mate Gregor T olksdorf and his successor
V olodymyr K ozachynsk yi for making work a joy and enduring my cursing.
Next in line I thank Dr .-Ing. T obias Pogrzeba, Dr .-Ing. Marcel Schmidt, and Ariane W eber from
the Department of Chemistry at TU Berlin for the very fruitful and close cooperation within

the project. I would lik e to thank Michael Maiwald, Klas Me yer , and Andrea P aul from the
B AM, who ha ve greatly assisted on the analytic side of the project. V ery special thanks go
out to Dr .-Ing. Erik Esche for his continuous assistance, already since my under grad studies,
and bringing me to process systems engineering. His enthusiasm is al ways inspiring and his
outstanding helpfulness on any issue in v aluable.
Finally , this w ork is dedicated to my family , Doreen, and my friends who ne ver stopped belie ving
in my and helping me to get, where I am today .

Abstract
English Summa ry
In recent years, significant ef fort to wards more sustainable chemical processes and products is
noticeable in both academia and industry . Hence, process dev elopment and operation aim for the
implementation of ne w synthesis paths for emerging rene wable feedstocks and w aste pre vention.
Ho we ver , industrial realization of de veloped process concepts is often enough challenging due
to their nov elty , unkno wn fluid properties, and unidentified system phenomena. T o o v ercome
this hurdle and ne vertheless enable the early stage proof of concept for ne w innov ati ve process
concepts, a systematic procedure is de veloped within this thesis. It consists of a rigorous system
analysis and identification of operation challenges and subsequent de velopment of solution
strategies re garding process design and model-based optimal operation strate gies.
As a case study , the hydroformylation of 1-dodecene in surfactant containing multiphase media
is in vestigated. These so called microemulsion systems enable atom ef ficient homogeneously
catalyzed reaction paths, as well as efficient product separation and catalyst rec ycling via ex-
ploitation of their thermomorphic phase separation beha vior . Aiming for a successful realization
of a proof of concept in a mini-plant, the systematic analysis of this innov ati ve solv ent system
firstly focuses on the reaction itself. W ith a de veloped methodology for model adaption, a
mechanistic microkinetic model is successfully augmented reg arding rele v ant influences of the
microemulsion. Secondly , the dynamic phase separation behavior is systematically studied to
identify influencing factors and system inherent challenges, which are hampering process opera-
tion. K ey therein is the analysis of system controllability reg arding a vailable measurements. As
a result, a critical immeasurability of rele v ant surfactant concentrations, an ov erall very small
operation region, and a high sensiti vity of reaction and phase separation performance regarding
disturbances in concentrations are identified as major obstacles.

Based on this, design upgrades of reactor , settler geometry , and recycle configuration are de v el-
oped. Moreov er , process operation is assisted by the de velopment of optimal control trajectories.
As a cornerstone for this, a fully dynamic mini-plant model suitable for optimization is deployed.
K ey elements therein are the de veloped adapted kinetic model and a first time implementation of
the three-phasic separation of microemulsion systems. From the latter , a model-based soft-sensor
for surfactant concentrations is de veloped, which is based on optical observ ation of the phase
e volution of the microemulsion. The realization of dynamic online optimization then contains
a frame work for multi-rate moving horizon state estimation. This is used to obtain consistent
initial v alues for optimization from gi v en mini-plant measurements, a vailable on lar gely v arying
timescales. This way , the calculation of feasible mini-plant control trajectories via dynamic
optimization is possible aiming for product maximization and phase separation stabilization.
Implemented methods and frame works are tested in mini-plant runs. Here, the kinetic model
is successfully v alidated and a successful transfer of the hydroformylation reaction from the
lab into the mini-plant is achie ved. The functionality of the phase separation soft-sensor is
sho wn for v arious operation conditions, gaining vital concentration information from the system.
Additionally , a 70 h optimal mini-plant start-up trajectory is successfully generated to assist
mini-plant operation during critical transient states. The de veloped dynamic online optimization
frame work is then tested using case studies on artificial and plant measurement data. F or both
data sources, generally feasible trajectories are gained and promising results on con ver gence
beha vior and computational time furthermore indicate online applicability .
Finally , these results are combined to obtain the proof of concept for the hydroformylation
of 1-dodecene in microemulsions from a long-term continuous mini-plant campaign of 180 h.
Control of the critical phase separation step was tested for dif ferent operation modes and o ver -
all good oil phase purities of up to 99.5 % (amount of oily components in the separated oil
phase) were obtained. The reaction performance gained a steady-state product yield of up to
38 % with a chemo selecti vity of 92 %, which is in perfect agreement with reference lab-scale
in vestigations.
K eywords: micr oemulsions; hydr oformylation; mini-plant oper ation; multi-r ate
K eywords: mo ving horizon state estimation; dynamic r eal-time optimization

Deutsche Zusammenfassung
Im Zuge des steigenden gesellschaftlichen Be wusstseins für Nachhaltigkeit sind innerhalb v on
Industrie und W issenschaft verstärkte Bestreb ungen hin zu nachhaltigen und grünen chemischen
Prozessen und Produkten sichtbar . Hierbei zielt die Prozessentwicklung verstärkt auf die Reali-
sierung neuer Synthese wege für die Nutzung v on biobasierten Rohstoffen so wie stof flicher Ef fi-
zienz und Abfallv ermeidung ab . Die industrielle Umsetzung solcher neuartigen Prozesskonzepte
wird jedoch oftmals durch deren Neuheitsgrad so wie unbekannte Stoff- und Systemeigenschaf-
ten erschwert. Um diese Hindernisse zu überwinden und dennoch frühzeitig die Nutzbarkeit
neuer innov ati ver Prozessk onzepte zu realisieren, wird innerhalb der v orlie genden Arbeit ein
systematisches V or gehen entwickelt. Es besteht zum einen aus einer rigorosen Analyse und Iden-
tifikation v on systemeigenen Herausforderungen im Prozessbetrieb und zum anderen aus der
maßgeschneiderten Entwicklung v on Lösungsstrategien bezüglich v erbessertem Prozessdesign
so wie modellbasierter optimaler Prozessführungskonzepte.
Als Beispielprozess wird die Hydroformylierung v on 1-Dodecen in einem tensidgestützten
schaltbaren Mehrphasensystem untersucht. Diese sogenannten Mikroemulsionssysteme ermögli-
chen atomef fiziente homogen katalysierte Stoffumw andlungspfade so wie die ef fiziente Produkt-
abscheidung und Katalysatorrezyklierung mittels Phasentrennung. Mit dem Ziel eines erfolgrei-
chen Machbarkeitsnachweises für die Anwendung in einer Miniplant erfolgt die systematische
Analyse des Systems zunächst mit Fokus auf die Reaktion. Unter Nutzung einer entwick el-
ten Methode zur Modelladaptierung wird ein v orhandenes mechanistisches mikrokinetisches
Modell erfolgreich um rele v ante Einflüsse der Mikroemulsion erweitert. Im W eiteren erfolgt
die systematische Untersuchung des Phasentrenn verhaltens der Mikroemulsion, um rele v ante
Einflussgrößen so wie Herausforderungen für den Prozessbetrieb zu identifizieren. K ernelement
ist v or allem die Bewertung der Re gelbarkeit des T rennzustands bezüglich real verfügbarer
Messgrößen. Als Er gebnis werden die fehlende Messbarkeit rele v anter T ensidkonzentrationen,
sehr kleine Betriebsfenster zur Phasentrennung so wie hohe Sensitivitäten v on Reaktion und
Phasentrennung bezüglich K onzentrationsänderungen als kritische Herausforderungen für den
erfolgreichen Anlagenbetrieb identifiziert.
Basierend hierauf werden zunächst Design verbesserungen an Reaktor , Abscheider geometrie
so wie der Prozessrezyklierungen entwickelt. W eiterhin wird die Betriebsführung durch die Ent-
wicklung optimaler Betriebstrajektorien unterstützt. Als wichtiges Fundament wird hierzu ein
v ollständiges dynamisches Miniplant-Modell entwickelt. K ernelemente darin sind das entwi-
ckelte adaptierte Kinetikmodell so wie die erstmalige Implementierung einer dreiphasigen Ent-
mischung v on Mikroemulsionssystemen. Bezüglich letzterem erfolgt die Entwicklung eines

modellbasierten Soft-Sensors für T ensidkonzentrationen unter Nutzung optischer Auswertun-
gen des Phasentrennzustandes. Die Umsetzung einer dynamischen Echtzeitoptimierung umfasst
dann zusätzlich ein Frame work zur Zustandsschätzung auf be wegten Horizonten und stark un-
terschiedlichen Zeitskalen v on Messwerten. Hiermit werden gültige Modellinitialisierungen
ausgehend v on Miniplant-Messdaten ermöglicht. Somit wird die Optimierung künftiger T rajek-
torien für Reglersoll werte der Miniplant erreicht, wobei seitens des Optimierungsproblems auf
Produktmaximierung und Stabilisierung der Phasentrennung abgezielt wird.
Implementierte Methoden und Frame works werden dann in V ersuchen in einer Miniplant getes-
tet. Hier kann eine erfolgreiche V alidierung der Reaktionskinetik so wie der erfolgreiche T ransfer
der Hydroformylierungsreaktion aus dem Labor in die Miniplant aufgezeigt werden. Die Funk-
tionalität des Soft-Sensors zur Phasentrennung wird für di verse T rennzustände in der Anlage
bestätigt, wobei sinn v olle K onzentrationsprädiktionen erhalten werden. Zusätzlich erfolgt die
Berechnung einer 70 h Anfahrtrajektorie, um den Prozessbetrieb in kritischen transienten Zustän-
den zu unterstützen. Die dynamische Optimierung wird dann in F allstudien mittels künstlicher
so wie realer Miniplant-Daten getestet. V ielversprechende Er gebnisse bezüglich des K on ver genz-
verhaltens und Rechenzeit bestätigen dabei eine mögliche Echtzeitanwendung, w obei generell
sinn volle und umsetzbare T rajektorien erhalten werden.
Schlussendlich wurden die entwickelten Strate gien im Rahmen eines 180 h Langzeitbetriebes
der Miniplant genutzt. Hier konnte sehr erfolgreich die Machbark eit der Hydroformylierung von
1-Dodecen in Mikroemulsionen bestätigt werden. Das Aufrechterhalten der kritischen Phasen-
trennung war über den gesamten Zeitraum und di v erse Betriebszustände möglich, wobei sehr
gute Ölphasenreinheiten v on bis zu 99.5 % (Anteil Ölkomponenten in Ölphase) erreicht wurden.
Die Reaktion konnte mit einer hohen stationären Produktausbeute v on 38 % so wie Produkts-
elekti vität von 92 % umgesetzt werden und zeigt zudem eine sehr gute Übereinstimmung mit
Labor -Referenzwerten.
Schlüsselwörter: Mikr oemulsionen; Hydr oformyleriung; Multir aten-Zustandsschätzung
Schlüsselwörter: auf be we gten Horizonten; dyn. Echtzeitoptimierung

Publications
This thesis is partially based on already published contrib utions. In the follo wing, these are
di vided into Journal articles, papers within conference proceedings, oral presentations or posters
with only abstract, and a list of all supervised theses.
Journal Articles
■
D. Müller, E. Esche, T . Pogrzeba, M. Illner, F . Leube, R. Schomäcker, G. W ozny (2015).
Systematic Phase Separation Analysis of Surfactant-Containing Systems for Multiphase
Settler Design. Industrial & Engineering Chemistry Resear ch 54.12, 3205–3217. DOI :
10.1021/ie5049059
■
T . Pogrzeba, D. Müller, M. Illner , M. Schmidt, Y . Kasaka, A. W eber , G. W ozny, R.
Schomäcker, M. Schw arze (2016a). Superior catalyst rec ycling in surfactant based mul-
tiphase systems – Quo v adis catalyst complex?. Chemical Engineering and Pr ocessing:
Pr ocess Intensification 99, 155–166. D O I : 10.1016/j.cep.2015.09.003
■
M. Illner, D. Müller, E. Esche, T . Pogrzeba, M. Schmidt, R. Schomäcker, G. W ozny, J.
-
U.
Repke (2016c). Hydroformylation in Microemulsions: Proof of Concept in a Miniplant.
Industrial & Engineering Chemistry Resear c h 55.31, 8616–8626. D O I : 10.1021/acs.iecr
.6b00547
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D. Müller, M. Illner, E. Esche, T . Pogrzeba, M. Schmidt, R. Schomäck er, L. T . Biegler,
G. W ozny, J.
-
U. Repke (2017). Dynamic real-time optimization under uncertainty of a
hydroformylation mini-plant. Computers & Chemical Engineering 106, 836–848. DOI :
10.1016/j.compchemeng.2017.01.041
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A. Paul, K. Me yer, J.
-
P . Ruiken, M. Illner, D.
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N. Müller, E. Esche, G. W ozny, F . W estad,
M. Maiwald (2017). Process spectroscop y in microemulsions—Raman spectroscopy for
online monitoring of a homogeneous hydroformylation process. Measur ement Science
and T echnolo gy 28.3, 035502. D O I : 10.1088/1361- 6501/aa54f0
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K. Meyer, J.
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P . Ruiken, M. Illner, A. P aul, D. Müller, E. Esche, G. W ozny, M. Maiw ald
(2017a). Process spectroscopy in microemulsions—setup and multi-spectral approach for

reaction monitoring of a homogeneous hydroformylation process. Measur ement Science
and T echnolo gy 28.3, 035501. D O I : 10.1088/1361- 6501/aa54f3
■
T . Pogrzeba, M. Schmidt, N. Miloje vic, C. Urban, M. Illner , J.
-
U. Repke, R. Schomäck er
(2017b). Understanding the Role of Nonionic Surfactants during Catalysis in Microemul-
sion Systems on the Example of Rhodium-Catalyzed Hydroformylation. Industrial &
Engineering Chemistry Resear c h 56.36, 9934–9941. DOI : 10.1021/acs.iecr.7b02242
■
E. Esche, C. Hoffmann, M. Illner, D. Müller, S. Fillinger, G. T olksdorf, H. Bonart, G.
W ozny, J.
-
U. Repke (2017). MOSAIC - Enabling Lar ge-Scale Equation-Based Flow Sheet
Optimization. Chemie Ingenieur T echnik 89.5, 620–635. DOI : 10.1002/cite.201600114
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T . Pogrzeba, M. Illner , M. Schmidt, J.
-
U. Repke, R. Schomäcker (2017a). Microemul-
sion Systems as Switchable Reaction Media for the Catalytic Upgrading of Long-Chain
Alkenes. Chemie Ing enieur T echnik 89.4, 459–463. D O I : 10.1002/cite.201600140
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A. Misra, L. de Souza, M. Illner, L. Hohl, M. Kraume, J.
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U. Repke, D. Thév enin (2017).
Simulating separation of a multiphase liquid-liquid system in a horizontal settler by CFD.
Chemical Engineering Science 167, 242–250. D O I : 10.1016/j.ces.2017.03.062
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M. Illner, M. Schmidt, T . Pogrzeba, C. Urban, E. Esche, R. Schomäcker, J.
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U. Repke
(2018c). Palladium-Catalyzed Methoxycarbon ylation of 1-Dodecene in a T wo-Phase
System: The Path to ward a Continuous Process. Industrial & Engineering Chemistry
Resear c h 57.27, 8884–8894. DOI : 10.1021/acs.iecr.8b01537
■
T . Pogrzeba, M. Illner, M. Schmidt, N. Miloje vic, E. Esche, J.
-
U. Repke, R. Schomäck er
(2019). Kinetics of hydroformylation of 1-dodecene in microemulsion systems using a
rhodium sulfoxantphos catalyst. Industrial & Engineering Chemistry Resear ch . D O I :
10.1021/acs.iecr.8b06157
Conference P ap ers
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D. Müller, M. Illner, A. Fleck, E. Esche, T . Barz, R. Schomäck er, G. W ozny (2014).
Enabling Online-Optimization for a Multiphase System in a Hydroformylation Mini-Plant.
In: Confer ence Pr oceedings of the 20th International Confer ence of Pr ocess Engineering
and Chemical Plant Design 2014 . Ed. by M. Kraume, G. D. W ehinger. Copy Print Berlin.
I S B N : 978-3-00-047364-7
■
M. Illner, T . Pogrzeba, M. Schmidt, D. Müller, E. Esche, R. Schomäcker, J.
-
U. Repke,
Schomäcker, G. W ozn y (2016b). Hydroformylation of 1-dodecene in Microemulsions:
Operation and V alidation of Lab Results in a Miniplant. In: T echnical T ransactions -
Mechanics Issue 1-M (1) 2016 , 107–120. D O I : 10.4467/2353737XCT.16.011.4975

■
D. Müller, E. Esche, M. Illner , T . Pogrzeba, M. Schmidt, R. Schomäcker, L. T . Biegler,
G. W ozny (2016). Dynamic Real-time Optimization Under Uncertainty of a Hydroformy-
lation Mini-plant. In: Computer Aided Chemical Engineering - Pr oceedings of the 26th
Eur opean Symposium on Computer Aided Pr ocess Engineering . V ol. 38, 2337–2342
■
C. Hof fmann, M. Illner , D. Müller, E. Esche, G. W ozny, L. T . Biegler, J.
-
U. Repke (2016).
Moving-horizon State Estimation with Gross Error Detection for a Hydroformylation
Mini-plant. In: Computer Aided Chemical Engineering - Pr oceedings of the 26th Eur o-
pean Symposium on Computer Aided Pr ocess Engineering . V ol. 38. Elsevier , 1485–1490.
D O I : 10.1016/B978- 0- 444- 63428- 3.50252- 6
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M. Illner, E. Esche, J.
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U. Repke (2018a). Optimal Control of Surfactant containing
Multiphase Systems - Challenges and Solution Strategies for a stable Mini-Plant Operation.
In: 13th International Symposium on Pr ocess Systems Engineering (PSE 2018) . Ed. by
M. R. Eden, M. G. Ierapetritou, G. P . T o wler. V ol. 44. Computer Aided Chemical
Engineering 13. Else vier, 739–744. DOI : 10.1016/B978- 0- 444- 64241- 7.50118- X
■
A. Misra, C. Bonamy, L. M. de Souza, L. Hohl, M. Illner, M. Kraume, J.
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U. Repke, D.
Thévenin (2018). A multi-fluid approach to simulate separation of liquid-liquid systems
in a gra vity settler. In: Computer Aided Chemical Engineering - Pr oceedings of the 28th
Eur opean Symposium on Computer Aided Pr ocess Engineering . Else vier, 31–36. DOI :
10.1016/B978- 0- 444- 64235- 6.50008- 5
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J. W eigert, M. Illner , E. Esche, J.
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U. Repke (2018). De velopment of a State Estima-
tion En vironment for the Optimal Control of a Mini-plant for the Hydroformylation in
Microemulsions. In: Chemical Engineering T ransactions . V ol. 70, 973–978
Oral Presentations and P osters with only Abstract
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M. Illner, D. Müller, E. Esche, R. Schomäcker, G. W ozn y (2015). Hydroformylation in
Microemulsions on a Mini-plant Scale: Operation Challenges and Solution Approaches.
In: A CHEMA 2015, F rankfurt, Germany
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A. Paul, J.
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P . Ruiken, K. Me yer, F . W estad, M. Illner, D. Müller, E. Esche, M. Maiwald
(2015). Online Spectroscopy in Microemulsions – A Process Analytical Approach for a
Hydroformylation Miniplant II - Calibration and Prediction by Raman Spectra. In: 11.
K olloquium Arbeitskr eis Pr ozessanalytik 2015, W ien, A ustria
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M. Illner , jan-P aul Ruiken, K. Meyer, D. Müller, E. Esche, A. Paul, J.
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U. Repke, M.
Maiwald (2016f). Systematic Approach for Online Spectroscopy in T echnical Systems -
Experimental Design for Raman Spectroscopy in Micro Emulsions. In: Analytica Confer-
ence and F air , Munic h, Germany

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M. Illner , D. Müller, E. Esche, J.
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U. Repke (2016d). Hydroformylierung in Mikroemul-
sionen – Anforderungen und Lösungsansätze im Miniplant-Betrieb. In: Pr ocessNet-
J ahr estagung und 33. DECHEMA-J ahr estagung der Biotec hnologen 2016, Aac hen, Ger-
many
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M. Illner , T . Pogrzeba, M. Schmidt, R. Schomäcker, J.
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U. Repke (2016e). Hydroformyla-
tion of 1-Dodecene in Microemulsions: Proof of Concept and Long-term Operability on a
Mini-Plant Scale. In: AIChE Annual Meeting, San F rancisco, USA
■
T . Pogrzeba, M. Schmidt, M. Illner, R. Schomäcker (2016b). Microemulsion systems as
smart solvent systems for homogeneous catalysis: Rhodium-catalyzed hydroformylation
of long-chain alkenes in aqueous media. In: AIChE Annual Meeting, San F rancisco, USA
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M. Illner, D. Müller, E. Esche, R. Schomäcker, J.
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U. Repke (2016a). Hydroformylierung
in Mikroemulsionen - Anforderungen und Lösungsansätze im Miniplant-Betrieb. In:
Chemie Ingenieur T echnik - Special Issue: Pr ocessNet-J ahr estagung und 32. DECHEMA-
J ahr estagung der Biotec hnolog en 2016, Aachen, Germany . V ol. 88. 9. W iley, 1334–1334.
DOI : 10.1002/cite.201650346
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M. Illner, K. Meyer, A. P aul, E. Esche, M. Maiwald, J.
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U. Repke (2017b). Operation and
Optimal Control of Multiphase Systems – Hydroformylation in Microemulsions on the
Mini-plant Scale. In: EUR OP A CT 2017 - 4th Eur opean Confer ence on Pr ocess Analytics
and Contr ol T echnology , P otsdam, Germany
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K. Meyer, A. P aul, J.
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P . Ruiken, F . W estad, M. Illner, D. Müller, E. Esche, J.
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U. Repke,
M. Maiwald (2017b). Online Spectroscopy in Microemulsions – A Process Analytical
Approach for a Hydroformylation Mini-plant. In: EUR OP ACT 2017 - 4th Eur opean
Confer ence on Pr ocess Analytics and Contr ol T echnology , P otsdam, Germany
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M. Illner , E. Esche, J.
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U. Repke (2017a). Miniplant-Betrieb und stabile Prozessführung für
Mehrphasenreaktionssysteme – Hydroformylierung langkettiger Alk ene in Mikroemul-
sionen. In: J ahr estr effen der Pr ocessNet-F ac hgemeinsc haft "Pr ozess-, Apparate- und
Anlag entechnik" (P AA T), Würzb ur g, Germany
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D. Zedel, M. Illner, M. Kraume, A. Dre ws (2017). Modelling and prediction of surfactants
remov al from organic solv ents by organic solv ent nanofiltration. In: 1th International
Congr ess on Membr anes and Membr ane Pr ocesses (ICOM 2017), San F rancisco, USA
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M. Illner , E. Esche, J.
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U. Repke (2018b). Prozessführungskonzepte zur Realisierung
neuartiger Prozesse – Hydroformylierung langkettiger Alk ene in Mikroemulsionen. In:
J ahr estr effen der Pr ocessNet-F achg emeinsc haft "Pr ozess-, Appar ate- und Anlagentec h-
nik" (P AA T), Cologne , Germany
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V . K ozachynsk yi, S. Bublitz, M. Illner , J. W eigert, C. Hof fmann, E. Esche, J.
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U. Repke
(2018). Conceptual data model based on an OPC U A architecture, its benefits and imple-

mentation. In: J ahr estr effen der Pr ocessNet-F achg emeinschaft "Pr ozess-, Apparate- und
Anlag entechnik" (P AA T), Cologne , Germany
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E. Esche, M. Illner , R. W ilhelm, J.
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U. Repke (2018). Auswahl und Implementierung
v on optimierenden Prozessführungskonzepten. In: Pr ocessNet-J ahr estagung und 33.
DECHEMA-J ahr estagung der Biotec hnologen 2 018, Aachen, Germany . V ol. 90. Chemie
Ingenieur T echnik 9. W iley , 1234–1234. DOI : 10.1002/cite.201855226
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A. T . Penteado, T . Karsten, M. Illner, J.
-
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M. Stockmann (2019). Systematische Entwicklung eines tensidgestützten mehrphasigen
Lösemittelsystems für die redukti ve Aminierung und Hydroaminomethylierung. Bache-
lors Thesis . T echnische Univ ersität Berlin

Contents
List of Figures i
List of T ables v
List of Symb ols vii
List of Abb reviations xi
1 Intro duction and Motivation 1
1.1 Hydroformylation in Liquid Multiphase Systems . . . . . . . . . . . . . . . . 1
1.2
Liquid Multiphase Reaction Media - Challenges for Process Design and Operation
3
1.3 Research Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Outline of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Theo retical F undamentals and Background Info rmation 7
2.1 Thermodynamics & Characteristics of Microemulsions . . . . . . . . . . . . . 8
2.1.1 Surfactant Adsorption on Surf aces and Micelle Formation . . . . . . . 9
2.1.2 Properties and Phase Beha vior of Microemulsion Systems . . . . . . . 11
2.1.3 Description of the Three-Phase Body . . . . . . . . . . . . . . . . . . 14
2.1.4 Coalescence Beha vior and Separation Dynamics . . . . . . . . . . . . 16
2.2 Kinetics of Rhodium-Catalyzed Hydroformylation Reactions . . . . . . . . . . 19
2.3 Systematic In v estigation of Reacti ve Microemulsion Systems . . . . . . . . . . 21
2.3.1 Status Quo: Process Design & Operation for Reaction in Microemulsions 22
2.3.2 Systematic Analysis of Microemulsion Systems for Process Application 23
2.3.3 Equipment for Multiphasic Separation . . . . . . . . . . . . . . . . . . 28
2.3.4 Systematic W orkflow for the Deri v ation of Adapted Kinetic Models . . 29
2.4 Modeling for Dynamic Processes . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.4.1 Modeling Strategies for Optimization Purpose . . . . . . . . . . . . . . 31
2.4.2 Implementation of Dynamics and Reformulation of Equations . . . . . 32
2.4.3 Parameter Estimation and Identifiability Analysis . . . . . . . . . . . . 34
2.5 Optimal Process Operation Strate gies: State Estimation & Dynamic Optimization 36
2.5.1 Dynamic Real-T ime Optimization and Automation Hierarchy . . . . . 36
2.5.2 State Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3 Systematic Analysis of Reactive Multiphase Systems – Hydrofo rmylation
of 1-do decene in Micro emulsions 45
3.1 Applied Substances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Contents
3.2 Hydroformylation Reaction Network . . . . . . . . . . . . . . . . . . . . . . . 46
3.3 Analysis of Influencing Factors on Reaction Performance in the Mini-Plant . . 48
3.4
Systematic Analysis of Microemulsion Systems for Process Design and Operation
53
3.4.1 Step 1: Definition of System Requirements & Component System . . . 53
3.4.2 Step 2: Influence Identification . . . . . . . . . . . . . . . . . . . . . . 55
3.4.3 Step 3: Prescreening of the System . . . . . . . . . . . . . . . . . . . 56
3.4.4 Step 4a: Unit Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.4.5 Step 4b: Analysis of Controllability of Influencing Factors . . . . . . . 62
3.4.6 Step 5: Full System Mapping . . . . . . . . . . . . . . . . . . . . . . 64
3.5 Summary on System Analysis – Identified Challenges . . . . . . . . . . . . . . 64
4 Derivation of Strategies fo r Pro cess Design & Op eration 65
4.1 Mini-Plant Setup and Redesign . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.1.1 General Remarks on Design Specifications . . . . . . . . . . . . . . . 66
4.1.2 Redesign of Reaction Section . . . . . . . . . . . . . . . . . . . . . . 69
4.1.3 Redesign of Settler and Rec ycle . . . . . . . . . . . . . . . . . . . . . 70
4.2 Process Automation and Analytics . . . . . . . . . . . . . . . . . . . . . . . . 72
4.2.1 Process Monitoring and Control . . . . . . . . . . . . . . . . . . . . . 72
4.2.2 Automated Phase Le v el Detection . . . . . . . . . . . . . . . . . . . . 75
4.2.3 Implemented and Applied Analytics . . . . . . . . . . . . . . . . . . . 76
4.2.4 Communication Structure and Data Management . . . . . . . . . . . . 78
4.3 De velopment of the Dynamic Process Model . . . . . . . . . . . . . . . . . . 81
4.3.1 General Structure and Scope of Modeling . . . . . . . . . . . . . . . . 81
4.3.2 Feed Section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.3.3 First Principles Model of the Reactor Unit . . . . . . . . . . . . . . . . 86
4.3.4 De velopment of an Adapted Kinetic Model . . . . . . . . . . . . . . . 88
4.3.5 Gas Solubility Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.3.6 Phase Separation Model and Soft-Sensor De velopment . . . . . . . . . 97
4.3.7 Settler Model for Dynamic Three-Phase Separation . . . . . . . . . . . 106
4.3.8 Recycle and Product Section . . . . . . . . . . . . . . . . . . . . . . . 109
4.4 Strategies for Optimal Operation . . . . . . . . . . . . . . . . . . . . . . . . . 111
4.4.1 Implementation of Surfactant Soft-Sensor . . . . . . . . . . . . . . . . 111
4.4.2 Multi-Rate State Estimation . . . . . . . . . . . . . . . . . . . . . . . 113
4.4.3 Dynamic Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 124
4.4.4 Continuous Implementation of Optimal Control Strategies . . . . . . . 126
5 Pro of of Concept: Mini-Plant Results and Application of Optimal Control
Strategies 129
5.1
Con ventional Unassisted Mini-Plant Operation and Identification of Operational
Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
5.1.1 Conditions and Standard Operation Modes . . . . . . . . . . . . . . . 130
5.1.2 Mini-Plant Operation Results . . . . . . . . . . . . . . . . . . . . . . 133
5.2 V alidation and Application of Strate gies for Process Design & Operation . . . . 136
5.2.1 V alidation of Kinetic Model . . . . . . . . . . . . . . . . . . . . . . . 137
5.2.2 Applicability of Raman Spectroscopy . . . . . . . . . . . . . . . . . . 140

Contents
5.2.3 Application of Soft-Sensor and Phase Separation Model . . . . . . . . 142
5.2.4 De velopment of Optimal Start-Up T rajectories . . . . . . . . . . . . . 145
5.2.5 Case Study Multi-Rate Moving Horizon State Estimation . . . . . . . . 150
5.3 Successful Realization of Continuous Operation . . . . . . . . . . . . . . . . . 155
5.3.1 Conditions and Operation Strategy - Plant Op 2 . . . . . . . . . . . . . 155
5.3.2 Proof of Concept: Long-T erm Mini-Plant Results . . . . . . . . . . . . 157
5.3.3 D-R TO Application Case Study . . . . . . . . . . . . . . . . . . . . . 161
6 Conclusions and F uture Directions 165
6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
6.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
List of References I
App endix A: System Analysis & Exp erimental Setups A-1
A.1 Kinetic In vestig ations of the Hydroformylation . . . . . . . . . . . . . . . . . A-1
A.2 Experimental Procedures for Phase Separation Analysis . . . . . . . . . . . . . A-2
A.3 Results of the Systematic Phase Separation Analysis . . . . . . . . . . . . . . A-5
App endix B: Mini-plant & Analytics B-1
B.1 Additional Information on Mini-Plant system . . . . . . . . . . . . . . . . . . B-1
B.2 Additional Information Analytics & Experimental . . . . . . . . . . . . . . . . B-3
B.3 Handling and Calculation of Measurement Errors . . . . . . . . . . . . . . . . B-6
App endix C: Mo del Development & Optimal Pro cess Control Strategies C-1
C.1 Reformulations of Equations for Dynamic Models . . . . . . . . . . . . . . . . C-1
C.2 Dynamic Mini-Plant Model – De velopment and F ormulations . . . . . . . . . C-2
C.3 Dynamic Mini-Plant Model – Additional Information . . . . . . . . . . . . . . C-19
C.4 Optimal Operation Strate gies . . . . . . . . . . . . . . . . . . . . . . . . . . . C-21
App endix D: Op eration & Optimization Results D-1
D.1 Calculations and Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1
D.2 Computational Setups and Solv er Settings . . . . . . . . . . . . . . . . . . . . D-4
D.3 Mini-Plant Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-6
D.4 Optimization and State Estimation . . . . . . . . . . . . . . . . . . . . . . . . D-8

List of Figures
1.1 Hydroformylation reaction scheme . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Process Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1 W orkflow system analysis & deri v ation of process design and operation strategies 8
2.2 Surfactant adsorption and interfacial tension . . . . . . . . . . . . . . . . . . . 10
2.3 Unfolded phase prism showing the binary phase diagrams . . . . . . . . . . . . 12
2.4 Isothermal Gibbs triangles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.5 Gibbs Phase Prism and Kahlweit’ s Fish Diagram . . . . . . . . . . . . . . . . 14
2.6 Gibbs triangle for three phasic separation . . . . . . . . . . . . . . . . . . . . 15
2.7 Interfacial tension in three-phase body ov er temperature . . . . . . . . . . . . . 16
2.8 Separation dynamics and viscosity in ternary system . . . . . . . . . . . . . . 17
2.9 Coalescence beha vior along the three-phase body . . . . . . . . . . . . . . . . 19
2.10 Structure of the hydrophilic Rh-SulfoXantPhos catalyst complex . . . . . . . . 20
2.11 Mechanism rhodium-catalyzed hydroformylation . . . . . . . . . . . . . . . . 21
2.12 Step 1 for the systematic phase separation system analysis . . . . . . . . . . . 24
2.13 Step 2 for the systematic phase separation system analysis . . . . . . . . . . . 24
2.14 Step 3 for the systematic phase separation system analysis . . . . . . . . . . . 25
2.15 Step 4 for the systematic phase separation system analysis . . . . . . . . . . . 26
2.16 Step 5 for the systematic phase separation system analysis . . . . . . . . . . . 27
2.17 Schematic settler with internals . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.18 W orkflo w for kinetic model adaption . . . . . . . . . . . . . . . . . . . . . . . 30
2.19 Curvature of dif ferent sigmoidal function implementations . . . . . . . . . . . 34
2.20
Process automation hierarchy and control loop for dynamic real-time optimization
37
2.21 Comparison of estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.1 Reaction network for the hydroformylation of 1-dodecene . . . . . . . . . . . 47
3.2 Reference kinetic trajectory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.3 Influence of ligand concentration on hydroformylation of 1-dodecene . . . . . 50
3.4 Influence of stopped stirrer operation on hydroformylation of 1-dodecene . . . 51
3.5 Influence of microemulsions phase beha vior on hydroformylation of 1-dodecene 52
3.6 Influence of surfactant concentration on hydroformylation of 1-dodecene . . . . 53
3.7 Phase separation impedance through catalyst acti v ation . . . . . . . . . . . . . 56
3.8 Phase Beha vior and feasibility . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.9
Phase separation time ov er temperature for mixture
α = 50 %
-
γ = 8 %
-
Y = 0 % 58
3.10 Phase fraction ev olution for mixture α = 50 % - γ = 8 % - Y = 0 % . . . . . . . 59
3.11 Phase diagram for 1-dodecene-aq. catalyst solution-Marlipal ® 24/70 . . . . . . 60
3.12 Settler operation with knitted wire meshs . . . . . . . . . . . . . . . . . . . . 61
i

List of Figures
3.13 Dense surfactant layer b uild-up and liquid crystals in settler . . . . . . . . . . . 62
4.1 Overvie w of applied strategies for process design and operation . . . . . . . . . 65
4.2 Simplified process flo w diagram . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.3 High pressure section mini-plant . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.4 Modular settler configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.5 Ne w mixer -settler design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.6 V isualization of the process automation of the mini-plant . . . . . . . . . . . . 75
4.7 Flo w diagram of the image processing script for phase le vel detection . . . . . 76
4.8 Process automation communication structure and information exchange . . . . 80
4.9 Model scheme for the hydroformylation mini-plant . . . . . . . . . . . . . . . 82
4.10 T rigger Functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.11 Model scheme for the feed section . . . . . . . . . . . . . . . . . . . . . . . . 85
4.12 Model scheme for the reactor . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.13 Comparison initial kinetic model and exp. data - v ar . temperature and catalyst . 90
4.14 Comparison initial kinetic model and exp. data - Ligand, surf actant v ariation . . 91
4.15 Parity plot for adapted kinetic model and e xperimental data . . . . . . . . . . . 93
4.16 Comparison of adapted kinetic model and experimental data . . . . . . . . . . 94
4.17 Surface plots gas solubility . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.18 Parity plot for solubility of CO . . . . . . . . . . . . . . . . . . . . . . . . . . 97
4.19 General approach for phase separation model dev elopment . . . . . . . . . . . 98
4.20 Three-phase region boundary temperature model . . . . . . . . . . . . . . . . 100
4.21 Modeling strategy for the phase v olume fraction e v olution . . . . . . . . . . . 100
4.22 Surface plots of the phase volume fraction models . . . . . . . . . . . . . . . . 102
4.23 Modeling strategy for the excess phase composition . . . . . . . . . . . . . . . 103
4.24 Experimental determination of temperature dependent cmc . . . . . . . . . . . 104
4.25 Surface plot of model for the excess phase composition . . . . . . . . . . . . . 105
4.26 Model scheme for the settler . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.27 Scheme for the dynamic activ ation of settler phase drains . . . . . . . . . . . . 108
4.28 Model scheme for the recycle and product section . . . . . . . . . . . . . . . . 109
4.29 Implementation of concentration soft-sensor into DCS . . . . . . . . . . . . . 112
4.30 Simultaneous state estimation on full measurement set . . . . . . . . . . . . . 120
4.31 Multi-rate state estimation frame work . . . . . . . . . . . . . . . . . . . . . . 123
4.32 Routine and time frame for D-R TO calculations . . . . . . . . . . . . . . . . . 128
5.1 Con ventional mini-plant operation: temperatures and pressure . . . . . . . . . 131
5.2 Con ventional mini-plant operation: recycle . . . . . . . . . . . . . . . . . . . 132
5.3 Con ventional mini-plant operation: yield and con version . . . . . . . . . . . . 133
5.4 Con ventional mini-plant operation: reactant concentrations . . . . . . . . . . . 134
5.5 Con ventional mini-plant operation: quality of phase separation . . . . . . . . . 135
5.6 Comparison simulation vs. plant: reaction performance for batch operation . . 138
5.7 Comparison simulation vs. plant: reactant mass fractions . . . . . . . . . . . . 138
5.8 Comparison simulation vs. plant: reaction performance for continuous operation 140
5.9 Raman results mini-plant operation . . . . . . . . . . . . . . . . . . . . . . . . 141
5.10 Application case study phase separation soft-sensor . . . . . . . . . . . . . . . 143
ii

List of Figures
5.11 Soft-sensor performance: Online Phase separation state tracking . . . . . . . . 144
5.12 Reaction con version for three dif ferent start-up optimization scenarios . . . . . 147
5.13 Optimal start-up trajectory: feed streams . . . . . . . . . . . . . . . . . . . . . 147
5.14 Simulation and optimal start-up trajectory: Comparison settler lev els . . . . . . 148
5.15 Optimal start-up trajectory: recycle streams and temperatures . . . . . . . . . . 149
5.16 State estimation case study: Le vels . . . . . . . . . . . . . . . . . . . . . . . . 152
5.17 State estimation case study: phase separation and rec ycle . . . . . . . . . . . . 153
5.18 State estimation case study: reactant concentrations . . . . . . . . . . . . . . . 154
5.19 Successful mini-plant operation: recycle . . . . . . . . . . . . . . . . . . . . . 157
5.20 Successful mini-plant operation: temperatures and pressure . . . . . . . . . . . 158
5.21 Successful mini-plant operation: yield and con version . . . . . . . . . . . . . . 158
5.22 Successful mini-plant operation: selecti vity . . . . . . . . . . . . . . . . . . . 159
5.23 Successful mini-plant operation: phase separation quality . . . . . . . . . . . 160
5.24 D-R TO Case Study Plant Op 2: yield . . . . . . . . . . . . . . . . . . . . . . . 163
5.25 D-R TO Case Study Plant Op 2: concentrations . . . . . . . . . . . . . . . . . 164
A.1 Influence of catalyst concentration on hydroformylation of 1-dodecene . . . . . A-1
A.2 Influence of stirrer speed on hydroformylation of 1-dodecene . . . . . . . . . . A-2
A.3 Experimental setup prescreening of phase separation - Glass reactor . . . . . . A-3
A.4 Experimental setup for Shake & W ait experiments . . . . . . . . . . . . . . . . A-3
A.5 Experimental setup for full phase separation mapping . . . . . . . . . . . . . . A-4
A.6 Comparison separation performance depending on catalyst acti v ation . . . . . . A-5
B.7 Reactor Sampling System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-2
B.8 Inte gration of micro-GC into the mini-plant . . . . . . . . . . . . . . . . . . . B-3
B.9 Calibration test stand for Raman spectroscopy . . . . . . . . . . . . . . . . . . B-5
C.10 Normalization of concentration obtained from the excess phase model . . . . . C-17
C.11 Comparison recycle stream setpoints and measurements . . . . . . . . . . . . . C-22
C.12 Flo wchart of simulation frame work for state estimation initialization . . . . . . C-25
C.13 Flo wchart of dynamic optimization framew ork . . . . . . . . . . . . . . . . . C-26
D.14 Residence time definition mini-plant . . . . . . . . . . . . . . . . . . . . . . . D-2
D.15
Con ventional mini-plant operation: feed rates 1-dodecene, catalyst, and surfactant
D-6
D.16 Con ventional mini-plant operation: recycle . . . . . . . . . . . . . . . . . . . D-6
D.17 Successful mini-plant operation: feed rates 1-dodecene, catalyst, and surfactant D-7
D.18 Successful mini-plant operation: recycle . . . . . . . . . . . . . . . . . . . . . D-7
D.19 SSuccessful mini-plant operation: reactant concentrations . . . . . . . . . . . . D-8
D.20 D-R T O Case Study Plant Op 2: phase separation . . . . . . . . . . . . . . . . D-11
D.21 D-R T O Case Study Plant Op 2: settler le vels . . . . . . . . . . . . . . . . . . . D-11
D.22 D-R T O Case Study Plant Op 2: temperatures . . . . . . . . . . . . . . . . . . D-12
iii

List of T ables
3.1 Applied substances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.3 Influencing factors on reaction performance . . . . . . . . . . . . . . . . . . . 49
3.4 Ranges of interest for influencing factors on phase separation beha vior . . . . . 55
3.5 Sensiti vity of phase separation influencing factors on operation re gion . . . . . 60
3.6 Controllability analysis for phase separation operation . . . . . . . . . . . . . . 63
3.7 Experimental ranges of factors for full system mapping . . . . . . . . . . . . . 64
4.1 Installed sensor de vices in the mini-plant . . . . . . . . . . . . . . . . . . . . . 73
4.2 Dimensions and v olumes of mini-plant containers and piping systems . . . . . 74
4.3 Applied analytics and detectable substances . . . . . . . . . . . . . . . . . . . 77
4.4 Indexing for the dynamic mini-plant model . . . . . . . . . . . . . . . . . . . 82
4.5 Prepared samples for gas solubility experiments . . . . . . . . . . . . . . . . . 95
4.6 Plant measurements for state estimation, sampling frequencies, and delays . . . 114
5.1 Operation schedule and controls for standard operation modes . . . . . . . . . 130
5.2 Feed composition batch reaction mini-plant . . . . . . . . . . . . . . . . . . . 137
5.3 Feed composition for plant simulation Plant Op 1 . . . . . . . . . . . . . . . . 139
5.4 Properties measurement data for state estimation case study . . . . . . . . . . . 150
5.5 Computational seconds for the state estimation case study . . . . . . . . . . . . 151
5.6 Operation schedule and controls for successful long-term plant operation . . . . 156
5.7 Initials D-R T O case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
5.8 Properties measurement data for D-R TO case study . . . . . . . . . . . . . . . 162
A.1 Experimental results of full system mapping . . . . . . . . . . . . . . . . . . . A-6
B.2 Dimensions and v olumes of mini-plant containers and piping systems . . . . . B-1
B.3 GC calibration results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-3
C.4 Inde xing for the dynamic mini-plant model . . . . . . . . . . . . . . . . . . . C-2
C.5 Kinetic experiments for initial kinetics . . . . . . . . . . . . . . . . . . . . . . C-5
C.6 P arameter initials and bounds for initial kinetics . . . . . . . . . . . . . . . . . C-6
C.7 Kinetic experiments for adapted kinetics . . . . . . . . . . . . . . . . . . . . . C-7
C.8 P arameter initials and bounds for adapted kinetics . . . . . . . . . . . . . . . . C-8
C.9 Final kinetic parameters for the adapted model . . . . . . . . . . . . . . . . . . C-9
C.10 Gas solubility data CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-10
C.11 Gas solubility data CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-10
C.12 Gas solubility model parameters . . . . . . . . . . . . . . . . . . . . . . . . . C-11
C.13 T emperate boundary model parameters . . . . . . . . . . . . . . . . . . . . . . C-11
C.14 Phase volume fraction model parameters . . . . . . . . . . . . . . . . . . . . . C-12
v

List of T ables
C.15 Phase volume fraction model parameters . . . . . . . . . . . . . . . . . . . . . C-13
C.16 ν ik : number of structural groups of type k in molecule i . . . . . . . . . . . . . C-13
C.17 Rel. van der W aals v alues for structural groups for UNIF A C . . . . . . . . . . C-13
C.18 a nm : interaction parameter for pair structural group n and structural group m . . C-14
C.19 Surface T ension Measurements 1-dodecene/tridecanal . . . . . . . . . . . . . . C-14
C.20 Surface T ension Measurements 1-dodecene . . . . . . . . . . . . . . . . . . . C-15
C.21 Surface T ension Measurements W ater Phase . . . . . . . . . . . . . . . . . . . C-16
C.22 Parameters for density correlations . . . . . . . . . . . . . . . . . . . . . . . . C-19
C.23 Model feed tank composition . . . . . . . . . . . . . . . . . . . . . . . . . . . C-19
C.24 Controller and trigger parameters . . . . . . . . . . . . . . . . . . . . . . . . . C-20
C.25 Design variables tanks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-20
C.26 Approximation result of δ y
δ x . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-23
C.27 Continued: approximation result of δ y
δ x . . . . . . . . . . . . . . . . . . . . . . C-24
D.28 Control initials and bounds for start-up optimization . . . . . . . . . . . . . . . D-8
D.29 Results start-up optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . D-9
D.30 Results start-up optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . D-10
vi

List of Symb ols
Gr eek Symbols
Symbol Description Unit
α Oil to water ratio wt.-%
∆ Dif ference v arious
ε Small positi ve scalar v alue –
Γ Surface e xcess concentration mol m − 2
γ Acti vity coef ficient –
γ Surfactant mass fraction wt.-%
µ Chemical potential J mol − 1
Φ Objecti ve function v arable
Φ V olume fraction %
ψ Catalyst pre-equilibia rate constant g L − 1
ρ Density g L − 1
σ Interfacial tension N m − 1
σ Standard de viation –
σ 2 Parameter or measurement v ariance –
τ Residence time h
θ Parameter v ector –
Indices
Symbol Description
cp Index collocation points
fe Index finite elements
i Index for components
k T ime step inde x
m Number of states
n Parameter inde x
r Index of reaction
s Index for streams
u Index for units
vii

List of Symbols
u Index iteration steps
Constants
Symbol Description Unit
R Uni versal gas constant = 8 . 31446 J mol − 1 K − 1
Latin Symbols
Symbol Description Unit
Λ Scaling parameter –
τ Collocation polynom root –
Θ Measurement sensiti vity matrix –
A Area m 2
a Acti vity –
c Concentration mol L − 1
E Acti viation energy J mol − 1
E Manipulable v ariable set –
F Stream g L − 1
f Arbitrary function v alue –
G Free enthalpy J
g Arbitrary equality constraint –
g Molar Gibbs enthalpy J mol − 1
h Arbitrary inequality constraint –
H U Hold-up g
I Integrator control de viation –
K Inhibition coef ficient –
k re f Enhancement factor –
K I Controller parameter –
K P Controller gain –
L Le vel m
l Collocation polynom –
M Molar mass g mol − 1
m Mass g
n Amount of substance mol
P Parameter –
P Probability –
p Pressure bar
Q Process noise cov ariance matrix –
R Measurement noise cov ariance matrix –
viii

List of Symbols
r Reaction rate mol L − 1 min
R 2 Coef ficient of determination –
S Selecti vity – or %
S Sensiti vity matrix –
Sl ack Small scalar v alue –
T T emperature K
T RI G Sigmoidal function –
u Control v araible –
V V olume m 3
v Measurement noise –
v V elocity m s − 1
w Mass fraction – or wt.-%
w Process noise –
X Con version – or %
X State vector –
x Arbitrary v ariable –
x Mole fraction – or mol.-%
Y Measurements vector –
Y Y ield – or %
y State v ariable –
Dimensionless Numbers
Symbol Description Definition
Re Reynoldszahl wd ρ
η
Superscripts
Symbol Description
cmc Critical micelle concentration
Conti Continuous operation
em Emulsion phase
eq equilibrium
e x Excess phase
E Start point or initial
Gas Gas feed section
II Phase/State 2
I Phase/State 1
Lig Ligand
ix

List of Symbols
LM Ligand to metal ratio
Lvl Le vel
L Liquid
Mix Emulsion phase
mol Molar v alue
Oil Oil phase
PhS Phase separation
Reactor Reactor section
Rh Rhodium catalyst
SP Setpoint
Surfactant Surfactant
tot T otal
V V apor
W ater W ater phase
Subscripts
Symbol Description
cat Catalyst
F eed Feed section
Hyfo Hydroformylation
LB Lo wer bound
l Lo wer limit
Out Outlet
Rec Recycle
R Reaction, Reactor
Settler Settler section
Sol Solubility
UB Upper bound
u Upper limit
x

List of Abb reviations
AE A lgebraic E quation system
APC A dv anced P rocess C ontrol
AS A utomation S ystem
cmc c ritical m icelle c oncentration
cp c ritical p oint
cst c ritical s olution t emperature
D AE D if ferential- A lgebraic E quation (system)
DCS D istrib uted C ontrol S ystem
D-R TO Dynamic R eal- T ime O ptimization
FFKM Perfluoroelastomer
FKM Fluoroelastomer
FTIR F ourier - T ransform I nfra- R ed
GC G as C hromatography
HSV H ue S aturation V alue
ICP I nducti vely C oupled P lasma
KPI K ey P erformance I ndicator
LB L o wer B ound
LLE L iquid- L iquid- E quilibrium
MB M ass B alance
MFC M ass F lo w C ontroller
ME M icro e mulsion
MES M icro e mulsion S ystem
xi

List of Symbols
MHE M oving H orizon E stimator
MILP M ixed I nte ger L inear P rogramming
MINLP M ixed I nte ger N on L inear P rogramming
MPC M odel P redicti ve C ontrol
NLP N on L inear P rogramming
NMR N uclear M agnetic R esonance
ODE O rdinary D if ferential E quation (system)
OPC O pen P latform C ommunication
OPC D A O pen P latform C ommunication D irect A ccess
PE P arameter E stimation
PIT P hase I n version T emperature
PLS P artial L east S quares
PTFE P oly T etra F luoro E thylene
R OI R egion O f I nterest
RSME R oot M ean S quared E rror
R TO R eal- T ime O ptimization
STD ST andard D e viation
SX S ulfo X ant P hos
TDC T ri d e c anal
UB U pper B ound
UNIF A C UNI versal F unctional Group A cti vity C oef ficients
xii

1 Intro duction and Motivation
Since the introduction of the twelve principles of green chemistry by Anastas et al. (2000) in the
early 1990s, continuously increasing ef forts tow ards more sustainable chemical processes and
products are perceptible in both academia and industry . F ollo wing these guidelines, the imple-
mentation of ne w synthesis paths for rene wable feedstocks and w aste prev ention are stri ved for .
Moreov er , the en vironmental impact of applied reactants, solvents, or additi ves is required to be
as lo w as possible. From a process dev elopment perspecti ve, homogeneous catalysis is an excep-
tionally important basis for sustainable processes, since ne w synthesis routes utilizing renew able
feedstocks can be unlocked. Additionally , desirable system features such as high atom ef ficiency
and tar get product selecti vity , as well as milder reaction conditions are attainable. Ho wev er ,
deploying such systems for lar ge-scale industrial applications can be delayed or e ven widely
inhibited due to their complexity and unkno wn system phenomena or interactions (Iv anko vi ´
c,
2017). Hence, it is necessary to address these challenges in a holistic approach comprising
process design and suitable process operation strategies. For this, a systematic system analysis,
as well as integrated lab-scale and mini-plant scale in vestig ations are crucial.
1.1 Hydrofo rmylation in Liquid Multiphase Systems
W ithin the chemical industry , the hydroformylation is one of the most important applications
of homogeneous catalysis with lar ge-scale production capacities exceeding 12 million metric
tons per year worldwide (2009) (Bahrmann et al., 2013). Predominantly , short-chained alkenes
(propene, butene) are con verted, yielding essential intermediates. From these, a wide range
of higher v alue products, such as plastifiers, detergents, surfactant alcohols, and fla v orings are
produced (Cornils et al., 1994; W . A. Herrmann, 2002). The reaction itself was disco vered in
1937 by Otto Roelen while studying the Fischer -T ropsch-Synthesis. It constitutes the reaction
of olefins with carbon monoxide and hydrogen to wards aldehydes in the presence of transition
metal catalysts (Roelen, 1938; Gesellschaft Deutscher Chemiker, 2013). F ollo wing the general
reaction scheme in Fig. 1.1, the hydroformylation of terminal olefins leads to a functionalization
by the introduction of a formyl group. A product mixture of linear and branched aldehydes is
1

1 Introduction and Moti v ation
obtained of which the terminal products are usually fa vored. According to Franke et al. (2012),
rele v ant catalysts for industrial applications are based on rhodium or cobalt, considering the
former as superior due to higher acti vity . By applying tailored ligands the reaction performance
is then dri ven to wards economically f a vorable high chemo- and re gioselectivities.
R
olefin
+ CO/H 2
synthesis gas
[Rh] catalyst R H
O
linear aldehyde
or R
H
O
branched aldehyde
Fig. 1.1: General hydroformylation reaction scheme. R denotes an alkyl group.
Alongside superior reaction performance features homogeneous catalysis ho wev er also holds a
major drawback: the necessity of ef ficient catalyst recovery from product streams. Considering
the high catalyst costs of 7858 $/Oz.
1
already catalyst leaching in the order of parts per million
causes annual financial loss of se veral million euros for a 400 kt plant application (W iese et al.,
2006). Significant ef forts regarding equipment and ener gy demand are thus required to av oid
catalyst leaching. This led to the de velopment of current state of the art industrial processes,
such as the Ruhrchemie / Rhône-Poulenc process (Cornils et al., 1982; Cornils et al., 1995;
Auch-Schwelk et al., 2001). Here, two-phase reaction media are applied in which v aluable
rhodium catalysts are immobilized in an aqueous phase through ligand modification. Hence,
ef ficient catalyst recycling and mild reaction conditions are enabled. Ho we v er , such a process is
limited to short-chained substrates (C<5). Longer -chained olefins (C>5) are thus still con verted
using more rob ust but less acti ve and inselecti ve cobalt catalysts in single phase reactions at
se vere process conditions (300 bar , 200 ◦ C) (Börner et al., 2016).
T o ov ercome this dra wback and enable the ef ficient con version of also long-chained substrates
from rene wable feedstocks, new process concepts for homogeneous catalysis in liquid multi-
phase systems are stri ved for . Hereof, the question arises how to mer ge two contradictory design
features of a reaction mixture: perfect miscibility for reaction and perfect separability for sep-
aration. Major adv ancements in this direction stem from academic research and aim for the
application of thermomorphic solv ent systems (Behr et al., 2005; Zagaje wski et al., 2014), ionic
liquids (Haumann et al., 2008) and its combination with super critical CO
2
(W ebb et al., 2003),
or ne w reactor concepts (W armeling et al., 2017).
Another very promising approach in v olves the application of surfactant-based tunable solv ent
systems and will be in v estigated within this thesis – hydroformylation in microemulsions. The
main idea here is to immobilize the rhodium catalyst in an aqueous phase via ligand modification.
Surfactant are then applied to enable necessary lar ge interfacial areas between the long-chained
1
rhodium price 25
th
March 2019, source: https://tradingeconomics.com/commodity/rhodium, accessed 2020-01-09
2

1.2 Process De velopment for Liquid Multiphase Reaction Media
oily substrates and the catalyst solution (T inucci et al., 1990). Besides promising lab-scale
in vestigations (Vyv e et al., 1999; Haumann et al., 2002; Miyagaw a et al., 2005), extensi ve ef-
forts to wards a holistic process de velopment ha ve been made within the Collabor ative Resear ch
Center TR63 InPR OMPT
2
. Figure 1.2 visualizes the de veloped general process concept for the
production of long-chained aldehydes. Initially , the substrate and aqueous catalyst solution are
emulsified in the reactor through application of a surfactant – a macroscopically homogeneous
microemulsion system is formed. The hydroformylation reaction is then enabled through the in-
sertion of synthesis gas and carried out at o verall mild process conditions. Follo wing the reactor ,
a separation unit is applied to e xploit the thermomorphic separation beha vior of microemulsions
(M. Müller et al., 2013). Through temperature adjustment, a multiphasic separation in a gra vity
settler is achie ved, which ideally allo ws for the separation of a highly pure oily phase, as well as
the ef ficient recycling of the v aluable rhodium catalyst and surfactants.
Lig
Lig
Rh-Cat
CO, H 2
Surfactant
Alkene
Catalyst
Oil phase
W ater phase
Educt/Product
Recycle (Catalyst, Surfactant)
Reactor
Settler
Micellar phase

Fig. 1.2: Process Concept for the hydroformylation in microemulsions (Illner et al., 2018a).
Looking at the twelve principles for green chemistry , this process concept offers great potential,
since it enables the ener gy ef ficient con version of long-chained rene wable substrates via cataly-
sis. Moreov er waste pre vention and utilization of safe solv ents (water) is ensured. Howe ver , its
viability reg arding reaction performance, product separation, and catalyst recycling has yet to be
analyzed in detail. Hence, as part of this thesis the operability of such a system is in vestigated
for a technical mini-plant system (Illner et al., 2016c).
1.2 Liquid Multiphase Reaction Media - Challenges fo r
Pro cess Design and Op eration
As stated by Illner et al. (2018c) commercial applications of liquid multiphase systems for ho-
mogeneous catalysis root back to the 1980s. Prominent examples are the already introduced
2 https://www .inprompt.tu-berlin.de/
3

1 Introduction and Moti v ation
Ruhrchemie / Rhône-Poulenc process or the Shell higher olefin process (SHOP) for the produc-
tion of alpha-olefins via ethene oligomerization using nickel catalyst homogeneously dissolv ed
in polar solv ents such as 1,4-b utanediol (Lutz, 1986; Keim, 2013). Ho we ver , applications are
limited to two-phase systems. Actual industrial-scale applications of innov ati ve reaction media,
such as microemulsions, are scarce. Thus, the question is raised, what hinders the implemen-
tation of ne w process concepts in v olving liquid multiphase reaction media and ho w can their
readiness for the chemical industry be improv ed?
T aking a closer look at homogeneous catalysis and the requirements for economically viable
processes using such systems, one major aspect becomes apparent: “the major challenge for
most multiphase reaction media lies in quantitati ve rec ycling and stability of the e xpensi ve cata-
lyst (...) within a viable b ut low en vironmental impact multiphase system” (Illner et al., 2018c).
Reg arding this, Rösler et al. (2018) provide an e xemplary re vie w of methods for water -based
liquid multiphase systems for homogeneous catalysis and their de velopment to wards continuous
catalyst recycling. From this, se v eral general challenges arise:
■
Homogeneous catalysis is characterized by a high le vel of comple xity with underlying
catalytic cycles, as well as se v eral possible catalyst decomposition routes (Leeuwen et al.,
2011, p. 1 f f.). Applied to nov el multiphase reaction media complexity increases e ven
more, since the chemical matrix of the actual catalyst solvent changes dynamically and
influences of substances appear (Brunsch et al., 2012; Hamerla et al., 2013b).
■
Fluid properties for systems, like microemulsions, as well as substrates from ne w sus-
tainable feedstocks are often widely unkno wn and only accessible by extensi ve studies
(Kahlweit et al., 1987).
■
The measurability of all necessary process v ariables for process application might not be
gi ven or is only a vailable through the application of adv anced process analytical tools,
such as spectroscopy .
■
Early stage applications of optimizations methods for process design or operability analy-
sis might be dif ficult, since reliable and predictiv e models are not av ailable (Mitsos et al.,
2018). This is for example true for the desired application of microemulsion systems, for
which at the moment no predicti ve models for multiphasic separation beha vior exist.
■
The conceptual design of nov el multiphase systems initially relies on well-defined lab-
scale in vestigation and often omits characteristics of dynamic operations or disturbances.
■
The integration of reaction and separation using internal recycles is crucial since un-
kno wn interaction phenomena might occur , such as accumulation of components and
subsequently altered reaction beha vior (Illner et al., 2016b).
4

1.3 Research Goals
Thereof, two main conclusions can be drawn: Follo wing standard approaches for conceptual
process design, which initially rely on well-defined lab-scale experiments, the general viabil-
ity of such inno v ati ve multiphas e reaction media cannot be suf ficiently analyzed. The early
stage integration of mini-plant e xperiments is thus crucial. Moreov er , the o verall feasibility
of those techniques is then often only attainable by an integrated deplo yment of (optimal) pro-
cess design and process operation methods to meet their complexity and dynamic operation
characteristics.
1.3 Resea rch Goals
Referring to the afore mentioned challenges for establishing nov el process concepts for liquid
multiphase reaction media, the ov erarching research goal for this thesis is gi ven:
The main purpose is to present a holistic guideline for rigorous system analysis to identify
system inherent phenomena and arising challenges reg arding the operability of larger scale
technical realizations. This way , a tailored and efficient de v elopment of solution approaches
reg arding process design and optimal operation is enabled. Hence, not only a faster realization
of process applications of liquid multiphase reaction media is aimed for , b ut also the required
ef forts to wards their operability can be e v aluated at an early stage.
W ithin this thesis, a first approach to this is realized and applied for the hydroformylation of
1-dodecene in microemulsions. For this purpose, se veral sub-objecti ves are deri ved, which
represent major de velopments to achie ve the main research goal:
■
an approach for the systematic analysis of the phase separation beha vior of microemul-
sions systems reg arding their controllability and suitability for continuous processes
■ a thermodynamically founded model for the three-phase separation of microemulsions,
■ adequate separation unit design applicable for microemulsion systems,
■ the de velopment of a soft-sensor to enable inference of immeasurable concentrations,
■ a systematic approach to formulate adapted macrokinetic models,
■
and a frame work for online state estimation and calculation of optimal trajectories consid-
ering measurement data from a mini-plant system a vailable on dif ferent time scales.
5

1 Introduction and Moti v ation
1.4 Outline of Thesis
T o achiev e these objecti v es
Chapter 2
initially provides rele v ant theoretical fundamentals and
background information. Therefore, a brief re vie w of the status quo for process design and
operation strategies for surfactant based liquid multiphase systems is pro vided. Thermodynamic
fundamentals and coalescence beha vior of microemulsions are mandatory and thus revie wed.
Like wise, the kinetics and the catalytic cycle for the hydroformylation reaction are presented.
Subsequently , guidelines for the systematic analysis of microemulsion systems are gi ven, in-
cluding microemulsion separation beha vior , as well as a kinetic model adaption methodology .
Process model formulation and reformulation strategies for process optimization are outlined.
Finally , principles for chemical plant automation and the implementation of dynamic real-time
optimization and state estimation are gi ven.
In
Chapter 3
the rigorous system analysis for the hydroformylation of 1-dodecene in microemul-
sion is carried out, focusing mainly on the hydroformylation reaction and the phase separation
characteristics of the microemulsion. Both aspects are analyzed in close regard of the application
in a technical mini-plant system and its operability .
Chapter 4
then highlights the de velopment of tailored process design and operation strate gies.
Firstly , the setup of a mini-plant system and impro v ements of units are gi ven. In addition, the
implementation of process analytics, automation, and communication structures are sho wn. A
dynamic mini-plant model is deri ved based on first principles and empiric correlations. Therein,
a first-time modeling approach for the three-phasic separation of microemulsions including com-
pound distrib ution is shown. Additionally , an adapted kinetic model for the hydroformylation
of 1-dodecene based on a mechanistic model is de veloped. Finally , methods for optimal process
operation are presented: an online soft-sensor implementation for immeasurable concentrations,
a moving horizon state-estimator handling multi-rate measurements, and a frame work for dy-
namic real-time optimization for mini-plant trajectory de velopment.
Chapter 5
is then dedicated to a pr oof of concept for the de veloped unit design impro vements
and operation strategi es. Hence, a series of mini-plant operations is presented with results on
the crucial performance criteria: reaction performance, phase separation ef ficiency and control-
lability . Hereof, model validity , soft-sensing, as well as state-estimation and dev eloped optimal
mini-plant trajectories are tested. This also focuses on the feasibility of controlling the mini-
plant system throughout dif ferent operation modes. Concluding, a successful realization of a
long-term continuous operation of the mini-plant is presented.
Finally ,
Chapter 6
concludes with a summary of the rele vant aspects and results of this thesis
before gi ving an outlook on future de velopments and research directions.
6

2 Theo retical F undamentals and
Background Info rmation
T o further organize this thesis in accordance with the stated objecti ves, Fig. 2.1 provides the
structure for a systematic procedure to realize nov el and se verely challenging process concepts,
which is subsequently applied on the system at hand. Based on that, this chapter initially
introduces the rele v ant theoretical background for carrying out the crucial steps therein.
Entering this workflo w , a problem formulation and specification of goals is giv en – the hydro-
formylation of 1-dodecene in microemulsions applied on a continuous mini-plant system. Thus,
a profound understanding of the reaction mechanism and the solv ent system is essential. Hence,
the physical and thermodynamic properties of
M
icro
e
mulsions (
ME
s) are presented in Sec. 2.1.
In the follo wing, details on the reaction mechanism and kinetics of the rhodium catalyzed hy-
droformylation are gi ven in Sec. 2.2. These theoretical fundamentals are ke y to a subsequent
systematic analysis of the system outlined in Sec. 2.3. This analysis should explicitly contain
the combination of crucial reaction and separation steps to re veal rele v ant interactions. More-
ov er , testing in already lar ger-scale systems, such as a mini-plant is vital to identify operational
challenges arising from una voidable disturbances or impurities.
This way , ke y operational challenges are systematically identified and subsequently resolved by
the de velopment of tailored process design upgrades or adv ances strategies for process operation.
Hence, this chapter pro vides further theoretical background, starting with a short excerpt on
separation unit designs in Sec. 2.3.3. Section 2.4 then addresses strategies for a systematic
process model de velopment, model reformulation strategies, and parameter estimation. Con-
cluding, deliberations on the implementation of dynamic real-time optimization, in volving state
estimation and dynamic optimization are gi ven in Sec. 2.5.
7

2 Theoretical Fundamentals and Background Information
Theoretical Background
Identification of Operational Challenges – Systematic System Analysis
Reaction
Performance
Separation
Behavior
Analysis
Lab
Scale
Mini-
plant
Challenge 1
Challenge 2
Challenge n
Process Concept
Solution Strategies: Derivation and Implem entation
Process Operation
Full System Modeling
Dynamic
Optimization
State
Estimation
Soft-Sensor
Process Design
Reactor
Settler
Proof of
Concept
…

Fig. 2.1:
W orkflo w for the rigorous analysis of surfactant-based multiphase systems and deri vation of
process design and operation strategies.
2.1 Thermo dynamics & Cha racteristics of Micro emulsions
Microemulsion systems form the cornerstone of the process concept re garded in this thesis.
Hence, this section provides a detailed description of these system. Of special interest are the
ke y features of providing intensified mixing of oil and w ater for reactiv e systems, the specific
phase separation beha vior as well as relev ant influences and control parameters.
8

2.1 Thermodynamics & Characteristics of Microemulsions
In general emulsions can be considered as disperse systems of two immiscible liquids, such as
water and nonpolar oily compounds. Formed water in oil (w/o) or oil in w ater (o/w) emulsions
are thermodynamically instable and tend to separate quickly . Howe v er , the stability of these
emulsions can be increased by applying an amphiphilic compound, which is soluble in oil and
water . Gi ven a suf ficient amount of amphiphile, its molecules aggregate at the w ater/oil interface
and enable a successi ve solubilization. Thus, an optically homogeneous and stable emulsion is
obtained. First descriptions of such a system were provided by J. H. Schulman et al. (1943) and
W insor (1956), follo wed by e xtensi ve studies in the follo wing years (J. H. Schulman et al., 1946;
J. Schulman et al., 1948; J. Schulman et al., 1949; Bowcott et al., 1955). In 1959 J. H. Schulman
et al. (1959, p. 1677) characterized these systems as “optically isotropic transparent oil and
water dispersions” and deri v ed the name ‘micro-emulsion’ based on estimated emulsion droplet
diameters around 100 to 500 Å. Indeed, microemulsions were later found to be nano-structured
with a verage droplet diameters around 10 to 200 nm (T adros, 2013).
Follo wing the question of Kahlweit (1988, p. 617) “what are microemulsions good for?”, a v ari-
ety of applications can be found. These range from enhanced oil recov ery (see re vie ws by Abe
(1996) and Bera et al. (2014)) o ver cosmetics and pharmaceutics (Boonme, 2007; Hong et al.,
2001; Hloucha et al., 2014) to chemical reaction media (Schwuger et al., 1995; Klier et al., 2000;
Schomäcker et al., 2009). All of these applications trace back to the special characteristics of mi-
croemulsions to ef ficiently reduce the interfacial tension between hydrophobic and hydrophilic
phases. Thus,
ME
s allo w for efficient emulsification, as well as a specific phase separation
beha vior . These features are also exploited for the process concept at hand (Fig. 1.2).
Rema rk:
For this thesis, only nonionic surfactants are considered as emulsifiers and the fol-
lo wing discussion is confined accordingly .
2.1.1 Surfactant A dso rption on Surfaces and Micelle F o rmation
In general, surfactants are components, which are acti ve at the interface between e.g. hydrophilic
and hydrophobic substances. Se v eral system features, such as interfacial tension, conducti vity ,
and turbidity are thus altered (Myers, 2005a). If for two contacting phases
I
and
I I
all compo-
nents in each phase would be uniformly distrib uted, the total free enthalpy
G
would be the sum
of the free enthalpy of the phases. W ith surfactant molecules accumulating at the interface this
assumption does not hold. This deviation is e xpressed as Gibbs surface ener gy
G ( σ )
(Eq. (2.1a)),
from which the surface e xcess concentration
Γ
, denoting the adsorption or desorption of a com-
ponent i at interface A , can be deri v ed (Atkins et al., 2010):
9

2 Theoretical Fundamentals and Background Information
G ( σ ) = G − ( G ( I ) + G ( I I ))
Γ i = − 1
RT · d σ
d ln c i
(2.1a)
(2.1b)
If no w
i
adsorbs at the interface,
Γ
is to be positi ve and thus the change in interf acial tension
will be neg ativ e. This can also be seen in Fig. 2.2. Note, that in this case
Γ
is expressed as the
ratio of
Γ ( c i )
and the saturation concentration at the interface. Starting at
c 0 = 0 mol L − 1
, the
0
20
40
60
80
100
0
0,5
1
1,5
2
2,5
3
3,5
1 2,7 7,29 19,683 53,1441 143,48907
Interfacial tension σ
Logarithmic surfactant concentration ln c
Surface coverage in %
Hydrophobic
phase
Hydrophilic
phase
c 0
cmc
c i <<c cmc c i <c cmc c i >c cm c

Fig. 2.2:
Qualitati ve representation of the interfacial tension and the surf ace co verage depending on the
logarithmic surfactant concentration. Abov e the diagram, the coordination of the surfactant
molecules at the interface of a tw o-phase system is schematically sho wn.
interfacial tension of the tw o-phase system applies. W ith increasing surfactant concentration the
surface co verage rises accordingly , till at roughly 60 % cov erage a significant decrease of the
interfacial tension occurs (Menger et al., 2011). Surfactant molecules increasingly aggreg ate at
the interface and start to align their head with the h ydrophilic phase. Thus, a new interf ace is
formed, for which the interfacial tension is reduced, due to the higher interactions between e.g.
water and the hydrophilic head of surf actant molecules. Once the interface is fully co vered with
surfactant molecules, the interf acial tension remains constant. The corresponding concentration
is introduced as the
c
ritical
m
icelle
c
oncentration (
cmc
). This phenomenon can also be exploited
to easily access the
cmc
of surfactants in liquid solv ents by interfacial tension measurements
(Oetter et al., 1988). First predicti ve thermodynamic models for the
cmc
based on
UNI
versal
F
unctional Group
A
cti vity
C
oef ficients (
UNIF A C
) ha ve already been sho wn by Flores et al.
10

2.1 Thermodynamics & Characteristics of Microemulsions
(2001) and Cheng et al. (2002). Ho wev er , especially for aliphatic ethoxylate style surfactants
predictions are rather poor and the introduction of ne w group contributions and subsequent e x-
perimental quantification is still necessary . For w ater -oil systems the
cmc
is of great importance,
as it marks the beginning phase transfer acti vity of the surfactant. Abov e the
cmc
, surfactant
molecules form ener getically fa v orable spherical aggre gates in the b ulk phase, which are able to
e.g. trap oil in the water (see Fig. 2.2). The structure of these so called micelles and aggre gate
numbers are dependent on v arious influences (Myers, 2005b; Lindman et al., 2018).
2.1.2 Prop erties and Phase Behavio r of Micro emulsion Systems
In vestigating microemulsions, “the primary aim (...) is to find the conditions under which the
surfactant solubilises the maximum amounts of w ater and oil” (Sottmann et al., 2009, p. 2). This
is closely related to the phase separation beha vior and finding relev ant adv antageous characteris-
tics for e.g. mixing and separation unit operations.
Considering a ternary system of water (
A
), oil (
B
), and an amphiphile (
C
), the composition
v ariables are typically chosen as the oil to water ratio α and the amphiphile mass fraction γ :
α = m B
m A + m B
(2.2)
γ = m C
m A + m B + m C
(2.3)
W ith the influence of temperature
T
being dominant and weak influence of pressure
p
on the
phase equilibria, the phase beha vior of ternary systems can be represented by the Gibb’ s phase
prism in Fig. 2.5 with components A , B , C as base and T as ordinate (Kahlweit et al., 1990).
Since the phase beha vior of the ternary system is a direct consequence of the features of the
related binary systems, these are firstly analyzed using the unfolded phase prism in Fig. 2.3. It
is e vident, that for the
A
-
C
system a mutual solubility of water and the nonionic surf actant
is gi ven at lo wer temperatures. At ele v ated temperatures, an upper miscibility gap with lo wer
c
ritical
s
olution
t
emperature (
cst
) is formed (
c p β
). The in verted case is present for the
B
-
C
system. Here, a miscibility gap with upper
cst
is present at lo w temperatures but the nonionic
surfactant gets increasingly soluble in oil with increasing temperature. Additionally , an extended
miscibility gap e xists for the system water and oil with an upper critical solution temperature
well abov e to boiling point of the mixture. It is obvious, that the position of the critical points
in the systems
A
-
C
and
B
-
C
depend on the choice of the surfactant. Discussing the phase
beha vior of the ternary mixture, it can be anticipated, that it mainly results from the interplay
of the binary miscibility gaps and respecti ve critical points
c p al pha
and
c p bet a
. Thus, Fig. 2.4
outlines Gibbs diagrams at increasing temperatures for the ternary system:
11

2 Theoretical Fundamentals and Background Information
T
T
T
α
γ
H 2 O (A) (B) Oil
critical
point cp 
Nonionic
surfactant
(C)
critical
point
cp 
p=const.

Fig. 2.3:
Schematic illustration of unfolded phase prism sho wing binary phase diagrams for systems of
water (A), oil (B), and a nonionic surf actant (C). Figure adapted from (Kahlweit et al., 1985).
■
At lo w temperatures (
T 1
) the nonionic surfactant
C
is well soluble in water
A
, whereas the
miscibility gap between oil and water is still dominant. Thus, a lar ge miscibility gap is
formed. Here, “the negati ve slope of the tie lines indicates that a nonionic surfactant-rich
water phase (
A em
) coexists with an oil-e xcess phase (
B ex
)” (Sottmann et al., 2009, p. 4).
This separation state is denoted as the two lo wer re gion
2
follo wing Knickerbocker et al.
(1979) or W insor I system (W insor, 1956).
■
W ith increasing temperature the plait point
c p β
of system
A
-
C
is approached and the
binary miscibility gap is influencing the central miscibility gap. Thus, a second plait point
appears on the water rich side, forming another two-phase re gion at
T 2
. For thermody-
namic reasons, the water -rich phase (
A em
) separates into a surfactant rich phase (
M E
, the
actual microemulsion) and an aqueous e xcess phase (
A ex
). Therefore, a central three-phase
region ( 3 or W insor III ) is formed, surrounded by three adjacent two-phase re gions.
■
Further temperature increase raises the mutual solubility of oil and surfactant. Accordingly ,
phase
M E
mov es along the binodal of the central miscibility gap, closing the three-phase
body and finally mer ges with the oil rich phase (
B ex
). Thus, another extended miscibility
gap e xists. The positiv e slope of the tie lines indicates the formation of a surfactant rich oil
phase (
B em
) and an aqueous e xcess phase (
A ex
). This situation is denoted as
2
or W insor
II system.
■
A homogeneous and stable emulsion phase (
1
or W insor
IV
) is formed, if the surfactant
concentrations exceeding the plait point (see Fig. 2.5).
12

2.1 Thermodynamics & Characteristics of Microemulsions
■
At high surfactant concentrations in the W insor
IV
system, lyotropic mesophases or liquid
crystal phases can occur . These are partly of high viscosity , hindering e.g. pumpability of
liquids. For additional information see (Lange vin, 1986; Kahlweit et al., 1985).
II I I I I IV
2 3 2
_ _
_ 1
Nonionic
surfactant
(C)
B ex
A ex
ME
B em
A em
A ex
B ex
ME
T 1 < T 2
Nonionic
surfactant
(C)
Nonionic
surfactant
(C)
T 2 < T 3
H 2 O (A) (B) Oil H 2 O (A) (B) Oil H 2 O (A) (B) Oil

Fig. 2.4:
Schematic isothermal Gibbs triangles for mixtures of oil (
B
), water(
A
), and a nonionic sur -
factant (
C
) at rising temperatures. Phase equilibria are either denoted with
2 , 3 , 2 , 1
or
I - IV
according to W insor (1956). The de veloped phases are labeled according to the continuous
liquid:
B
(oil),
A
(water), and
ME
(bi-continuous microemulsion phase); with superscripts
ex
(excess phase) and em (emulsion phase). The figure is adapted from (Sottmann et al., 2009).
The kno wledge of the phase behavior , component distribution, and v olume fractions of existing
phases is of high rele vance for potential industrial applications. Here, the so called “Kahlweit’ s
Fish Diagram” on the left in Fig. 2.5 pro vides a qualitati ve representation. It represents a slice
through the Gibbs phase prism at a constant oil to water ratio of
α = 50 , w t . − %
and allo ws for
the follo wing statements:
■
A minimum amount of nonionic surfactant
γ 0
is needed to allo w for the formation of
emulsion phases. This is closely related to the
cmc
described in Sec. 2.1.1. Belo w the
cmc, the phase beha vior of the water -oil-system is dominant.
■
At lo w temperatures and abov e the
cmc
the
2
region is established with an oily e xcess
phase and an surfactant-rich aqueous phase in which micelle structures are formed.
■
At high temperatures and abov e the
cmc
the
2
region is established with an aqueous e xcess
phase and an surfactant-rich oil phase in which in verse micelle structures are formed.
■
W ithin a defined temperature window
[ T l , T u ]
the three-phase body is established, in which
a surfactant-rich bi-continuous emulsion phase is formed, holding also water and oil.
Additionally , oily and aqueous excess phases are formed.
13

2 Theoretical Fundamentals and Background Information
■
The ideal state of the
ME
is located at
X
˜
. “It defines both the minimum mass fraction
γ
˜
of
surfactant needed to solubilise w ater and oil (...), as well as the corresponding temperature
T
˜ , which is a measure of the phase in version temperature” (Sottmann et al., 2009).
K eeping this in mind,
M
icro
e
mulsion
S
ystems (
MES
s) can basically be tailored to wards a poten-
tial application: either a high solubilization is stri ved for , or the immobilization of components
in either hydrophobic or hydrophilic phases is desired. For the process concept at hand, this will
be discussed in Chap. 3.
Nonionic

surfactant (C)
Oil (B)
T
X
~
~

0 ~

T
T l
T u
T
surfactant-rich
oil phase
oil excess
phase
surfactant-rich

emulsion phase
wate r excess
phase
surfactant-rich
wate r phase

H

2 O (A)
T
T
T l
T u
T
~
cep
cp
cep
cp
3 1
X
~
2
_
2

Fig. 2.5:
Left: Schematic phase prism of the ternary system water , oil, and nonionic surfactant sho wing
the general temperature dependent phase beha vior . Right: Phase beha vior and qualitati ve
e volution of phase v olume fractions depicted in a T ,
γ
-diagram at constant oil to water ratio
α = 50 w t . − %. The diagrams are based on figures sho wn in (Sottmann et al., 2009).
2.1.3 F eatures and Description of the Three-Phase Bo dy
It has been outlined by Illner et al. (2016c) and Illner et al. (2018a) that component distrib utions
and separation dynamics of the three-phase system are adv antageous for the process concept
at hand. Thus special attention is gi ven to its characteristic features and description. T o guide
the discussion Fig. 2.6 depicts an isothermal Gibbs triangle and possible separation states. If
a three-phase separation is established starting from a prepared mixture at setpoint
SP 1
, a
central microemulsion phase is formed with adjacent excess phases of oil and w ater . From a
thermodynamic point of vie w , the follo wing features of such a system can be stated:
14

2.1 Thermodynamics & Characteristics of Microemulsions
■
The v olume fraction
Φ
of each phase depends on
SP 1
and the corresponding corners of
the three-phase body at the current temperature (le vers).
■
The component concentrations in these indi vidual phases ho we ver are the same for an y
SP 1 in the three-phase body , as long as T and other e xternal influences remain constant.
■
As phase
M E
mov es clockwise to wards the B-side of the triangle with rising temperature,
one will encounter shifting v olume fractions. The aqueous excess phase will gro w with
temperature, while the oil phase will shrink (see right illustration in Fig. 2.6.)
■
The surfactant concentrations of the e xcess phases water
A ex
and oil
B ex
are approximately
at the le vel of the
cmc
for corresponding binary systems. For temperatures v arying from
0-40 ◦ C, cmcs of 10 − 5 -10 − 4 mol L − 1 are found for water ((Rosen et al., 1982)).
■
The composition of the excess phases
A ex
and
B ex
is then mainly determined by the two-
phasic oil-water miscibility gap belo w the three-phase body .
■
The
cmc
of a nonionic surfactant in the aqueous and oily e xcess phase is temperature
dependent. W ith increasing temperature,
cmc ( A )
will decrease, while
cmc ( B )
will increase
(Kahlweit et al., 1990).
2
2
1
2
_
H 2 O (A)
Nonionic
surfactant
(C)
T=T ,
p=const.
Phase state
transition
area
(B) Oil
3
A ex B ex
ME
cmc(B)
cmc(A)
SP1
Φ B
ex
Φ ME
Φ A
ex
Φ B
ex
Φ ME
Φ A
ex
Φ B
ex
Φ ME
Φ A
ex
T l < T < ෩
T ෩
T ෩
T < T < Tu
~
a) b)

Fig. 2.6:
Left: Schematic isothermal triangle of the ternary system at
T
˜
with miscibility gaps. Right:
Schematic e volution of the v olume fractions of the dev eloped phases ov er temperature within
the three-phase body .
Influence of Surface A ctive o r P ola r Substances on the Three-Phase Bo dy
For the application of microemulsions as reaction media, se veral surface acti v e or polar com-
ponents are added to the
MES
, such as catalyst precursors or ligands. This will alter the phase
separation beha vior and especially the expanse and position of the three-phase body .
15

2 Theoretical Fundamentals and Background Information
Since polar compounds, such as salts, are mainly soluble in water also the solubility of the
surfactant in w ater is altered. In general, lyotropic salts, like NaCl, or Na
2
SO
4
reduce surfactant
solubility and thus lead to a widening of the three-phase body , although its position on the
T
ordinate is lo wered (Kahlweit et al., 1988; Ritter et al., 2016). For hydrotropic salts, the opposite
beha vior is found.
The influence of catalyst precursors, preformed catalysts, or ligands on the system can be v arious,
depending on the structure of the added molecules. Besides extended e xperimental in vestig a-
tions, quantum chemistry based methods can be deployed to predict chemical potentials of
components and acquire distrib ution coefficients, as well as phase beha vior characteristics. A
comprehensi ve analysis hereof is pro vided by W ille (2013).
2.1.4 Coalescence Behavio r and Sepa ration Dynamics
A ke y feature for understanding the separation dynamics in a microemulsion system is the
preferential contact between the phases in volv ed. Here, an “important property of the three-
phase body (...) is the minimum of the interfacial tension
σ A ex − B e x
between the aqueous and the
oil-rich phase at
T
˜
” (Kahlweit et al., 1988, p. 507). W ith the starting e v olution of the three-
phase body the surfactant rich w ater phase separates into phase
A ex
and
M E
. The interfacial
tension between these phases will start from zero and increases with temperature. Concurrently ,
the interfacial tension between microemulsion phase and oily e xcess phase will decrease, till
reaching zero at
T u
(see Fig. 2.7 and the discussion in (Kahlweit et al., 1988)). This ef fect is
mainly due to the “mov ement” of the surfactant from the w ater -rich to the oil-rich phase.
T / °C
σ / mNm -1
32
24
26
28
30
18
20
22
16
14
0.001 1 0.1 0.01
H 2 O - n-decane - C 8 E 3
σ B ex – ME
σ A ex – ME + σ A ex – ME
σ A ex – B ex
σ A ex – ME
T u
T l
3
2
2

Fig. 2.7:
T emperature dependent ev olution of the interfacial tension of the coe xisting phases in the
three-phase body . Image based on figure and data shown in (Kahl weit et al., 1988).
16

2.1 Thermodynamics & Characteristics of Microemulsions
It is apparent, that the near -critical phases in the “border” re gion of the three-phase body (e xcess
phases and microemulsion phase) sho w increased mutual wetting. Here, the interfacial tensions
σ A ex − M E
and
σ B ex − M E
drop to zero. Howe ver , within the body of Kahlweit’ s Fish the
σ A ex − M E
and
σ B ex − M E
are well abov e zero. This leads to a specific phase separation behavior: the time
to reach thermodynamic equilibrium for a separating emulsion is significantly larger at the
boundaries of the three-phase body (Kahlweit et al., 1987).
Fig. 2.8 displays the phase separation dynamics for an e x emplary ternary mixture water –n-
tetradecane–C
12
E
4
. A zone of fast separation at the center of the three-phase body is surrounded
by rather extended areas of reduced coalescence and slo w separation. A similar situation is
observed for the kinematic viscosity of the stirred mixture. According to Kahlweit et al. (1987,
p. 441) “the curve sho ws two weak maxima close to
T l
and
T u
, and a distinct minimum close
to the mean temperature of the three-phase interv al. The width of this minimum corresponds
well with that of the region of rapid phase separation (...)”. These findings are major aspects
to be considered for technical applications of microemulsions, especially in separation units.
Even small changes in process v ariables, such as temperature or concentrations are prone to
significantly alter the mixture properties.
35
25
30
20
50
2
5
1
10
20
20 0 40 60 100 80 20 15 25 30 35
T / °C
α / wt.-%
ν / mm 2 s -1
T / °C
fast
2
2

slow
slow
2 2

3
T u T l
T u
T l

Fig. 2.8:
Left: Dynamics of the phase separation in a ternary system of H
2
O–n-tetradecane–C
12
E
4
with
γ = 2
wt.-%. fast indicates a separation within less than 30
min
. Right: T emperature dependent
viscosity of the system. Image based on figure and data sho wn in (Kahlweit et al., 1987).
Coalescence Behavio r
Furthermore, the understanding of the coalescence beha vior of microemulsions is ine vitable for
process applications. The coalescence of droplets or droplets on interfaces has been in the focus
of research for many years with a major application field in liquid-liquid e xtraction (A. R. Smith
et al., 1963; Bohnet, 1976; Deibele et al., 2000). Up to date, numerous modeling approaches for
two-phase systems ha ve been de v eloped, subsequently increasing the le v el of detail (see re vie ws
17

2 Theoretical Fundamentals and Background Information
by V ijayan et al. (1975), Liao et al. (2010)). Ho wev er , droplet coalescence underlies se veral
influences, which also include surface acti v e substances. Thus, a profound analysis of the influ-
ence of surfactant and other substances on the coalescence of a multiphase mixture is ine vitable.
Exemplarily , it is referred to the work of Danov et al. (1999) and Ale xandrov a et al. (2018), who
in vestigated the ef fect of soluble and insoluble surfactants on film drainage and coalescence.
Due to the afore outlined comple xity of
MES
s, a rigorous description of their multiphase (and
dynamic) separation is highly challenging and yet demands a deep understanding of the system
and extended e xperimental in v estigations. Hence, the coalescence behavior of
MES
s will be
discussed from a more general perspecti ve on the basis of Fig. 2.9. The aim is to set up heuristics
to aid equipment design and process operation. Further information on theoretical background
and experimental methods is pro vided by Kahlweit et al. (1988).
■
W ith the beginning three-phase body (
2 / 3
transition) droplets of oily excess phase (oil)
and surfactant rich phase (ME) e xist as singlets in a continuous (surfactant rich) water
phase (aq). The interfacial tension between w ater and emulsion phase is almost zero and
the separation is slo w (see Fig. 2.7). Hence, one observ es rising oil droplets dragging
surfactant, while the ev olution of water and microemulsion phase is slo w . Moreov er
surfactant may accumulate at the interf ace of oil and microemulsion phase.
■
Increasing the temperature leads to an accelerated separation within the three-phase body .
W ith the changing interf acial tensions the formation of dif ferent emulsion states is en-
forced. These may hav e the character of a droplet in droplet emulsion, as has been sho wn
by Hohl et al. (2016). These dual droplets again will rise up in an aqueous phase and
disrupt at the aq-ME interface. W ith sufficiently lar ge interfacial tensions, the ME droplet
quickly coalesces with the ME phase. The oil droplet continues to rise due to density
dif ferences and finally merges with the oil phase.
■
The in v erted case is present, if the top of the three-phase body is approached (
T > ˜
T
).
Here dual droplets of water and microemulsion phase are found in an oily continuous
phase. If such a system is stirred and then set to separate, one observes descending dual-
droplets. Again, these break at contact with the ME phase. As the ME droplet merges, the
aq droplet descends to wards the water phase.
■
In the transition zone to wards the
2
-phase region, the interfacial tension between mi-
croemulsion phase and oil drops to zero. Again, only single droplets are present as
disperse phase in an oily continuous phase. On separation, falling water droplets dragging
surfactant are observ ed. The separation of oil and emulsions phase is v ery slow and ag ain
surfactant accumulation can occur at the interf ace water-ME .
■
At
T
˜
also bi-continuous microemulsion phases can be present, which separate rather fast,
as the disperse oil and water droplets mo ve to wards their corresponding phases.
18

2.2 Kinetics of Rhodium-Catalyzed Hydroformylation Reactions
_
oil
ME
aq
aq
ME
oil
oil
ME
aq
aq
ME
oil
aq
ME
oil
aq
ME
oil
aq
oil
ME
Separating
System
aq
ME
oil
Stirr ed
System
2/3 – System
T ransition
T
3/2 – System
T ransition
Ending
3 – System
Beginning
3 – System

Fig. 2.9:
Schematic illustration of the coalescence beha vior along the three-phase body . The upper
section sho ws separating systems with droplet configurations and coalescence. The lo wer part
sketches the situation of continuous and disperse phases in the stirred system. oil : oil rich
phase, aq : aqueous phase, ME : microemulsion phase.
2.2 Kinetics of Rho dium-Catalyzed Hydrofo rmylation
Reactions
From a process design and control perspecti ve, the knowledge of reaction kinetics is vital to
optimize reaction yield and selecti vity , which thus will be shortly outlined.
In 1961 Heck et al. (1961) proposed a first mechanism for the cobalt-catalyzed hydroformylation
based on theoretical assumptions, which was later e xpanded and v alidated through e xperiments
and further theoretical in vestigations (Heck et al., 1963). This formed the basis for the for -
mulation of a reaction mechanism of the hydroformylation based on ligand modified rhodium
catalysts (Ev ans et al., 1968; W ilkinson et al., 1968; Bro wn et al., 1970). Despite the W ilkinson
mechanism being well accepted and hydroformylation processes being successfully applied in
19

2 Theoretical Fundamentals and Background Information
chemical industry for more than sixty years, current research still aims for a detailed understand-
ing of reaction mechanisms and formulation of suitable ligand-Rh-catalyst systems (Shylesh
et al., 2013; Jörke et al., 2017; W odrich et al., 2018).
Important adv ances in ligand de velopment were achie ved with the synthesis of bidendate diphos-
phine ligands. This led to significant improv ement in regioselecti vity of the reaction due to
steric ef fects resulting from the ligand’ s “bite-angle” (T olman, 1977). In 1995 Kranenb ur g et al.
(1995) found optimal selecti vities for the Rh-catalyzed hydroformylation of 1-octene, if Xant-
phos (4,5-bis(diphenylphosphino)-9,9-dimeth ylxanthene) was used as a ligand. Later , Goedheijt
et al. (1998) described the synthesis of a sulfonated form of Xantphos – SulfoXantPhos (4,5-
Bis(diphenylphosphino)-9,9-dimeth yl-2,7-disulfoxanthene disodium). This highly hydrophilic
ligand enables the formation of w ater -soluble catalyst complex es besides high re gioselecti vities
around 97 %. W ith water -soluble catalysts being of interest for this thesis, a catalyst complex
can be formed using the catalyst precursor Rh(CO) 2 (acac) and SulfoXantPhos (see Fig. 2.10).
Fig. 2.10:
Structure of the hydrophilic Rh-SulfoXantPhos catalyst comple x. Figure taken from
(Pogrzeba et al., 2017b).
T o understand reaction performance and influences on the reaction in lab and mini-plant, the
mechanism of the Rh-catalyzed hydroformylation, reduced to the formation of linear aldehy-
des, is presented in Fig. 2.11 (Pogrzeba, 2018). Initially , the catalytically activ e species (
1a
) is
formed from Rh(CO)
2
(acac) and SulfoXantPhos in the presence of synthesis gas. This species
is in equilibrium with an inacti ve rhodium dimer (
1c
) and the non-selecti ve unmodified rhodium-
tetracarbonyl species (
1b
), depending on the partial pressures of CO and H
2
(Sandee et al., 1999;
Li et al., 2002; Silva et al., 2003). The catalytic cycle starts with the dissociation of CO from
rhodium species (
1a
) and subsequent coordination of the alkene (
3
). In the next step, the alk ene
is inserted into the catalyst complex (
4
), whereas the orientation of the ligand at the metal center
influences the regioselecti vity of the reaction, as either the terminal or inner carbon atom is
linked to the comple x. Afterwards CO is added (
5
) and inserted into the rhodium-alkyl bond
to form species (
6a
). At this stage high CO concentrations promote the formation of a catalyti-
cally inacti ve ac yl species (
6b
), which is in equilibrium with (
6a
). The cycle is closed with the
addition of H 2 to form species ( 7 ) and the follo wing reducti ve elimination of the aldeh yde.
20

2.3 Systematic In vestigation of Reacti ve Microemulsion Systems

Fig. 2.11:
Mechanism of rhodium-catalyzed hydroformylation of alkenes using a bidentate diphosphine
ligand. Based on the mechanism by W ilkinson et al. (1968) and further dev eloped by Desh-
pande et al. (2011). Figure taken from (Pogrzeba et al., 2019).
2.3 Systematic Investigation of Reactive Micro emulsion
Systems fo r Pro cess Design and Op eration
The realization of nov el process concepts in v olving liquid multiphase systems requires sufficient
understanding of system inherent phenomena and challenges. Hence, knowledge on the desired
reaction with underlying catalytic system, the multiphase reaction media with physical and
thermodynamic features, as well as the interaction of both is to be b uild up. Systematic and
ef ficient methodologies for this are ke y to an enhanced process de velopment, earlier readiness
of nov el process concepts, and increased rele vance for industrial applications. Focusing on
microemulsion systems, this section thus discusses methodologies for in vestig ating such systems
and the aligned deri v ation of strategies to enable application.
21

2 Theoretical Fundamentals and Background Information
2.3.1 Status Quo: Pro cess Design and Op eration fo r Homogeneous
Catalysis in Micro emulsion Systems
Microemulsions as reaction media were already reported more than 50 years ago by Cordes
et al. (1969) or W allace et al. (1973). Recently , further acti vity for specifically con ve ying
or ganic reactions using long-chained substrates is noticeable (Haumann et al., 2002; Dwars et
al., 2005; Hamerla, 2014). Most of these contrib utions stem from academia and comprise batch
experiments e valuating reaction performances using v arying microemulsion formulations. Thus,
at least selection guidelines for the formulation of
MES
can be deri ved. Ho wev er , industrial
applications of de veloped
MES
for or ganic reactions are still v ery limited (Schomäcker et al.,
2009). Moreover , modeling approaches for
MES
are scarce and mainly focus on equilibrium
state calculations with reduced applicability in ternary systems (García-Sánchez et al., 2001) or
are still subject to extended e xperimental in vestig ations (T orrealba et al., 2018). A significant
deficiency of methodologies on ho w to design and operate actual processes in volving such no vel
solvent systems can thus be stated.
T o close this gap, increasing efforts in understanding microemulsion systems ha ve been made
within the Collaborative Resear ch Center TR63 InPR OMPT with the aim to unlock a holistic
process de velopment for such systems b ut yet also deepen the understanding of its inherent
features. Essential deliberations, aiding the analysis of
MES
encompass the in vestigation of mass
transfer phenomena reg arding the application of homogeneously catalyzed reactions (Hamerla
et al., 2013a) and the coalescence beha vior of microemulsions (Hohl et al., 2018). Additionally ,
Pogrzeba et al. (2016a) and Pogrzeba et al. (2017b) provide insights on the influence of surf actant
type and microemulsion system on the reaction performance for the hydroformylation of 1-
dodecene, as well as the catalyst recycling. Moreov er , M. Müller et al. (2013) and D. Müller
et al. (2015) provide heuristics on process design, analysis of
MES
s reg arding separation unit
design, and modeling approaches.
Despite these adv ances, early stage implementations of reactiv e
ME
s in mini-plant systems still
re veal significant challenges re garding stable continuous operations and reaction performance
(Illner et al., 2016a; Illner et al., 2016b). W ithin long-term operations, significant byproduct for -
mation and the frequent loss of feasible product and catalyst separation was observ ed. Moreov er ,
the causality between operation, intervention, or disturbances and the observ ed phenomena were
not fully understood. Regarding industrial-scale applications, this situation is not acceptable
and significant improv ement on av ailable methodologies for the realization of
MES
s as reaction
media is necessary .
22

2.3 Systematic In vestigation of Reacti ve Microemulsion Systems
2.3.2 Systematic Analysis of Micro emulsion Systems fo r Pro cess
Application
As part of the workflo w for a rigorous analysis of surfactant-based multiphase system in Fig. 2.1,
a profound identification of rele v ant operational challenges is crucial. Hence, a systematic work-
flo w for the analysis of such systems for technical applications is de veloped in the follo wing.
Considering the comple x nature of microemulsions and the catalytic system of the hydroformy-
lation reaction se veral general requirements for such an analysis must be met:
■
Reaction performance and phase separation beha vior must be in vestigated considering all
compounds existing in the final application due to significant influences.
■
This also means to consider interactions between the reaction and separation performance.
■
Challenges arising from the dynamic operation in a continuously operated plant need
to be analyzed – operation with internal recycles must be considered re garding possible
disturbances like concentration shifts, mixing beha vior , or accumulations.
■
Special attention should be gi ven to the measurability and controllability . Thus, whether
all system v ariables having significant influence on the reaction performance and phase
beha vior are trackable and controllable with av ailable instruments and actuators.
■
If the latter is not the case, the application of adv anced analytics or deployment of model-
based methods (e.g. state estimation) is to be checked.
T o acquire sufficient and rele v ant data for this, a workflo w for the analysis of surfactant con-
taining multiphase systems for technical applications is introduced. The main idea is to use
preliminary screening experiments, to quickly characterize the (dynamic) separation beha vior
and de velop first equipment designs as well as process models. Starting from deliberations in
our o wn contribution (D. Müller et al., 2015) this w orkflo w is substantially extend to meet the
abov e mentioned requirements. This way , the acquisition of suitable data models of the phase
separation beha vior for process control purposes and vital information on the controllability of
the system, as well as reduction of the experimental eff ort is ensured. The general structure of
this systematic guideline consists of six successi ve steps:
Step 1
defines constraints and requirements for process application. Firstly , desired features of
the multiphase system are defined. For a mix er-settler system this could be suf ficient solubi-
lization of reactants and a fa vorable component distrib ution in the separated system. Secondly ,
already predominant limitations of the technical system are to be collected. These could entail
constraints on applicable temperatures, pressure, or the av ailability of sensors and actuators to
measure and influence state v ariables of the multiphase system. Finally , the component system
is specified. This includes microemulsion formulation, the catalyst system, reaction educts and
23

2 Theoretical Fundamentals and Background Information
products, applied gases, and potential additi ves. These specifications are most relev ant, because
changes in the component system alter phase equilibria and systems dynamics significantly .
Step 1
System &
Goals
Step 2
Influence
identification
Step 3
Prescreening
of the system
Step 4a
Unit design
for plant
operation
Step 5
Full system
mapping
Step 6
Equipment
dimensioning
Definition of system requir ements / sys tem specifications and choice of component system
System r equirements (Goals)
▪ solubilization
▪ component distributi on
▪ desired throughput,
▪ max. residence time,
▪ separation quality
System limitations
▪ available equipment
▪ available sensors
▪ limitations on temperature
/ pressure
Component System
▪ systematic choice of
surfactants
▪ possible reaction
byproducts
Step 4b
Controllabi-
lity Analysis

Fig. 2.12:
Systematic analysis of surfactant containing multiphase systems, Step 1: system specification
and goals for process operation. Figure adapted and e xtend from (D. Müller et al., 2015).
Step 2
then handles the identification of rele vant influencing f actors on the behavior of mul-
tiphase systems and possible ranges of interest. This prescreening is performed theoretically ,
including a thorough literature surve y . For multiphase systems holding nonionic surf actants,
deliberations of Kahlweit et al. (1985), Kahl weit et al. (1987), and Sottmann et al. (2009) were
essential to set up the guiding scheme in Fig. 2.13.
Step 2: Th eoretical prescr eening & investigation range
Emulsification and
Phase Separation
Influence parameters
Properties
Surface tension
Density
V iscosity
Polarity
Specific heat capac ity
Molecular translation
Brownian motion
Molecular orientation
Phase equilibrium
Reaction equilibr ium
T emperature T
Pressure P
Concentrations c i
V elocity field v
Electrical field E
Magnetic field H
Geometry
Non-eq. behavior
Coalescence
Droplet breakage
Foaming
Diffusion
Dispersion
Heat conductivity
Heat convection
Pressure drop
Separation kinetics
Reaction kinetics
Important questions
By which means can
we manipulate the
system to best fulfil
the set requirements?
In which ranges
should further
examinations be
performed?
Which are relevant
influenceable factors
for the system in
hand?
Step 1
System &
Goals
Step 2
Influence
identification
Step 3
Prescreening
of the system
Step 4a
Unit design
for plant
operation
Step 5
Full system
mapping
Step 6
Equipment
dimensioning
Step 4b
Controllabi-
lity Analysis
T race components

Fig. 2.13:
Systematic analysis of surfactant containing multiphase systems, Step 2: influence identifica-
tion. Figure adapted and e xtend from (D. Müller et al., 2015).
24

2.3 Systematic In vestigation of Reacti ve Microemulsion Systems
Note, that limitations defined in
Step 1
are to be considered. Hence, if de vices providing electric
or magnetic fields (application on
MES
sho wn in (T ekle et al., 1989; Palyska et al., 1993))
cannot be used in the final process application, the corresponding influence is omitted.
Step 3
aims for an e valuation of defined influences. Therefore, simplified prescreening experi-
ments ( Shake & W ait ) are suggested. Multiple test tubes with varying compositions are prepared
and heated in thermostatic baths. Once the desired temperature is reached, the test tubes are
remov ed from the bath, vigorously shaken, and returned into the bath. Phase separation for all
compositions is then observed for a fixed time (In vestig ation Stage 1). Thus, the general sepa-
ration kinetics and forming phase equilibria are observed (Observ ation Stage 1). A feasibility
analysis is connected to this step. Here, the system observations are to be scanned for suitable
separation dynamics and phase equilibrium states in accordance with defined goals in
Step 1
.
Step 3: Prescr eening of the system
Preliminary region of intere st e.g. T min,max , c i,min,max , τ min , τ = f(T , c i ),
Relevance ran king of influence parameter
Flow and geometrical restrictions
Additional applied means (fields, substances, etc.)
Shake & W ait experiments
Investigation
Stage 1
Observation
Stage
Investigation
Stage 2
Prescr eening
conclusions
Step 1
System &
Goals
Step 2
Influence
identification
Step 3
Prescreening
of the system
Step 4a
Unit design
for plant
operation
Step 5
Full system
mapping
Step 6
Equipment
dimensioning
Step 4b
Controllabi-
lity Analysis
Feasibility Analysis: Phase equilibria, dynamics, and component distribution
Separation kinetics & Phase equilibrium  f( T ,c i , …)
Infeasible Feasible
Sensitivity and
Operation
Region
Phase separation
impediment due
to foaming
General phase separation
impediment
Degassing
investigations
Apply electic,
magnetic, or
gravitational
force field
Add
surface active
substance

Fig. 2.14:
Systematic analysis of surfactant containing multiphase systems, Step 3: Preliminary screen-
ing and sensiti vity analysis. Figure adapted and extend from (D. Müller et al., 2015).
25

2 Theoretical Fundamentals and Background Information
Se veral reasons for impeded applicability of the current system can e xist such as foaming or
changing of interfacial tensions due to ongoing chemical reactions. The application of counter
measures is then tested on In v estigation Stage 2. If the feasibility test is successful, an additional
assessment on the sensiti vity of the influence factors on separation quality and dynamics is done.
Mostly insensiti ve influence f actors are to be discarded for further in vestigations. From the
feasibility step, also possible operation regions for phase separation, as well as lo wer and upper
bounds on the influence parameters can be estimated. On infeasibility , further in vestigations
(return to In vestigation Stage 1) are considered.
Step 4
is designed twofold. Firstly a preliminary unit design is to be performed based on the
Step 4a: Unit design for plant operation
Step 4b: Implementation of Contr ol Loops / Contr ollability Analysis
Unit type selection
Design Stage
1
Investigation
Measurability
and Sensors
Step 1
System &
Goals
Step 2
Influence
identification
Step 3
Prescreening
of the system
Step 4a
Unit design
for plant
operation
Step 5
Full system
mapping
Step 6
Equipment
dimensioning
Step 4b
Controllabi-
lity Analysis
Additional
equipment
Centrifugal extract or 2 phase gravity settler 3 phase gravity settler
Design Stage
2 External fields Knitted wire meshs Multipe heating zones
Feasibility Analysis / Fluid Dynamics
Key Influence
Parameters
from Step 3
Stable Operation / Separation Efficiency
T emperature Concentration A Concentration B Residence T ime
Objective
Control Element
▪ Separation dynamics significant ly changed?
▪ Phase composition altered?
▪ Accumulation phenomena?
Pt100
fast ! not available MFC
fast
! not available
Heating
element Feed pump B Recycle Feed pump A
Soft-Sensor
development
Apply state
estimation
Preliminary control scheme – successful if all loops closed
Additional modeling tasks and implementation of APC methods possible
Information on additional necessary measurements to be recorded in Step 5
If not
successful

Fig. 2.15:
Systematic analysis of surfactant containing multiphase systems, Step 4: Unit design and
operability analysis for continuous processes. Figure e xtend from (D. Müller et al., 2015).
26

2.3 Systematic In vestigation of Reacti ve Microemulsion Systems
gained information on the multiphase system. Decisions are to be made on type of unit, applied
force field, and the number of handled separated phases. Additionally , internals can be chosen.
Follo wing the discussion in Sec. 2.3.3, the technical realization of a separation process can alter
the separation dynamics. This has to be checked re garding hampering ef fects. Exemplarily , the
implementation of knitted wire meshs can lead to significant accumulations of certain compo-
nents in a settler unit and inhibit its operation.
The second part of
Step 4
in volv es the operability and controllability of the system. T o this
extend of the guideline, a certain rele v ant set of parameters influencing the phase separation of
a multiphase system has been identified (K ey Influence P arameters). It no w has to be consid-
ered, if these actually can be technically controlled in the real application. For this purpose, the
measurability of these influences and a vailable sensors is check ed. Of interest might also be the
a vailable sampling rate of the corresponding sensor , which should match with the observed sen-
siti vity of the corresponding influence parameter . If no apparent technical measuring method is
a vailable one also could proceed with
A
dv anced
P
rocess
C
ontrol (
APC
) methods. Exemplarily ,
soft-sensor de velopment is named at this point. If measurability is ensured for all rele vant influ-
ence parameters, their controllability is checked. This means the av ailability of corresponding
control elements. If this is not true, the unit concept needs to be changed in an iterati ve process.
The major outcome of
Step 4
is thus a preliminary unit design and most important, information
on additional necessary measurements to quantify certain influence parameters.
Step 5
then continues with the main experimental section of this guideline. Up to no w , a reduced
set of influence parameters and rele v ant operation regions for the separation of the multiphase
system has been identified. A feasible set of measurements has been listed, which ensures a
reliable tracking of ke y influence parameters and states. Hence, an appropriate experimental
testing system can be designed, alongside an ef ficient e xperimental plan. Based on this, mapping
experiments are performed to determine separation dynamics and states.
Step 5: Full mapping of the system
▪ Design of experiments accor ding designated relevant influence parameters and corresponding ranges
▪ Dynamic separation experiments and observation of relevant states
▪ Apply parallelization of experiments and dynamic trajectories (variation of T and c i )
Step 1
System &
Goals
Step 2
Influence
identification
Step 3
Prescreening
of the system
Step 4a
Unit design
for plant
operation
Step 5
Full system
mapping
Step 6
Equipment
dimensioning
Step 4b
Controllabi-
lity Analysis

Fig. 2.16:
Systematic analysis of surfactant containing multiphase systems, Step 5: Full system mapping.
Figure adapted and extend from (D. Müller et al., 2015).
27

2 Theoretical Fundamentals and Background Information
This information is then used to generate empiric input/output models for the phase separation
dynamics and component distrib ution in the system. W ith the guideline at hand, their usage
in real technical applications is ensured, as the consideration of measurability or observ ability
of rele vant states is enforced. Additionally , this information is used to proceed with a detailed
design and construction of the chosen separation unit ( Step 6 ).
2.3.3 Equipment fo r Multiphasic Sepa ration
For the scope of this thesis, the separation of
MES
is to be continuously operated in a mini-plant.
Hence, a short outline on technical realizations of separation units is gi ven.
Depending on the thermodynamic and physical properties of the mixture to be separated, a
v ariety of technical realizations exist. Applying a systematic guideline sho wn by Seader et al.
(2011, p. 309) on the component system discussed (Sec. 3.1), a single stage mixer -settler process
is fa v orable. W ith suf ficiently lar ge density differences of the substances the separation can
be performed in a gra vity settler . The apparatus itself is designed as a horizontal or slightly
inclined separation chamber with an inlet, separation area, possible internals, and phase drains
(see Fig. 2.17). The inlet is positioned at the le vel of the e xpected interface to a void back-mixing
of already separated phases (Padilla et al., 1996).
Heavy phase
Light phase
Buil-up
height
Separation length
Perforated plate Plate packings

Fig. 2.17:
Schematic representation of a gra vity settler with sev eral internals for the separation of a
hea vy dispersed phase. Illustration based on (Schlieper et al., 2004).
The phase e volution o ver the length of the settler first of all depends on the coalescence beha vior
of the mixture. In addition, also the flo w regime is rele v ant for a continuous process. In ves-
tigations by Jef freys et al. (1970) sho wed, that mixing processes at the settler inlet lead to a
stabilization of the emulsion. Moreov er , a radial velocity gradient might occur . Thus circulation
flo ws or reflux are observed, hampering separation ef ficiency (Dro wn et al., 1977).
Apparently , the separation ef ficienc y can be increased by the use of internals, such as plates,
baf fles, perforated plates, or knitted wire meshs. Coalescence enhancement mostly results from
28

2.3 Systematic In vestigation of Reacti ve Microemulsion Systems
the contact of the emulsion with metals, for which the disperse phase shows a high wetability .
The disperse phase forms a liquid film on the surface, from which then lar ger droplets detach
(Ber ger, 1986). Additionally , a simple filtration effect is e xploited for knitted wire meshs. W ith
emulsion droplets lar ger than the mesh free diameter , droplets are impounded and coalescence
is enhanced because of the increased velocity at droplet collisions (Bohnet, 1976).
2.3.4 Systematic W o rkflo w fo r the Influence Identification and Derivation
of A dapted Kinetic Mo dels
The formulation of kinetic models to predict reaction performance is to date challenging. Usu-
ally , expensi ve and time consuming e xperimental studies hav e to be performed if these models
are to be de veloped for process simulations or process optimization (Mitsos et al., 2018). Ac-
cording to Besora et al. (2018, p. 3), especially homogeneously catalyzed reactions include a
multitude of steps, like “of f-cycle formation of the catalyst from a pre-catalyst, formation of
adducts between the reactants, catalyst deacti vation processes, (...) isomerizations”, which hav e
to be initially identified. Spectroscopic methods can be applied to identify acti ve catalytic com-
pounds, states, and detailed reaction routes (Bhaduri et al., 2014; Grabo w et al., 2014). From
that, complete mechanistic or microkinetic models can be formulated, aided by quantum chem-
istry computational tools (Murzin et al., 2016; Besora et al., 2018). Using kinetic e xperiments,
actual reaction rates and kinetic parameters can be estimated. Ho we v er , this is mostly done
for idealized systems, yielding microkinetic descriptions. Often enough, additional influences
on the kinetics are present for practical applications. These are induced by trace components,
interaction with mass transfer or changes of the actual reaction media. Thus, an adaption of the
kinetic model to wards the application is necessary . For this, a systematic workflo w is presented
in our contrib ution (Pogrzeba et al., 2019) and shown in Fig. 2.18.
Step 0
therein is used to collect a vailable data on the re garded component system and reaction.
First of all, the literature is revie wed for a vailable kinetic models (microkinetic, mechanistic,
or e ven empiric)
M od el 0
and kinetic parameters. For a v ariety of reactions, this is successful
and yields in a profound basis for kinetic model formulations. Additionally , necessary physical
properties for the system at hand are collected.
In
Step 1
the initial
M od el 0
is tested for its general suitability for the application case, the system
S real
. Kinetic experiments are conducted under idealized conditions, for which only the inputs
I 0
and controls
u 0
included in
M od el 0
are v aried. W ith the experimental results, a parameter
estimation is performed on the kinetic parameters of
M od el 0
and it is checked, whether
M od el 0
suf ficiently describes the kinetics in S real for v ariations of I 0 and controls u 0 .
29

2 Theoretical Fundamentals and Background Information
Step 0: Collect available inform ation
𝑴𝒐𝒅𝒆𝒍 𝟎 describes
kinetics in 𝑺 𝒓𝒆𝒂𝒍
regarding 𝑰 𝟎 , 𝒖 𝟎 ?
Kinetic model information for system 𝑺 𝒕𝒉𝒆𝒐 :
Mechanistic model, microkinetic
𝑴𝒐𝒅𝒆𝒍 𝟎 = 𝒇(𝑰𝒏𝒑𝒖𝒕𝒔 𝑰 𝟎 , 𝑪𝒐𝒏𝒕 𝒓𝒐𝒍𝒔 𝒖 𝟎 , 𝑷𝒂𝒓𝒂𝒎𝒆𝒕𝒆𝒓𝒔 𝑷 𝟎 )
Yes
No
Phys. properties for
system of interest 𝑺 𝒓𝒆𝒂𝒍
Available parameter
sets for 𝑴𝒐𝒅𝒆𝒍 𝟎
Step 1: Kinetic information and parameter
estimation for 𝑴𝒐𝒅𝒆𝒍 𝟎
Perform exp eriments for system 𝑺 𝒓𝒆𝒂𝒍
Variation of input set 𝑰 𝟎
of 𝑴𝒐𝒅𝒆𝒍 𝟎
Variation of control set
𝒖 𝟎 of 𝑴𝒐𝒅𝒆𝒍 𝟎
Perform pa rameter estimation and get 𝑷 𝟎
𝒐𝒑𝒕
Other models
available?
Yes No
Other modeling
approach
Step 2: Influence identification
Screen system 𝑺 𝒓𝒆𝒂𝒍 for additional
influences on reaction kinetic
Catalytic system
▪ Ligand type
▪ Ligand to metal ratio
▪ Catalyst
preequilibria
Mass transfer
▪ Stirrer speed
▪ Emulsifier
▪ Composition of
system
Updated input/control sets 𝑰 𝒓𝒆𝒂𝒍 , 𝒖 𝒓𝒆𝒂𝒍
Experimental quantification of relevant new
influences
Step 3: Model update and parameter
estimation for 𝑴𝒐𝒅𝒆𝒍 𝒓𝒆𝒂𝒍
Adapt 𝑴𝒐𝒅𝒆𝒍 𝟎 to account for additio nal influences
𝑴𝒐𝒅𝒆𝒍 𝒓𝒆𝒂𝒍 = 𝒇(𝑰 𝒓𝒆𝒂𝒍 , 𝒖 𝒓𝒆𝒂𝒍 , 𝑷 𝒓𝒆𝒂𝒍 )
Surrogate math
expression according to
kinetic data
Integrate updated
sets 𝑰 𝒓𝒆𝒂𝒍 , 𝒖 𝒓𝒆𝒂𝒍
Perform pa rameter estimation and get 𝑷 𝒓𝒆𝒂𝒍
𝒐𝒑𝒕
𝑴𝒐𝒅𝒆𝒍 𝒓𝒆𝒂𝒍
describes kinetics
in 𝑺 𝒓𝒆𝒂𝒍 regarding
𝑰 𝒓𝒆𝒂𝒍 , 𝒖 𝒓𝒆𝒂𝒍 ?
Finished
No
Yes

Fig. 2.18:
W orkflo w for the adaption of microkinetic or mechanistic models to describe the reaction
performance for systems with inherent additional influences. Figure adapted from (Pogrzeba
et al., 2019).
30

2.4 Modeling for Dynamic Processes
If this is true,
Step 2
then deals with the identification of rele vant influences of
S real
on the
reaction performance. This includes systematic screening experiments on additionally applied
components (co-solvents, emulsifiers), the catalytic system (applied ligand), and the technical
realization (stirrer speed, pressure drop, recycling of the reaction mixture). Influences, which
sho w a significant sensiti vity on the reaction performance, are included in updated sets of inputs
I real
and controls
u real
. Additional kinetic e xperiments are then used to quantify these influences.
Step 3
then handles the model update. The additional rele v ant influences of
S real
are imple-
mented into the kinetic model by augmentation of the rate equations to obtain the adapted kinetic
model
M od el real
. W ith the collected experimental data sets for
I real
and
u real
, a parameter esti-
mation is performed. Finally it is checked, whether
M od el real
suf ficiently describes the kinetics
in
S real
for v ariations of
I real
and controls
u real
. If this is not the case, possible hidden effects
are present and the influences identification in Step 2 is repeated. Otherwise, an adapted kinetic
model is obtained, describing the reaction performance for the desired application case.
2.4 Mo deling fo r Dynamic Pro cess Op erations and Control
The deri v ation of a dynamic mini-plant model including rele v ant phenomena related to the hy-
droformylation reaction and the microemulsions system is a ke y element for the subsequent
de velopment of desired optimal process operation strate gies. Ho wev er , finding a suitable model
formulation for this is challenging, since contradictory objectiv es apply: suf ficient accurac y
reg arding system-lev el phenomena and lo w computational ef fort for optimization calculations.
Connected to this, se veral re vie ws display recent advances in model deri vation and today’ s pos-
sibilities in conceptual process design (Mitsos et al., 2019) and nonlinear process optimization
(L. T . Biegler, 2018). Consequently , an ov erview on methods for the deri v ation of models for
process optimization, model simplification, and improv ement of con ver gence beha vior rele v ant
for this work is gi v en in the follo wing.
2.4.1 Mo deling Strategies and Implementation fo r Optimization Purp ose
According to Hangos et al. (2001, p. 14) “Process modelling is one of the ke y acti vities in process
systems engineering (...) dri v en by such application areas as process optimization, design and
control”. Thus, also a multitude of approaches is av ailable for the deri vation of models, which
can be categorized from so called ”white box“ models based on ph ysical or chemical laws to
data-dri ven ”black box“ models (Edgar et al., 2001, p. 41) or any combination of both, called
”grey box“ modeling.
31

2 Theoretical Fundamentals and Background Information
Mechanistic o r First Principles Mo dels
Reg arding the ev aluation and optimization of process operations, rigorous models based on
physical or chemical fundamentals are important, since the y inherently offer high accurac y and
superior inter - and extrapolation features. Moreov er , these models are “conceptually attracti ve
because a general model for any system size can be de v eloped ev en before the system is con-
structed” (Edgar et al., 2001, p. 41).
Follo wing (W ozny et al., 2006, p. 95ff.), the systematic model deri v ation starts with the sub-
di vision of a considered process into sub spaces according to the desired modeling depth. For
each sub space a complete set of linearly independent balance equations is formulated adhering
to the MESH-I-systematics (
M
aterial balances,
E
quilibrium relations,
S
ummation equations,
and
H
eat balances, and
I
: momentum balances). These could be further expanded considering
non-equilibrium modeling and rate equations for heat and mass transfer . Additionally , mecha-
nistic kinetic models are included to describe reaction rates. Regarding process optimization,
this set of equations is then expanded by an objecti ve function (e.g. economic criteria) and
inequality constrains (physical limitations or quality goals). Ho wev er , obtained equations can
hold any de gree of complexity and thus f ast con ver gence is not ensured. Subsequent handling
of equation system scaling, reformulations, or model simplifications are mandatory to enable
model application for optimization purpose.
Surrogate Mo dels
One approach to tackle the complexity of first principles models is to replace comple x elements
therein by surrogate models. Thereby , the input-output relation of the original model is well
approximated by a dif ferent and ideally far less comple x model structure. This feature, beneficial
for computational speed, has also led to an increasing application of surrogate models in the
domain of chemical engineering (Bhosekar et al., 2018; McBride et al., 2019).
Se veral w orkflo ws for modeling surrogates exist, for which F orrester et al. (2008, p. xvii) depict
an ex emplary scheme. Initially , samples or snapshots from the original model are generated.
Afterwards the actual model structure is designed, adapted to the av ailable sample data and
finally v alidated. One generalistic MatLab framew ork is presented by Gorissen et al. (2010),
providing a v ariety of applicable surrogate types, acti ve learning (sampling data choice), and
automated model selection.
2.4.2 Implementation of Dynamics and Refo rmulation of Equations
Chemical engineering applications are widely characterized by a nonlinear process beha vior .
Thus, sophisticated dynamic models must also suf ficiently co ver dynamics, such as step-wise
32

2.4 Modeling for Dynamic Processes
transitions caused by switching of phase equilibria or stream de-/acti v ation. This is especially
important for the microemulsion system at hand with multiple possible phase configurations.
Hence, continuous switching functions, as well as continuous formulations of conditional state-
ments are outlined in the follo wing. In addition, model simplifications, in v olving structural
reformulation of equations are sho wn in Sec. C.1 and consequently applied throughout the mod-
eling work for this thesis. These reformulations are used to counter the usually large structural
complexity of dynamic process models, which causes slo w con ver gence for simulation and
optimization applications.
Min/Max-op erato rs:
Adhering to physicality , state v ariables such as flo ws, concentrations, or pressures solely sho w
v alues greater than zero. This then also needs to be represented in respectiv e models and thus a
formulation for the smooth max-operator max(0,x) is introduced:
max ( 0 , x ) ≈ 0 + x + (( x − 0 ) 2 + ε ) 0 . 5
2 ε > 0 (2.4)
This function enables the left hand side v alue cut-of f at the v alue of zero, if
x
gets neg ativ e. A
small positi ve v alue
ε
is deployed to render this equation twice continuously dif ferentiable and
a void singularities. As a default v alue ε = 10 − 5 is used throughout this thesis.
If an upper limitation of model v ariables is desired, the respectiv e min(x,a) operator is applied:
min ( a , x ) ≈ a + x − (( x − a ) 2 + ε ) 0 . 5
2 ε > 0 (2.5)
Switching F unctions fo r Dynamic Mo del Switching
In the follo wing, a mathematical expression for the conditional acti v ation of model elements is
introduced. This is of utmost importance for the modeling w ork in this thesis, where dynamic
switching of, e.g., phase separation beha vior is to be represented. T o maintain twice continuous
dif ferentiability , sigmoidal functions are applied using two implementations within this w ork:
Exponential sigmoidal function : T r ig E x p ( x ) = c
1 + exp ( − a · ( x + b )) (2.6)
Smooth square root approximation : T r ig SSQRT = c
2 +
c
2 · ( x + b )
√︁ ( x + b ) 2 + a (2.7)
In both cased
x
is the ar gument of the function and a continuous switching from zero to a
specified parameter v alue
c
is performed, when
( x + b )
changes from neg ativ e to positi ve v alues.
Parameter
a
is used to adjust the steepness of the switching functions. T wo general goals apply
33

2 Theoretical Fundamentals and Background Information
for adjusting
a
. On the one hand a smooth gradient while switching is desired to a void lar ge
Jacobian or Hessian entries and possible singularities. On the other hand, the switch should
quickly approach the desired v alue
c
. Additionally , arguments of the e xponential function larger
than
≈ 300
must be a voided in Eq. (2.6). Figure 2.19 depicts the respecti v e function beha vior
of the sigmoidals for v aried parameters
a
,
b
, and
c
. Comparing the function behavior of both
sigmoid types, it is obvious, that the exponential formulation is adv antageous regarding its
switching beha vior . The smooth square root approximation ho wev er is generally considered as
numerically fa v orable in a voiding the e xponential function.
-10 -8 -6 -4 -2 0246 8 10
x
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
T r ig E xp
Ex p on en tia l Sigm oi da l
a,b,c=1
a=100, b,c=1
a,b=1, c=0.5
a,c=1, b=3

-10 -8 -6 -4 -2 0246 8 10
x
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
T r ig SS Q R T
Sm o oth S q ua re R o ot A pp r o xi m at io n
a,b,c=1
a=1e-8, b,c=1
a,b=1, c=0.5
a,c=1, b=3

Fig. 2.19: Curv ature sigmoidal function implementations, depending on parameters a , b , and c .
2.4.3 P a rameter Estimation and Identifiabilit y Analysis
Closely related to the formulation of process models is the task to identify parameters used
therein. Se veral strate gies can be found in literature to perform
P
arameter
E
stimation (
PE
),
for which a detailed discussion of estimation methods, statistical background, and ev aluation
can be found in (Bard, 1973). The corresponding parameter estimation is then defined as an
optimization problem of the form
θ : = min
θ
Φ W LSQ ( u , θ ) (2.8)
Φ W LSQ ( u , θ ) : = 1
2 ( y ( u , θ ) − ¯ y ) T W − 1
y ( y ( u , θ ) − ¯ y ) . (2.9)
34

2.4 Modeling for Dynamic Processes
with
Φ W LSQ
denoting the objecti ve function in a weighted nonlinear least square formulation,
experimental data
¯ y
,
y
being the model response, and a weighting matrix
W − 1
y
being the in verse
of the measurement error co v ariance matrix. The solution of the problem are the estimated
model parameters
θ
. Assuming measurement errors being independent, of normal distribution,
unbiased, and with zero mean the measurement cov ariance matrix can be deri ved from kno wn
measurement standard de viations and Eq. (2.9) yields the maximum likelihood estimation prob-
lem (Bard, 1973). According to López et al. (2015) the solutions of
PE
problems is usually done
iterati vely using gradient-based methods, such as Le venber g-Marquardt (Marquardt, 1963) or
trust-region methods (Sorensen, 1982). Therein, Jacobian and Hessian information is used to
calculate step size and search direction re garding the parameter v alues to iterati vely approach
the (local) optimum of Eq. (2.8) (L. Biegler et al., 1986).
P a rameter Estimation fo r Empirical Mo dels
Next, a short outline for model preparation and parameter estimation for empirical models is
gi ven. For this, F orrester et al. (2008) provides a comprehensi ve guideline, which forms a basis
for subsequent deri v ations in this work:
Step 1 Modeling:
From system analysis and experimental observ ations, an empirical model
structure is firstly designed. The choice of the model structure is a priori not clear and one is
prone to choose too complex models causing o verfitting of data (and noise). W ith ov erfitting,
interpolation between actual measurement data points might hea vily deteriorate. Hence, revision
of a vailable data under consideration of ph ysical phenomena should be done to identify major
correlations (e.g. quadratic temperature dependence).
Step 2 Estimation:
Gi ven model structure and data, a parameter estimation is performed,
solving the optimization problem from Eq. (2.8).
Step 3 T esting:
Afterwards, the model is tested or cross v alidated using a test data set, which
has been randomly separated from the initial measurement data set (v alidation data) (James et al.,
2017). The accurac y of the validation and test data set is then compared using the
R
oot
M
ean
S quared E rror (RSME) as an accuracy measure o ver n samples:
RSM E = √︃ ∑ n
i = 1 y i − ¯ y i 2
n (2.10)
P a rameter Subset Selection and Identifiabilit y Analysis
For chemical engineering,
PE
problems are usually carried out on highly nonlinear and complex
systems, such as reactions kinetics. Often enough, these problems pose a poor identifiability of
35

2 Theoretical Fundamentals and Background Information
model parameters due to limited e xperimental data, inadequate model structures, or correlated
parameters (Cobelli et al., 1980; Jacquez et al., 1985). According to López et al. (2015, p. 95)
“this generates problems in parameter estimation (PE), e.g. multiple, meaningless, inaccurate or
unstable solutions, and/or con ver gence and numerical problems in the solv er”. T o ov ercome this
hurdle, regularization techniques are applied, such as parameter subset selection (Burth et al.,
1999), truncated singular v alue decomposition (Hansen, 1987), or T ikhonov (T ikhonov, 1963).
D. Müller et al. (2014) provide an algorithm, which e xpands the general idea of subset selection
and identifiability analysis to wards uncertainty quantification of model parameters. The idea is
to estimate v ariances of those parameters more precisely , which are actually identifiable. This
algorithm is used for parameter estimation tasks within this thesis. Detailed deliberations are
wai v ed at this point and it is referred to D. Müller et al. (2014) and Cárdenas (2016).
2.5 Optimal Pro cess Op eration Strategies: State
Estimation & Dynamic Optimization
The inherent complexity and operational challenges of processes in v olving surfactant-based mul-
tiphase reaction media require the consideration of model-based dynamic optimization strategies
at an early stage and systematic (online) inte gration into process automation. Hence, funda-
mentals of (dynamic) real-time optimization, as well as its inte gration into the general plant
automation hierarchy are briefly re vised. Since also the ef ficient determination of the current
plant’ s state is mandatory therein, state estimation procedures are subsequently looked at.
2.5.1 Dynamic Real-Time Optimization and Automation Hiera rchy
Dri ven by the steady increase of computational po wer and improv ements on solvers for optimiza-
tion problems,
R
eal-
T
ime
O
ptimization (
R TO
) emer ged as an essential technique for optimal
process operation and is no wadays widely applied online in chemical and petrochemical industry
(L. Bie gler et al., 2015). Here, optimization problems are directly connected to the process and
consider online data. Usually , steady-state models are deployed, which are solved in r eal-time
with respect to the time constants of the processes (Darby et al., 2011). A typical scheme of a
company’ s plant automation hierarchy with integrated
R TO
is gi ven on the left in Fig. 2.20, for
which the decision frequency rises from top to bottom. It is di vided into a top part, comprising
of fline long-term economically dri ven decision making on corporate planning and scheduling.
Infrequently , the Scheduling and T ime Planning layer provides updated tar get production specifi-
cations and quality constraints stemming from MILP or MINLP optimizations (L. Bie gler et al.,
36

2.5 Optimal Process Operation Strategies: State Estimation & Dynamic Optimization
2009). These are then used in the online, bottom part of the automation hierarchy .
Using online plant data, the Supervisory Online Optimization layer approximates the steady-
state of the plant (plant-model mismatch reduction) and calculates ne w optimal steady-state
conditions or operation trajectories. The transition to wards this ne w optimal operation point
can be handled on a time-discrete optimal trajectory using
M
odel
P
redicti ve
C
ontrol (
MPC
)
(Darby et al., 2011). The
D
istrib uted
C
ontrol
S
ystem (
DCS
) encompasses the regulatory control,
usually applying PID controllers and performing control actions e very second.
Ho we ver , this control structure poses se veral dra wbacks, such as infeasible trajectories since
the steady-state nonlinear
R TO
neglects the transition of steady states or instabilities due to the
mismatch of
R TO
and
MPC
models. This is especially se vere for highly dynamic nonlinear
processes, which are ne ver at a steady-state (T oumi et al., 2005; Zav ala, 2008). Consequently ,
the dynamic model is to be implemented into the optimization process itself. Thus
R TO
and
MPC
are mer ged into one
D
ynamic-R TO layer . A v ariety of de v elopments on that can be found,
whose feasibility is further enhanced through recent de velopments on algorithms for the ef fi-
cient solution of lar ge-scale
NLP
problems (L. T . Bie gler, 2018). Recent applications include
nonlinear model predicti ve control for a two- stage distillation sequence (L. Biegler et al., 2015)
or adsorption chillers (Bau et al., 2019), economic NMPC’ s e valuated in a re vie w by W olf et al.
(2016) or applied on air separation units (Caspari et al., 2018), and dynamic real-time optimiza-
tion applied on polymerization reactions (Pontes et al., 2015).
Field-devices:
sensors and actuators
Regulatory control
SCADA Network / DCS
Supervisory Level
Online-Optimization
Scheduling /
T ime planni ng
Advanced control /
Model predictive control
Corporate
Planning
Result analysis
Plant with
sensors
Dynamic
Optimization
Data reconcil. /
State Estimation
𝑢 0
𝑜𝑝𝑡
u opt
y ~ sec
Offline GC
samples
Model adaption
Result
implementation /
DCS
external
controls
y r ec , x
u appl
y ~ hrs
y : measurements
x : model states
u : controls
online offline

Fig. 2.20:
Left: Real-time decision making and process automation hierarchy . Figure redrawn and ex-
tended from (L. Biegler et al., 2009). Right: Control loop for dynamic real-time optimization.
A typical control loop for D-R TO is depicted on the right in Fig. 2.20. Core elements are the pro-
cess itself, including sensors and actuators, as well as a connected
DCS
handling the regulatory
control of this plant relying on measurement data
y
. This data is subdi vided into av ailable online
sensor data and slo w measurements, such as quality sampling from offline analytical methods.
The measurements are then v alidated to av oid the implementation of faulty data, like gross error
37

2 Theoretical Fundamentals and Background Information
or noise. This is usually included into a state estimation step, which aims for the calculation
of a v alid model state for a giv en future time according to the giv en measurements. This step
is of utmost importance, since these calculated model states
x
serve as the initial conditions
for subsequent optimization tasks and naturally not all model states
x
are measured. Based on
this, a model adaption through parameter refitting is often placed prior to the optimization to
handle plant-model mismatch (C. Y . Chen et al., 1987; Faber et al., 2007). More sophisticated
approaches, such as the modifier adaption with quadratic appr oximation assume also structurally
imperfect plant models. The plant-model mismatch is then handled by iterativ ely adding bias
and gradient corrections to the objecti ve function and constraints of the model to match it to the
plant state (Ahmad et al., 2019).
The standard approach, ho we ver , considers the calculation of an optimal trajectory i.e. con-
troller setpoints
u o pt
0
for a gi ven plant model and adhering to an economic cost function as
well as constraints. The optimization problem therein is usually formulated as an
N
on
L
inear
P rogramming (NLP) problem deploying a time-discretized plant model:
Definition 2.1 (Optimization pr oblem f ormulation f or NLP’ s):
ar g min
u f ( x , u , P ) (2.11)
s . t . g ( x , u , P ) = 0 (2.12)
h ( x , u , P ) ≤ 0 (2.13)
x LB ≤ x ≤ x U B (2.14)
u LB ≤ u ≤ u U B (2.15)
Here,
x ∈ R n x
is the vector of model states,
u ∈ R n u
the vector of decisions or controls, and
P ∈ R n P
(fixed) model parameters.
f : R n x + n u + n P → R
the objecti ve function,
g : R n x + n u + n P →
R n eq
the
n eq
vectorial function containing equality constraints, and
h : R n x + n u + n P → R n ineq
the
inequality constraints. Additionally , x and u adhere to specified lower and upper bounds.
Obtained results
u ( t k )
from this optimization problem are checked re garding consistency and
applicability , yielding
u o pt ( t k )
. The latter are then handed forward to the
DCS
, which implements
these optimal trajectories into the process. It has to be remarked, that for online applicability the
a vailability of optimal trajectories must be ensured at an y time. Therefore, parallel time-wise
shifted instances of the
D-R TO
loop are usually employed, or a backup of fline optimal trajectory
( e xternal contr ols ) is provided.
38

2.5 Optimal Process Operation Strategies: State Estimation & Dynamic Optimization
2.5.2 State Estimation
In the pre vious section, the necessity of estimating unmeasured or correcting measured states of
a plant model from actual plant measurement data was outlined. T o tackle this, se veral strate gies
for state estimation, also called observers ha ve been de veloped to ef ficiently track these missing
states and ov ercome technical measurability limitations or reduce costs for sensor installation
(Dochain et al., 2009). F or the sake of completeness, the reader is referred to Ali et al. (2015),
who provide an extensi v e re vie w and classification scheme of recent observ er implementations.
Follo wing this classification and based on our o wn work re garding the application of state
estimation methods on nov el surfactant-based multiphase reaction media (W eigert et al., 2018),
the focus is laid on Bayesian Estimators . This is mainly due to their applicability on nonlinear
dynamic systems and handling of constraints (Ji et al., 2015).
Ba y esian State Estimation
These methods are mainly based on the estimation of the probability density function of model
states based on a vailable measurement data and are naturally deri ved from stochastic considera-
tions. The general state estimation problem is described by the time-discrete dynamic system
x k + 1 = F ( x k , u k ) + w k with w ∼ N ( 0 , Q ) (2.16)
y k = h ( x k + v k ) with v ∼ N ( 0 , R ) (2.17)
for which
x
are the model states at time point
t k
,
u
system controls, and
y
the measured v ariables.
F
then is the discretized model and
h
the time-in v ariant part of the model, holding correlations
of
y
with model states.
w
and
v
then represent the independent random process and measurement
noise with respecti ve co variance matrices Q and R (López-Ne grete et al., 2012).
Bayesian estimators no w aim at determining the estimate of the state trajectory
X k
k − N
on the
time horizon from current time point
k
till
N
past steps under the condition that for
t 0 . . . t k
measurements
Y k
0
occurred. Thus, an optimization problem is set up, maximizing the conditional
probability P ( X k
k − N | Y k
0 ) through manipulating states x k :
max
x k − N ,..., x k
P ( X k
k − N | Y k
0 ) (2.18)
X k
k − N = { x k − N ,..., x k } (2.19)
Y k
0 = { y 0 ,..., y k } (2.20)
Including the entire a vailable measurement data time horizon into the optimization problem
would result in e xtremely large equation systems and is thus infeasible. T o ov ercome this hurdle,
39

2 Theoretical Fundamentals and Background Information
generally two approaches are considered (Ra wlings et al., 2006): firstly recursiv e predictor-
corrector methods, such as e xtended Kalman F ilter , unscented Kalman F ilter , or sequential
Monte Carlo F ilters and secondly
NLP
formulations on lar ger b ut finite time horizons consider-
ing the last N measurements, the so called M oving H orizon E stimators (MHEs).
Unlike the filtering methods, the
MHE
provides a more generic approach to wards state estimation
and does not rely on simplifications. It is thus applicable for any nonlinear process (Küpper et al.,
2009). It provides rob ust con v er gence features, also under erroneous a-priori distributions of
measurements or states and additionally allo ws for the handling of constraints on state variables
(Nicholson et al., 2014). Moreov er , it enables the handling of different sampling rates and/or
delays of measurements, which is a very common case for chemical processes (Krämer et
al., 2005; Ji et al., 2015). In contrast, mentioned filtering methods se verely rely on accurate
estimates of the process noise and the respecti ve co v ariance matrix
Q
(W eigert et al., 2018).
Gathering
Q
for a technical plant system, a non-exact model, and limited prior measurement
data is considered challenging. Follo wing the discussion in our o wn contrib ution (W eigert
et al., 2018),
MHE
is hence reg arded as superior concerning the desired application on the
hydroformylation in microemulsion and further looked at in detail.
Moving Ho rizon Estimato r F o rmulation
As already stated abov e, the moving horizon state estimator only considers model states and mea-
surements in a predefined past time interv al. The actual formulation of the estimator is performed
based on the follo wing statistical assumptions (Z. Chen, 2003; W . Chen et al., 2004):
■
All states
x
adhere to a first order Marko v process. Thus the next state
x k
can be predicted
by only kno wing the last state x k − 1 .
■ Noise of measurements are independent from those of the states.
■
Measurements are mutually independent. In practice this might be violated because of
e,.g., hysteresis ef fects.
■
All noise v ariables hav e zero mean and are assumed Gaussian, as defined in Eq. (2.16)-
2.17. This is practically not always ensured, since baseline-of fsets of sensors might appear
as well as hanging sensors or measurement range violations.
Considering the problem statement from Eq. (2.18), the conditional probability density function
P ( X k
k − N | Y k
0 ) can be re written applying Bayes theorem:
P ( X k
k − N | Y k
0 ) = P ( Y k
0 | X k
k − N ) · P ( X k
k − N )
P ( Y k
0 ) (2.21)
40

2.5 Optimal Process Operation Strategies: State Estimation & Dynamic Optimization
Based on this, the optimization problem in Eq. (2.18) can be further de veloped to wards the
smoothed arrival cost objecti ve function formulation of the MHE (Rao et al., 2000):
Definition 2.2 (NLP Moving Horizon Estimation):
{ ˆ x k − N ,..., ˆ x k } = ar g min
{ x k − N ,..., x k } (︃ φ ( k − N ) + 1
2
i = k
∑
i = k − N
v T
i R − 1
i v i + 1
2
i = k
∑
i = k − N
w T
i Q − 1
i w i )︃ (2.22)
s . t . x i + 1 = F ( x i , u i ) + w i (2.23)
y i = h ( x i ) + v i (2.24)
x LB ≤ x ≤ x U B (2.25)
Therein,
ˆ x
is the optimal state v ector at a specific discretization point, for which inequality
constraints reg arding
x LB
and
x U B
apply .
v
and
w
are the Gaussian measurement and process
noises with their respecti ve co variance matrices
R
and
Q
.
f es t
described a correlation or estimator
function, e v aluating and weighting the de viation of model and measurement variables
y
. Finally ,
F is the respecti ve process model and h the measurement model.
In Def. 2.2,
φ ( k − N )
describes the arrival cost , which represent a corrector based on the in-
fluence of past measurements prior to the current horizon. This becomes important for state
estimations on short horizons for which the information content of measurement is insuf ficient to
obtain adequate results. In contrast, the influence of
φ ( k − N )
becomes negligible with gro wing
estimation time horizon (López-Negrete et al., 2011). Statistically it represents the conditional
probability of gaining
x
at time step
k − N
under the condition, that all prior measurement data
Y k − N − 1
0
occurred. This probability is not accessible from scratch and thus se veral techniques
ha ve been de veloped to approximate the arri v al cost. Often enough filtering techniques based
on the extended Kalman filter or sequential Monte Carlo filter are deplo yed, which are again
disadv antageous regarding unconstrained states and necessarily accurate estimates of
Q
(Rawl-
ings et al., 2006; López-Negrete et al., 2011). Further de velopments thus approximate the arri v al
costs using the in verse of the reduced Hessian of the
NLP
Lagrange from prior estimations
(López-Negrete et al., 2012).
Data Reconciliation and Handling of Gross Erro rs
Measurement data in chemical plants are often erroneous and inconsistent with respect to the
plant model and conserv ation equations therein. In contrast to the assumptions made for deri ving
the
MHE
, these de viations or noises can also be non-Gaussian. This is true, if systematic errors
or of fsets, sensor failure, and introduced gross error due to wrongly prepared of fline quality
41

2 Theoretical Fundamentals and Background Information
samples are considered (Zhang et al., 2015). Handling of these is crucial, since otherwise the
state estimation result de viates significantly from the true state and the subsequent D-R TO might
yield infeasible results, leading to violated safety restrictions or infeasible process operation.
T o ov ercome this hurdle, data reconciliation strategies ha ve been proposed and de veloped to-
wards application on dynamic nonlinear systems (Özyurt et al., 2004). Therein, estimators
f es t
are used for weighting the measurement de viation
v
between real data and the model
v = y − ˆ y
, de-
ployed in a minimizing optimization problem. A widely applied class are Maximum-Likelihood-
Estimators. For those, M-estimators, such as Fair -Function and Redescending-Estimator provide
a lar ge rob ustness regarding gross errors (Nicholson et al., 2014). At this point, it is proposed to
directly implement these estimators into the Bayesian
MHE
to enable the direct reconciliation of
data within the state estimation step, without further handling in an external data reconciliation
loop. Related to Def. 2.2 this implies the follo wing substitution:
i = k
∑
i = k − N
v T
i R − 1
i v i =
i = k
∑
i = k − N
f es t ( v i ) (2.26)
This approach has been lar gely tested in our own prior w ork, using artificial data from an early
model implementation of the hydroformylation of 1-dodecene in microemulsions (Hof fmann
et al., 2016). Concluding, a comparison of estimators relev ant for this work, their definitions,
and remarks reg arding their implementation is giv en:
Definition 2.3 (F air -Function ):
The Fair -Function as defined by (Özyurt et al., 2004) is
a con vex Hubert- estimator and thus provides continuous first and second order deri vati v es.
The parameter
C
is used for tuning and is set to 6 in accordance with (Nicholson et al.,
2014).
f F F = C 2
F F [︃ | v
σ |
C F F
− ln (︃ 1 + | v
σ |
C F F )︃]︃ (2.27)
42

2.5 Optimal Process Operation Strategies: State Estimation & Dynamic Optimization
Definition 2.4 (Redescending-Estimator):
The redescending-estimator was proposed by
Hampel (1974) and enables an ef ficient cut-off of the estimator function v alue on rising
measurement de viations
v
. Ho wev er , its standard implementation is non-con v e x and thus a
smoothed formulation is gi ven belo w . P arameters
a RE
,
b RE
, and
c RE
are deployed to adjust
interv als of quadratic, linear or constant behavior of the estimator .
f RE =
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
1
2 (︁ v
σ )︁ 2 , 0 ≤ | v
σ | ≤ a RE
a RE · | v
σ | − a 2
RE
2 , a RE < | v
σ | ≤ b RE
a RE · b RE − a 2
RE
2 + a RE · ( c RE − b RE )
2 [︃ 1 − (︂ c RE −| v
σ |
c RE − b RE )︂ 2 ]︃ , b RE < | v
σ | ≤ b RE
a RE · b RE − a 2
RE
2 + a RE · ( c RE − b RE )
2 , | v
σ | > c RE
(2.28)
Smoothed resdescending-estimator:
f RE , g = − c 1 ⎛
⎝ 1 − 2
1 + e x p (︂ − 2 · κ 1 (︁ v
σ )︁ 2 )︂ ⎞
⎠ − c 2 ⎛
⎝ 1 − 2
1 + e x p (︂ − 2 · κ 2 (︁ v
σ )︁ 2 )︂ ⎞
⎠ (2.29)
Approximation parameters
c 1
,
c 2
,
κ 1
, and
κ 2
are to be adjusted in parameter estimation
according to Eq. (2.28) and using tuning parameters a RE , b RE , and c RE therein.
In Fig. 2.21 a comparison of these rob ust estimators to the well kno wn weighted least-squares
estimator is gi ven. It is apparent, that both, Fair -Function and Redescending-Estimator are
superior , since the occurrence of gross errors (large v alues of
v
) only has reduced influence
-6 -4 -2 0 2 4 6
Argument value / -
0
5
10
15
20
Estimator / -
Least-squares
Fair-Function
Redesc.-Estimator

Fig. 2.21:
Comparison of least-squares, Fair -Function, and Redescending-Estimator . Used parameters:
C F F = 6, a RE = 1, b RE = 2, c RE = 4.
43

2 Theoretical Fundamentals and Background Information
on the estimators. Howe v er , due to the non-con ve xity of the Redescending-Estimator and thus
also of deployed objecti ve functions, con ver gence into local minima is possible. Hence, for
this thesis the Redescending-Estimator is used together with an initialization run based on the
Fair -Function as con ve x estimator .
State Estimation on Multiple Sampling F requencies
T o this end, presented state estimators depend on the av ailability of all necessary measurements
to calculate respecti ve probability density functions. For online application of state estimation,
this is true for measurements, such as temperature or pressure, which are av ailable e very , e.g.,
second. In contrast, concentration data is usually only infrequently a v ailable. Moreo ver these
measurements are delayed due to sampling and analysis time. Ideally , the fast measurements are
still suf ficient to observe or estimate the state v ector and the mo ving horizon estimator might be
applied as is. In case of a high rele v ance of slo w and delayed measurements, state estimation is
to be adapted to wards the handling of multi-rate measurements (López-Ne grete et al., 2012).
Connected to this, the measurability of a system, also named observ ability , is of interest:
Definition 2.5 (Measurability):
The system defined by Eq. (2.16)-2.17 is measurable if it
is possible for all
t ∈ [ 0 , t end ]
to deri ve the initial conditions of the system
x 0
for any initial
point solely from the a vailable measurements
y ( t )
and control v ariables
u ( t )
(Salau et al.,
2014).
Focusing on
MHE
s, successful implementations for state estimation using multi-rate measure-
ments can be separated into two major approaches: fix ed structure and variable structure esti-
mators. The former use slo w measurements only on av ailability and otherwise perform mostly
linear or polynomial extrapolation of the measurement noise for the slo w measurements (T atiraju
et al., 1999; Kramer et al., 2005). Howe v er , this is disadvantageous re garding high nonlinear and
dynamic systems or significant measurability impro v ement of the model states through the slo w
measurements. F or the latter , López-Negrete et al. (2012) pro vide an implementation with a v ari-
able structure of the measurement model, depending on the a v ailability of slo w measurements
and subsequent (also v ariable) arri v al cost formulations.
Ho we ver , all implementations and case studies still assume a moderate ratio of sampling rates
between 1:10 and 1:100. Practical applications on real plant systems, as presented also in
this work ne vertheless hold ratios of sampling rates of more than 1:10000. T o cope with this,
weighting of the objecti ve function elements or e ven sequential tw o-step estimation approaches
are to be considered.
44

3 Systematic Analysis of Reactive
Multiphase Systems –
Hydrofo rmylation of 1-do decene in
Micro emulsions
Due to the complexity of reacti ve microemulsion systems,like introduced in Sec. 1.1, the deri v a-
tion of a suitable process or unit design, as well as strategies for process operation is still
challenging. Usually , a multitude of in vestigations and iterations in de v elopment stages between
lab-scale and a mini-plant test system are required. W ithin the scope of this work, the obsta-
cles for the realization of such processes are faced with a systematic analysis of the system
and its operability (Fig. 2.1). This encompasses not only the liquid multiphase system and the
hydroformylation reaction, but also interactions of both and the application in a continuously
operated mini-plant. Aiming for such a technical application, Fig. 1.2 already sho wed the desired
scheme of the process design. It combines the realization of the reaction in a reactor unit, as
well as a subsequent multiphasic separation in a settler . Both unit operations are characterized
by high operational complexity . This is further increased by multiple internal recycles, which
are mandatory for the ef ficient reuse of catalysts and additi ves.
T o cope with this, a systematic influence identification for the catalytic system and the reaction
kinetics is performed based on Fig. 2.18 and under consideration of the technical realization
and resulting obstacles. In the follo wing, the guideline for the systematic analysis of multiphase
media for process operation, presented in Sec. 2.3.2 is applied on the system at hand. The
obtained results form the cornerstone for subsequent modifications on the process design and
the deri v ation of strategies for process operation .
45

3 Systematic Analysis of Reacti ve Multiphase Systems
3.1 Applied Substances
Chemical substances used within this work are listed in T ab . 3.1 together with information on
purity and vendors. The microemulsion system hereby is formed with 1-dodecene as long-
chained oily substrate, the nonionic surfactant Marlipal
®
24/70, and an aqueous catalyst solution
consisting of water , rhodium precursor , and SulfoXantPhos as a ligand.
T ab. 3.1: Applied substances for plant operation, lab experiments, and analytics.
Substance Purity CAS-Number Supplier
1-dodecene ≥ 94 % 112-41-4 Merck KGaA
2-propanol ≥ 99 . 9 % 67-63-0 Carl Roth
Dodecane ≥ 99 % 112-40-3 Merck KGaA
Marlipal ® 24/70 - 68439-50-9 Sasol 1
Nitrogen ≥ 99 . 8 % 7727-37-9 Linde
[Rh(acac)(CO) 2 ] 2 ≥ 98 % 14874-82-9 Umicore
Sodium sulfate ≥ 99 % 7757-82-6 Carl Roth
SulfoXantPhos - 3 215792-51-1 Molisa GmbH
Synthesis gas CO 3 . 7 630-08-0 Linde
(CO:H 2 1 : 1 mol % ) H 2 5 . 0 1333-74-0 Linde
T ridecanal ≥ 96 % 4 10486-19-8 Alfa Aesar
T ridecanoic acid ≥ 99 % 638-53-9 Sigma Aldrich
W ater
deionized,
deg assed
7732-18-5 -
1
nonionic aliphatic surfactant pro vided as a donation from Sasol without further infor-
mation on purity (technical grade)
2 Rhodium precursor (Acetylacetonato)dicarbonylrhodium(I) donated by Umicore
3 purchased from Molisa GmbH without further information on batch quality
4 contains stabilizer α -tocopherol
3.2 Hydrofo rmylation Reaction Net w o rk
W ithin this work, the C12-alkene 1-dodecene is used as a model substance to in vestigate the hy-
droformylation of long-chained unsaturated substrates in
MES
s. The con version of 1-dodecene
in the presence of synthesis gas and a transition metal catalyst yields the tar get tridecanal as
the tar get product and se veral byproducts, such as isomers, aldols, alcohols, and h ydrogenation
46

3.2 Hydroformylation Reaction Network
products (Pruett, 1979). Thus, the kno wledge of the underlying reaction network, reaction kinet-
ics, and especially rele v ant parameters influencing the ov erall reaction performance is vital.
Extensi ve studies on the reaction network ha v e been performed for the system at hand. Markert
et al. (2013) identified the reaction network sho wn in Fig. 3.1 consisting of six main reactions:
T arget Reaction
r H y f oB Hydroformylation of 1-dodecene to wards tar get product tridecanal
Side Reactions
r iso Isomerization of 1-dodecene (equilibrium reaction)
r H yd A Hydrogenation of iso-dodecene and formation of dodecane
r H yd B Hydrogenation of 1-dodecene and formation of dodecane
r H y f oA Hydroformylation of 1-dodecene to wards byproduct iso-tridecanal
r H y f o C Hydroformylation of iso-dodecene to wards byproduct iso-tridecanal
1-dodecene
dodecane
iso-tridecanal
tridecanal iso-dodecene
r iso
r Hyd A r Hyd B
r Hyfo A
r Hyfo C
r Hyfo B
CO+H 2
CO+H 2 CO+H 2
H 2 H 2

Fig. 3.1:
Hydroformylation reaction network with isomerization, h ydrogenation, and hydroformylation
reaction paths postulated by (Markert et al., 2013). Figure taken from (Pogrzeba et al., 2019).
Caused by the side reactions, a v ariety of internal alkenes and branched aldehydes can theoret-
ically be formed. Howe v er , for long-chained alkenes, hydroformylation reaction rates signifi-
cantly drop for internal alkenes (W ender et al., 1956). In this case, iso-tridecanal is considered as
a pseudo-component mainly represented by 2-methyldodecanal for the remainder of this thesis.
Accordingly , iso-dodecene is introduced as a pseudo component for all internal C12-alkenes.
Optimal Reaction System and Reference Kinetic T rajecto ry from Lab-Scale
Se veral prescreening e xperiments regarding the formulation of the microemulsion and optimal
conditions for the hydroformylation of 1-dodecene ha ve been carried out (Rost et al., 2013;
Hamerla, 2014; Pogrzeba et al., 2015). Here, the nonionic aliphatic surfactant Marlipal
®
24/70
was found to be a suitable emulsifier for 1-dodecene in an aqueous catalyst system. The water
47

3 Systematic Analysis of Reacti ve Multiphase Systems
solubility of the rhodium catalyst [[Rh(acac)(CO)
2 ]
] is achie ved by using the sulfonated diphos-
phine ligand SulfoXantPhos (see Fig. 2.10). For such a system, the typical reaction trajectory
for experimentally optimized conditions is gi v en in Fig. 3.2. The reaction performance is charac-
terized by a v ery high chemo-selecti vity of up to 94 % to wards tridecanal and linear to branched
(n/iso) selecti vities abov e 98 %. W ithin 48 h of reaction, only small amounts of byproducts are
formed with the isomerization being dominant among them.
0 10 20 30 40 50
R ea cti on t ime i n h
0
20
40
60
80
100
M ass fr act ion in w t.- %
1- do d ece n e
tr id ec an a l
is o- tr id ec an a l
is o- do d ece ne
do d ec a ne

Reaction Conditions
T R = 95 °C
p R = 15 bar
Composition
α = 0.5
γ = 0.08
[Rh] = 298 ∙ 10 -4 wt.- %
[SX] = 4500 ∙ 10 -4 wt.- %

Fig. 3.2:
Reference kinetic trajectory from the lab-scale for the hydroformylation of 1-dodecene. Exper-
imental conditions: SX:Rh ratio = 4:1 with [Rh(acac)(CO)
2 ] = 1 · 10 − 3 mol L − 1
,
2 . 4 mol L − 1
1-dodecene, 20 g water , 3.5 g Marlipal
®
24/70, 1 wt.-% Na
2
SO
4
, reaction v olume
= 50
mL,
stirrer speed
= 1200
rpm. Maximum measurement error:
±
3 %. Data generated by T obias
Pogrzeba, T echnische Univ ersität Berlin, Department of Chemistry .
3.3 Analysis of Influencing F acto rs on Reaction
P erfo rmance in the Mini-Plant
For the transfer of the hydroformylation reaction from the lab-scale into the mini-plant (intro-
duced in Sec. 4.1), se veral influences on the reaction performance are of interest. Exemplarily ,
the concentration of the surfactant is prone to v ary in the reactor of a mini-plant with internal
recycles. Hence, additional v ariations of concentrations or completely ne w influences hav e to be
considered and e v aluated. This way , tailored improv ements of unit design and additional de vel-
opment of kinetic models focusing on the reaction performance in the mini-plant are enabled.
48

3.3 Analysis of Influencing Factors on Reaction Performance
Such an analysis has been carried out on the system at hand using a close feedback of lab-scale
in vestigations and pretesting in a mini-plant system. Initially , influencing factors with their
direct impact on the reaction or catalytic system ha v e been identified via a comparison of the
reaction conditions in the used laboratory scale setup (see (Pogrzeba et al., 2017b)) and the
mini-plant system, as well as sev eral mini-plant test runs. The subsequent quantification was
then performed on the lab-scale to ensure reproducibility .
T o guide the following discussion of results, T ab . 3.3 is introduced. Note, that the presented
experimental data in the remainder of this section were obtained in cooperation with T obias
Pogrzeba, T echnische Uni versität Berlin, Department of Chemistry and are partly already pub-
lished in our o wn contributions (Pogrzeba et al., 2017b; Pogrzeba et al., 2019).
T ab. 3.3: Additional identified influencing factors on reaction performance for the mini-plant system.
Influencing
factor
Influenced by Effect r eaction
perf ormance
Refer ence
Rhodium
concentration
feed, separation and
recycle operation
a v ailable activ e catalyst
and equilibria;
reaction rates and
selecti vity
Figure 2.11
Figure A.1
Ligand
concentration
feed, separation and
recycle operation,
decomposition
cat. species equilibria;
reaction acti vity and
selecti vity
Figure 2.11
Figure 3.3
Dissolved gasses system pressure,
phase composition
in reactor and
settler
cat. species equilibria;
reaction acti vity and
selecti vity
Figure 2.11
Figure 3.4
Micelle
structur e, phase
beha vior
mixture
composition,
temperature
none observed Figure 3.5
Emulsification,
interfacial ar ea
stirrer speed none in operation region Figure A.2
Surfactant
concentration
feed, separation and
recycle operation,
loss product stream
reaction rates Figure 3.6
49

3 Systematic Analysis of Reacti ve Multiphase Systems
Catalyst and Ligand Concentration
First of all, the ef fect of the catalyst concentration is discussed. Interestingly , the catalyst acti vity
is highest for lo w catalyst concentrations (diagram in Fig. A.1). Ho wev er , the selecti vity tow ards
the main product tridecanal is also lo w and only stabilizes at higher catalyst concentrations abov e
2 . 5 · 10 − 4 mol L − 1
. A similar beha vior is found in Fig. 3.3 re garding reaction con version and
selecti vity for dif ferent ligand concentrations. As stated by Pogrzeba et al. (2019), “this reaction
beha vior is due to the two preequilibria of the acti ve Rh-sulfoxantphos comple x (Fig. 2.11,
species 1a) with the corresponding dimeric Rh species (1c) and the unmodified Rh species (1b)”.
Especially lo w ligand concentrations in the presence of CO are prone to promote the formation
of the inselecti ve species (1b), featuring higher con v ersion b ut also increased formation of
byproducts (Börner et al., 2016, p. 18). This is to be considered for mini-plant operations, as
concentration shifts can se verely alter the reaction performance.
024 6 8
0
20
40
60
80
100
Ligand:Metal Ratio in mol/mol
in %
Con version
Selecti vity TDC
Fig. 3.3:
Influence of ligand concentration on con version and chemo selecti vity to wards the tar -
get product tridecanal for the hydroformylation of 1-dodecene. Experimental conditions:
[Rh(acac)(CO)
2 ]= 1 · 10 − 3 mol L − 1
, 2.4
mol L − 1
1-dodecene, 20 g water , 3.5 g Marlipal
®
24/70, (
α = 0 . 50
,
γ = 0 . 08
), 1 wt.-% Na
2
SO
4
, reaction v olume = 50 mL.
p R = 15 bar
syn-
gas,
T R = 95 ◦ C
, stirrer speed = 1200 rpm, duration: 4 h. Maximum measurement error:
±
3% .
Also, ligand degradation can occur during long-term operation. This was recently in v estigated
by Gerlach et al. (2017) for the diphosphite ligand BiPhePhos in thermomorphic solv ent systems.
In their case, hydroperoxides were present in the substrate feed causing oxidation of the ligand.
Despite the fact that high hydroperoxide amounts were found in 1-dodecene pro vided by Merck
KGaA, this decomposition path was not observ ed in lab-scale experiments and is considered not
to be rele v ant. Possibly , a stabilization of the ligand in the aqueous phase takes place in the case
of the MES.
50

3.3 Analysis of Influencing Factors on Reaction Performance
Concentration of Dissolved Gasses
Carbon monoxide plays an important role in catalytic equilibria and the formation of inselecti ve
unmodified Rh species, as described in Sec. 2.2. For a continuously operated mini-plant, con-
centrations of dissolved g asses are assumed to be constant for the reactor . Ho we v er , when the
mixture enters the settler , multiple phases are de veloped. Due to the creeping flow of phases, gas
replenishing is limited to dif fusion from the bulk g as phase through all liquid phases. Related to
this, Fig. 3.4 depicts the comparison of kinetic experiments. F or these, the stirrer was stopped
for a specific time to mimic the situation of mixture separation in a settler and subsequent reen-
tering into the reaction zone of the mini-plant (stirrer restart). It is obvious that for long resting
times (2 h) chemo-selecti vity significantly drops and large amounts of iso-dodecene are formed.
Interestingly , con version increases by 22 percentage points, e v en for the separated system.
0 . 0 0 . 5 1 . 0 1 . 5 2 . 0 2 . 5 3 . 0 3 . 5 4 . 0
0
20
40
60
80
100
Reaction time in h
Con version in %
Reference 1h Stop 2h Stop
0
20
40
60
80
100
Selectivity in %

Fig. 3.4:
Influence of stopped stirrer operation on con version and chemo-selecti vity to wards the tar -
get product tridecanal for the hydroformylation of 1-dodecene. Experimental conditions:
[Rh(acac)(CO)
2 ] = 1 · 10 − 3 mol L − 1
, molar ratio SX:Rh 4:1, 2.4
mol L − 1
1-dodecene, 20 g wa-
ter , 3.5 g Marlipal
®
24/70, (
α = 0 . 50
,
γ = 0 . 08
), 1 wt.-% Na
2
SO
4
, volume = 50 mL.
p R = 15 bar
syngas, T R = 95 ◦ C, stirrer speed = 1200 rpm. Maximum measurement error: ± 3 %.
W ith an immobile stirrer , phase separation takes place forming the
2
-system at high temperatures
(Sec. 2.1). Hence, larger amounts of catalyst are carried into the oil phase due to the surf actant
being located there. W ith carbon monoxide solubility being rather high in the oil phase (see gas
solubility data in Sec. 4.3.5) and the ligand being w ater soluble, the local concentrations thus
promote the presence of the unmodified Rh species (species 1b in Fig. 2.11). This results in
increased non-selecti ve alk ene con version despite possible limitations of mass transfer . Ho we ver
this ef fect does not set in immediately and is due to ongoing reaction in the oil phase or at the
interface. For a one hour -long stirrer stop only negligible changes in selecti vity and con v er -
sion were observed. Accordingly , the residence time in the separation and recycle section of a
continuously operated mini-plant should be kept as lo w as possible.
51

3 Systematic Analysis of Reacti ve Multiphase Systems
Micelle Structure, Phase Behavio r, and Emulsification
For
MES
s a v ariety of phase states are possible (Fig. 2.4). Apart from that, structure and
size of micelles as well as the types of continuous and disperse phases change dramatically .
Ne vertheless, the reaction performance is not af fected by this for the system at hand. Figure 3.5
confirms that with a comparison of reaction kinetics at equal conditions, b ut modified emulsion
type. According to Pogrzeba et al. (2017b), “the hydroformylation in the water -in-oil (w/o)
emulsion is slightly slo wer due to the higher amount of salt in the mixture”. In addition to that,
Fig. A.2 depicts the ef fect of the stirrer speed on the reaction trajectory . Again no significant
influence is observ able. In both cases the feature of
MES
to form nano-scaled droplets due to the
lo w interfacial tensions is beneficial. Suf ficiently high interfacial area e ven at lo w energy input
into the system is thus provided. In consequence, it can be stated, that the hydroformylation in
MES is not limited by mass transfer .
01234
0
10
20
30
Reaction time in h
Con version of 1-dodecene in %
2 region
3 region
¯
2 region
Fig. 3.5:
Influence of phase beha vior on con v ersion of 1-dodecene. Experimental conditions:
[Rh(acac)(CO)
2 ]= 1 · 10 − 3 mol L − 1
, molar ratio SX:Rh 4:1, 2.4
mol L − 1
1-dodecene, 20 g
water , 3.5 g Marlipal
®
24/70, (
α = 0 . 50
,
γ = 0 . 08
), 1 wt.-% Na
2
SO
4
, reaction volume = 50 mL.
p R = 15 bar
syngas,
T R = 95 ◦ C
, stirrer speed = 1200 rpm, duration: 4 h. Na
2
SO
4
added to
adjust phase behavior: 2: 0.1 wt.-%, 3: 1 wt.-%, 2: 3 wt.-%. Max. measurement error: ± 3% .
Surfactant Concentration
The last finding only holds, in case sufficient emulsifier is present to enable
ME
formation.
Naturally , the amount of surfactant has a crucial ef fect on the reaction, as can be seen in Fig. 3.6.
The amount of surfactant determines the amount of aqueous and oily phase, emulsified by the
microemulsion (lo wering their mutual interfacial tension). Thus, the con version increases rapidly
with rising
γ
. In contrast, the selectivity is constantly abo ve 90 %. For tracking and predicting the
reaction performance in a mini-plant system, sensing and control of the surfactant concentration
52

3.4 Systematic Analysis of Microemulsion Systems for Process Design and Operation
is therefore mandatory . Additionally , a distinct upper limit for
γ
exists, since the formation of
one homogeneous phase (W insor I system, Fig. 2.5) at high surfactant concentrations pre vents a
successful phase separation and thus settler operation.
0 5 10 15 20 25 30
0
20
40
60
80
100
Surfactant concentration γ in wt.-%
in %
Con version
Selecti vity TDC
Fig. 3.6:
Influence of surfactant concentration on con version and chemo selecti vity to wards the tar -
get product tridecanal for the hydroformylation of 1-dodecene. Experimental conditions:
[Rh(acac)(CO)
2 ]= 1 · 10 − 3 mol L − 1
, molar ratio SX:Rh 4:1, 2.4
mol L − 1
1-dodecene, 20 g
water , (
α = 0 . 50
), 1 wt.-% Na
2
SO
4
, reaction v olume = 50 mL.
p R = 15 bar
syngas,
T R = 95 ◦ C
,
stirrer speed = 1200 rpm, duration: 4 h. Maximum measurement error: ± 3% .
3.4 Systematic Analysis of Micro emulsion Systems fo r
Pro cess Design and Op eration
The systematic guideline presented in Sec. 2.3.2 is no w applied. Again, the focus lies on the
actual application in the a vailable mini-plant. Hence, rele v ant information on system phenomena
and challenges is desired.
3.4.1 Step 1: Definition of System Requirements & Comp onent System
Pro cess Requirements fo r a Continuously Op erated Mini-Plant
Initially , goals and requirements for the desired process operation are specified. Then, av ailable
information on technical equipment and dimensioning, as well as the applied substances is
gathered.
53

3 Systematic Analysis of Reacti ve Multiphase Systems
■ F ast separation:
The residence time in a settler unit for phase separation must be belo w
60 min to a void byproduct formation (see Fig. 3.4). It is set to 30 min.
■ Reaction perf ormance and efficient separation of oil:
The
MES
formulation should
enable ef ficient emulsification of 1-dodecene and the aqueous catalyst solution and thus
high reaction rates (see Fig. 3.6). Reaction products need to be ef ficiently separated from
the reaction mixture at lo w energy costs via phase separation (oil phase purity ≥ 95 %).
■ Catalyst r ecov ery:
The v aluable Rh catalyst and ligand are to be ef ficiently recycled
within the plant after phase separation (catalyst leaching into oil phase ≤ 1 ppm).
■ Thr oughput:
Settler design and residence time are to be specified to enable the processing
of 1-dodecene feedrates of up to 760 g/h
■ Operability:
Stable phase separation is to be achie ved also in the presence of disturbances.
Concentration shifts and accumulations should be a voided (influence on reaction)
Limitations of Mini-Plant System
Phase separation enhancement by applying e xternal magnetic or electric fields is not possible due
to technical restrictions in the mini-plant. Online sensors of the plant encompass measurements
of temperature, flo w , pressure, and le vel in their respecti ve ranges (Sec. 4.2). Of fline analytics for
liquid phase sampling is a vailable via
G
as
C
hromatography (
GC
), but limited to oily reactants.
Comp onent System
The reg arded substances are mainly dictated by the reaction system and the giv en optimal
formulation (Sec. 3.2). F or the analysis of the phase separation behavior of the
MES
, also the
formation of tridecanal and rele vant byproducts has to be considered. F or the latter , iso-dodecene
is found to be dominant. Since its chemical structure is comparable to the terminal olefin, it
is wai ved for the further discussion. The reaction network also comprises se veral catalytic
species. These are considered as surface acti ve molecules (nonpolar catalyst precursor link ed to
polar ligand or CO). Hence, catalyst acti v ation and possible alterations are considered as factors
influencing the beha vior of the phase separation.
T o simplify the discussion, quantitativ e statements on the mixture composition are gi ven in the
common indicators for
MES
from no w on. According to Eq. (2.2)
α
hence denotes the oil to
water ratio. Therein, oil comprises all organic reaction educts and products. W ater is represented
by the aqueous catalyst solution with a fixed composition according to the optimal reaction
setpoint (98.96 wt.-% water , 6.48
· 10 − 2
wt.-% [[Rh(acac)(CO)
2 ]
], 0.98 wt.-%
S
ulfo
X
ant
P
hos
54

3.4 Systematic Analysis of Microemulsion Systems for Process Design and Operation
(
SX
)). The surfactant mass fraction is gi ven by
γ
(Eq. (2.3)). Additionally , the reaction yield
Y
is used to denote the current content of tridecanal:
Y = m T r id ecanal
m 1 − d od ecene + m T r id ecanal + m By prod uct s
= m T r id ecanal
m Oil
(3.1)
3.4.2 Step 2: Influence Identification
Rele v ant influences on the separation beha vior of the
MES
are no w identified. This is based on
theoretical considerations and literature re vie w , outlined in Sec. 2.1:
1)
Looking at Fig. 2.11, dif ferent surface acti v e catalyst species act as
trace components
and
are prone to af fect the phase separation (dynamics) due to v arying interfacial tensions or
surfactant solubility (Sec. 2.1.3). Thus, the catalyst activ ation in the presence of synthesis
gas should be in vestigated first.
2)
In general, the influence of
pr essur e
on
LLE
s is found to be negligible. Ho we ver , for a
dynamic process application its influence on the separation dynamics is to be checked.
3)
The separation dynamics depend on the distrib ution of droplet size of the stirred mixture.
Thus the ef fect of stirrer type and speed are to be analyzed.
4)
Follo wing Sec. 2.1,
Concentrations
of water , catalyst, reactants, and surfactant directly
af fect phase equilibria and separation dynamics and should be in vestig ated respecti vely .
5) T emperatur e
is one of the most important state v ariables for multiphase systems. Also
se veral physical properties hold a high dependenc y on temperature.
Ranges of inter est
for screening these influences are then listed in T ab . 3.4. They mostly
depend on the expected operation conditions inside the mini-plant. A
Plant setpoint
is hence
deri ved from the optimal reaction system in Fig. 3.2. V ariations thereof are considered regarding
mini-plant tests and preliminary experiments (T ab . 3.3, (Pogrzeba et al., 2016a)).
T ab. 3.4: Ranges of interest for influencing factors on phase separation beha vior .
Influencing factor Plant setpoint Range of inter est
Catalyst acti vation activ ated inacti ve / acti vated
Pressure 15 bar [1,18] bar
Stirrer speed 1200 rpm [800,1400] rpm
α 50 % [40,60] %
γ 8 % [6,10] %
Y 40 % [0,40] %
T - [25,95] ◦ C
55

3 Systematic Analysis of Reacti ve Multiphase Systems
3.4.3 Step 3: Prescreening of the System
The prescreening is performed with particular attention on the feasibility of the observed separa-
tion beha vior regarding the process requirements. Information on used experimental setups and
conditions can be found in Sec. A.2.
Prescreening Results
First of all, the catalyst acti v ation is tested. Here, Fig. 3.7 re veals a complete impedance of
the phase separation, in case syngas is applied. No separation is achie ved in reasonable time
for the gi ven temperature range and a process application is not possible. The main reason for
this phenomenon is the aforementioned surface acti vity of the acti vated catalyst. T o cope with
this, a lyotropic salt – sodium sulfate – is added to the system, whose ionic strength ef ficiently
counteracts the emulsion stabilization due to the catalyst. This can be seen from experiments
sho wn in red plots, for which suf ficiently high oil le vels are reached after short settling times.
0246 8 10
T im e in m in
0
10
20
30
40
50
R el . oil ph a se l ev el in %
ac t. (sy n ga s)
in ac t. (N
2
)
ac t. (sy n ga s), s alt
in ac t. (N
2
), s al t

Fig. 3.7:
Relati ve le vel of oil phase o v er time for dif ferent catalyst activ ation states in the unmodified
system (black lines) and after addition of 1 wt.-% Na
2
SO
4
(red lines). T est conditions for
reference mixture (
α = 0 . 50
,
γ = 0 . 08
): 85
◦ C
, 3 bar for unmodified system. W ith Na
2
SO
4
addition: 78 ◦ C and 3 bar (N 2 ) and 8 bar (syngas).
Moreov er , the o v erall separation dynamics are significantly increased due to salt addition and a
steady state is reached in less than 4 minutes. For both catalyst states, the final le vel of the oil
phase is equal (see also Fig. A.6 for an e xtended screening) and hence no significant influence
of the catalyst acti v ation state on the phase equilibrium is visible.
56

3.4 Systematic Analysis of Microemulsion Systems for Process Design and Operation
The influence of pressure is then also discussed reg arding the experimental data in Fig. 3.7.
Comparing the experiments with salt addition (red plots), a slightly slower separation is no-
ticeable, while the equilibrium state remains unchanged. These impeded dynamics are mainly
caused by the pressure increase of 5 bar for the acti v ated case and cannot be not fully related
to the influence of catalyst acti vation. Regarding a process application with a projected system
pressure of 15 bar , this effect ho we ver is ne gligible and can easily be handled with an adequate
residence time in a respecti ve settler unit.
Finally , the influence of varying stirrer speeds on the phase separation dynamics and phase
equilibria has been found to be minimal (Fig. 7 left in (D. Müller et al., 2015).
At this point, a salt concentration of 2.17 wt.-% in the aqueous phase (1 wt.-% in the total
mixture with respect to
α = 0 . 5
and
γ = 0 . 08
) is defined and used for future in vestigations.
Additionally , the effect of syng as, system pressure, and stirrer speed are neglected hereafter to
simplify experimental setups.
F easibilit y analysis
Starting from the optimal formulation of the reaction mixture and the defined process require-
ments, a feasible phase state after separation is to be identified – the operation re gion for phase
separation. For this, Fig. 3.8 depicts observed separation states after 20 min of settling time
and selected analytical data on oil phase purity via GC and catalyst distrib ution measured via
I
nducti vely
C
oupled
P
lasma (
ICP
). Furthermore, settling times depending on temperature are
depicted in Fig. 3.9. W ith the supporting information on possible phase states of
MES
from
Sec. 2.1 the observ ed phase equilibria and emulsion types are identified, leading to the follo wing
conclusions:
■ Feasible oil phase v olume fractions > 15 % are only present for the 3-phase region.
■
In agreement with Fig. 2.8, applicable settling times are solely found for the three-phase
region.
■
For the transition between two-phase and three-phase re gions, the formation of dense and
highly viscous surfactant layers is observ ed alongside generally decelerated dynamics.
■
Oil phase purity (amount of oily components in oil phase) is close to 100 % in the three-
phase region, b ut slo wly decreases with temperature due to increased surfactant solubility
in the oil phase. Main impurity is the surfactant itself, while catalyst leaching into the oil
phase is minimal (see also (Pogrzeba et al., 2015)).
Hence, the three-phase region, a v oiding meta-stable peripheral zones, is identified as feasible
operation region for the process at hand, dictating also the according separation unit design.
57

3 Systematic Analysis of Reacti ve Multiphase Systems
Temperature
2
Region
Oil
phase
Dense surf.
lay er
Emulsion
phase
Water
phase
2/3
Transition
3 ph
Region
3/2
Transition
_ 2
Region
_
Phase Behavior
Φ Oil Phase < 3 % < 3 % ≤ 25 % ≤ 35 % ≤ 25 %
Applicable? no no limited fully limited
Oil phase purity - - ≥ 99.5 % ≥ 99.5 % ≥ 99 %
Rh leaching - - < 1 ppm < 1 ppm < 1 ppm

Fig. 3.8:
Observed phase beha vior of the
MES
and feasibility analysis to wards process application.
Mixture set to α = 50 %, γ = 8 %, and Y=0 %, photos taken after 20 min of settling time.
72 74 76 78 80 82 84
0
5
10
15
20
T emperatur ei n ◦ C

T ime in min

∞
3 ph
region
2
region
_
2
region

Fig. 3.9:
Phase separation time ov er temperature for the mixture
α = 50 %
-
γ = 8 %
-
Y = 0 %
.
∞
denotes very long settling times of se v eral days.
58

3.4 Systematic Analysis of Microemulsion Systems for Process Design and Operation
In addition, Fig. 3.10 depicts the e volution of the relati ve oil and w ater phase volumes depending
on temperature and settling time for the reference mixture
α = 50 %
,
γ = 8%
,
Y = 0%
. Again,
lar ge v olume fractions of more than 35 % and 20 %, for oil and w ater phase respectiv ely are
only found for an enclosed operation region. W ith increasing temperatures, a continuous shift
to wards lo wer oil phase le v els is observed, while the w ater phase lev el increases. Moreov er , the
oil phase forms faster than the w ater phase at lower temperatures, while in verse beha vior is found
at high temperatures. Looking at Sec. 2.1.3, this behavior is e xpected since surfactant solubility
changes to wards higher oil af finity , af fecting interfacial tensions and thus separation dynamics.
Hence, the operation strategy for the settler should aim for three-phase separation ideally belo w
the P hase I n version T emperature (PIT) to obtain fast de v eloping oily excess phases.
74 76 78 80 82 0
10
20
0
20
40
60
80
100
T emperatur e in ◦ C T ime in min
Rel. phase volume fraction φ in %
0
10
20
30
40
Fig. 3.10:
Experimental results on the relati ve phase v olume fraction e volution o ver temperature and
time for the mixture α = 50 % - γ = 8% - Y = 0 %. T op: oil phase, bottom: water phase.
Finally , Fig. 3.11 sho ws – Kahlweit’ s Fish diagram – for the
MES
to analyze the influence of
surfactant content, reaction yield, and temperature on the desired three-phase operation re gion.
Apparently , the surf actant concentration has a major influence, as the respecti ve temperature
interv al decreases drastically with increasing
γ
. Moreov er , the surfactant concentration should
not exceed 12 wt.-% to av oid the homogeneous
ME
phase state. The temperature-wise expanse
of the three-phase region is se verely limited and decreases from
≈ 9 ◦ C
at
γ = 6
wt.-% to
≈ 5 ◦ C
at
γ = 10
wt.-%. If tridecanal is present, a shift of the fish to wards lo wer temperatures and a
widening temperature interv al for three-phasic separation is observed. In addition, an increasing
oil to water ratio shifts the three-phase re gion to wards higher temperatures (see T ab . 3.5).
59

3 Systematic Analysis of Reacti ve Multiphase Systems
02468 1 0 1 2 1 4 1 6 1 8 2 0
6XUIDFWDQWFRQFHQWUDWLRQLQȖZW %
50
55
60
65
70
75
80
85
90
95
T emp erature in °C
Operation
region
2
2
_
3
1
Į  %  <   %
Į  %  <   %

Fig. 3.11:
Phase diagram for the mixture 1-dodecene-water -Marlipal
®
24/70. Black squares mark the
feasible operation region (3: three-phase re gion). Red dots show the shift of the operation
region at increased reaction yield. Data for
γ ≥ = 12
wt.-% generated by T obias Pogrzeba,
Department of Chemistry , T echnische Uni v ersität Berlin.
Sensitivity analysis
By no w , the initial set of influencing factors on the phase beha vior of the
MES
has been signifi-
cantly reduced. T o allo w for a reduction of the experimental ef fort for the full system screening
in
Step 5
, a sensiti vity analysis of the remaining relev ant influences is gi ven in T ab . 3.5. Therein,
respecti ve sensiti vities are gi ven re garding the three-phase re gion, which is characterized by
its temperature-wise location
T o pt
(mean upper and lo wer three-phase region boundaries) and
its extent (
T u − T l
). Based on this, respecti ve increments are proposed for further detailed in-
vestig ations. Note, that
Y
is sampled at a lar ger increment due to the high cost of tridecanal.
T ab. 3.5:
Sensiti vity of position of three-phase region (
T o pt
) and expanse (
T u − T l
) at local composition
α = 50%, γ = 8%, Y = 0 % re garding influencing factors.
Influencing factor Sensitivity on T opt Sensiti vity on T u − T l Suggested increment
α 0 . 1 ◦ C % − 1 − 0 . 2 ◦ C % − 1 10 %
γ − 4 . 4 ◦ C % − 1 − 1 . 8 ◦ C % − 1 0 . 5 %
Y − 0 . 2 ◦ C % − 1 0 . 1 ◦ C % − 1 20 %
60

3.4 Systematic Analysis of Microemulsion Systems for Process Design and Operation
3.4.4 Step 4a: Unit Design
At this point, a study on the design of the phase separation unit is gi ven. F or the
MES
, the
feasible three-phasic separation can be carried out in a standard gra vity settler with three drains,
since necessary settling times are found to be lo w . For this work, a modular settler concept,
introduced by D. Müller et al. (2015) is adapted and further e xtended. Ho wev er , a critical
re vision on the proposed application of coalescence enhancers is conducted regarding Fig. 3.12.
Continuous Operation
Start Up 12 h Recycle
Stop
Oil
phase
Emulsion
phase
Water
phase

Fig. 3.12:
T est of the settler operation using knitted wire meshs as internals. The phase separation state
is observed through a settler g auge glass. T est conditions:
α = 50 %
-
γ = 8 %
-
Y = 0 %
,
T Set t l er = 85 ◦ C, p S ett l er = 15 bar, 1 h residence time.
In preliminary mini-plant test runs, the continuous phase separation was in vestigated in a settler
module equipped with a knitted wire mesh and displacer . The experiment entailed reactor ,
settler , and rec ycling of the separated mixture back into the reactor . After initial filling of the
system, phase separation was established in the settler during Start Up . Afterwards, the recycle
pumps were acti v ated to establish a Continuous Oper ation . Here, a continuous shrinking of the
emulsion phase was encountered. In this case, the knitted mesh induced accumulation of the
surfactant rich microemulsion phase in the inlet section of the settler . T o verify this, the rec ycle
was stopped to obtain a re-equilibration. After 12 h, the volume fraction of the microemulsion
phase doubled. This indicates that accumulated surfactant w as at least partially redistrib uted in
the settler . Reg arding the process operation, these critical accumulations of surfactant are prone
to alter the separation state, dynamics, and impede process control. Hence, coalescers are not
considered for settler design.
Another operational challenge was identified in the b uild-up of surfactant rich dense layers.
Reg arding this, Fig. 3.13 sho ws the result from a mini-plant test run with continuous phase
separation. In this case, the emulsion starts to separate immediately after exiting the reactor .
This is due to the system’ s limitations of a maximum stream velocity of only
v R , Ou t = 0 . 02 m s − 1
.
The separation then occurs at an unkno wn temperature well belo w reactor temperature due to
61

3 Systematic Analysis of Reacti ve Multiphase Systems
heat loss at the pipe connecting reactor and settler . Considering phase configurations of the
microemulsion and coalescence beha vior from Fig. 2.9, the build-up of dense surfactant layers
becomes plausible, when this prior separation occurs at a temperature related to the
2
or 3/
2
transition state. In both cases, larger quantities of surf actant or emulsion droplets are present
in the oil phase. If this pre-settled mixture enters the settler , it will distribute according phase
densities and lar ger quantities of surfactant are carried to the settler oil phase. Since the settler
operates at lo wer temperatures, the surfactant is then dri ven to wards the middle phase and starts
to drain out of the oil phase. In this process, accumulation of surfactant is prone to occur at the
interface oil/emulsion due to small interf acial tensions.
Surfactant
Build- Up
Restore
Temperature
Oil
phase
Emulsion
phase
Water
phase
Dense surf.
layer
Liquid
Crystals
Increase
Temperature
Continuous Operation

Fig. 3.13:
Dense surfactant layer b uild-up (left) and subsequent formation of liquid crystals (right) after
settler heat up (middle). Settler gauge glass photos, taken at test conditions:
α = 50 %
-
γ = 8 % - Y = 0 %, initial T Set t l er = 80 ◦ C, p Set t l er = 15 bar, 0.5 h residence time.
For the plant operation, this state is critical, since the backmixing of accumulated surf actant
is very hard to achie v e. One solution is to increase the settler temperature and dissolve the
surfactant back into the oil phase. Ho we ver , follo wing Fig. 3.13 this is prone to trigger the
formation of liquid crystal like structures of the surf actant. These sho w very disadv antageous
fluid properties and are critical reg arding pump operation and possible pipe blocking in the
plant’ s rec ycle. Hence, an updated design of the settler united is proposed in Sec. 4.1.3, including
additional mixing zones at the inlet of the settler and uniform heating for separation.
3.4.5 Step 4b: Analysis of Controllabilit y of Influencing F acto rs
The systematic phase separation system analysis was used to identify rele v ant influencing factors
for the phase separation beha vior . Ne xt, the adjustability of these v ariables with respect to
process application is discussed using T ab . 3.6.
62

3.4 Systematic Analysis of Microemulsion Systems for Process Design and Operation
T ab. 3.6:
Analysis of rele vant influencing f actors of the phase separation behavior , their sensiti vity on
the position of the separation operation region, measurability , and controllability .
Influencing factor Sensitivity Measurement Sample rate Contr ol element
α moderate not possible 1 - feed rate, recycle ratio
γ very high not possible 1 - feed rate, recycle ratio
Y moderate GC 1 1 h feed rate, recycle ratio,
reaction conditions
T high Pt100 0.5 s heater unit of settler
1
suf ficient accuracy of concentration measurement via of fline analytics is only possible for 1-dodecene, iso-dodecene,
dodecane, iso-tridecanal, and tridecanal (see Sec. 4.2.3)
T o directly control the phase separation state, the composition of the mixture entering the settler
needs to be kno wn. The temperature can then be adjusted accordingly . Re garding this, Illner
et al. (2018a, p. 3) state, that “besides the separation temperature, which is easily accessible
and measured in suf ficient accuracy , rele v ant influence parameters are only av ailable with a
lar ge delay (oil phase concentration measurements) or are not accessible at all (surfactant con-
centration). Especially the latter hinders the controllability of the system dramatically , since
also the influence on the phase separation is the lar gest. Thus, frequent failures of operation
are to be expected, e ven with only small perturbations of a steady state operation point. ” The
applicability of
MES
as reaction media in a continuously operated mini-plant is hence hindered
by the immeasurability of rele v ant states and lack in predicti ve thermodynamic models.
T o ov ercome this hurdle, the de velopment of a model based soft-sensor is proposed. The main
idea is to e xploit the features of the three-phase body of
MES
described in Sec. 2.1.3. W ith
constant temperature
T
and pressure
p
, the microemulsion separates into three liquid phases
(corners of the inner triangular miscibility gap in Fig. 2.6) with respecti ve concentrations
c Phase
i
.
Depending on the initial composition of the mixture, v olume fractions of phases
Φ Phase
are
observed. If the initial mixture is v aried within the range of the three-phase miscibility gap, the
respecti ve compositions of the observ ed phases
c Phase
i
remain the same, but v olume fractions
change. Hence, the initial composition of the mixture
c Feed
i
can be correlated with the observ ation
of the v olume fractions of dev eloped phases and their respecti v e concentrations c Phase
i :
c Feed
i ( T , p ) = f (︂ c Phase
i ( T , p ) , Φ Phase ( T , p ) )︂ (3.2)
Hence,
Φ Phase
serve as ne w measurements, which must be considered for mini-plant automation
(e.g. camera system) and the follo wing full system mapping. From this, an empirical model
based on experimental data is deri ved, for which a detailed discussion is gi ven in Sec. 4.3.6.
63

3 Systematic Analysis of Reacti ve Multiphase Systems
3.4.6 Step 5: F ull System Mapping
Finally , the full system mapping includes the actual empirical in v estigation of the phase sepa-
ration beha vior and collection of data, which is used for model formulation later on. W ith the
performed influence and sensiti vity analysis, the relev ant factors and necessary le v els for a full
factorial design ha ve been decided to a 3
1
9
1
3
1
design for the respecti ve e xperimental parameters
oil:water ratio
α
, surfactant concentration
γ
, and tridecanal content
Y
. T able 3.7 additionally
sho ws the applied experimental ranges and increments, as well as desired experimental obser -
v ations. A detailed description of the e xperimental setup and procedure can found in Sec. A.2.
Additionally , T ab . A.1 pro vides a list of conducted experiments, actual concentration information
and observed upper and lo wer temperature limits for the three-phase body .
T ab. 3.7: Experimental ranges of parameters for the full system mapping and gathered observ ations.
Parameter Range Incr ement Measur ement
α [40,60] % 10 % gra vimetric
γ [6,10] % 0.5 % gra vimetric
Y [0,40] % 20 % gra vimetric
T v ariable 1-2 K temperature sensor
Observ ation Measur ement
Phase State Qualitati ve optical e valuation
Φ Phases V ideo capture and le vel detection
3.5 Summa ry on System Analysis – Identified Challenges
The systematic analysis for the hydroformylation of 1-dodecene in microemulsions re vealed
se veral obstacles re garding applicability and operability to wards a desired continuously operated
process. This is due to the system inherent properties and dependencies regarding reaction and
separability of the ME. Hence, the follo wing design and operational challenges are stated:
■ Reaction performance depending on mixing state and sensiti ve catalyst equilibria
■ Byproduct formation due to long residence times in separation and recycling steps
■ Small feasible operation region re garding phase separation
■
High sensiti vity of operation region position re garding virtually all system concentrations
■ Critical immeasurability of rele v ant component concentrations
■ Se verely changing fluid properties due to concentration or state v ariable changes
64

4 Derivation of Strategies fo r Pro cess
Design & Op eration
Follo wing the systematic system analysis in Chap. 3 it is obvious, that the transfer of the process
concept presented in Fig. 1.2 to wards a continuously operated process is se verely hindered by
system inherent challenges. Hence, also common procedures for realizing reactor -separator sys-
tems with internal rec ycles, as presented by L. T . Biegler et al. (1997) or R. Smith (2005, p. 18f f )
are not applicable. T o ne vertheless e xploit the highly advantageous features of
MES
applied on
the hydroformylation of 1-dodecene, the de velopment of tailored strate gies for specific process
design and adv anced model-based methods for process operation and control is necessary . As
an introduction to that, Fig. 4.1 provides a schematic o vervie w .
▪ 3-phase settler
▪ Mixing sectio n
▪ Heating zones
▪ Gas purge
▪ Stirrer design
▪ Sampling
system
▪ Recycle
configurati on
▪ Sensor placing
Identify Plant State
▪ Soft-senso r
▪ Phase separation model
▪ Multi-rate stat e est.
▪ Advanced analytics
▪ KPI for phase state
▪ Provide optimal
trajectories
▪ Implement D-RTO loop
Reaction
Performance
Phase
Separation
▪ Mixing and gassing
▪ Byproduct
formation
▪ High dynamics
▪ No steady -state
▪ Small op region
▪ Liquid cry stals
▪ Accumulations
▪ Measurability
surfactant/water
▪ Track phase state
Stabilize and
Control Operation
Reactor
Design
Settler
Design
Peripherals
Design
Solution Strategies for Process Design and Operation
System Challenges

Fig. 4.1: Overvie w of applied strate gies to cope with system inherent challenges.
65

4 De velopment of Strate gies for Process Design & Operation
Outlines on de veloped solution approaches are then presented in this chapter . First of all, the
technical realization of the mini-plant system, its subsequent redesign and equipment modifica-
tion with special focus on reactor and settler unit is sho wn. The connection of process design
and operation is then gi ven by deliberations on process automation, additionally implemented
analytics, and rele v ant communication structures.
One of the ke y features of this thesis is then presented with the dev eloped dynamic mini-plant
model. Highlights therein are an adapted kinetic model formulation for the hydroformylation in
microemulsions and a profound empiric model formulation for the three-phase separation of the
microemulsions system. Both of these model features are crucial to tackle the aforementioned
challenges in a quantitati ve manner and enable a profound first time description of the dynamic
process beha vior . This then forms the cornerstone for subsequent de velopment of ke y (optimal)
process operation strategies to cope with t he very high dynamics and non-steady state beha vior
of the system.
4.1 Mini-Plant Setup and Redesign
The initial mini-plant system design was set up and realized by M. Müller et al. (2013). How-
e ver , first tests on the hydroformylation of 1-dodecene in
MES
confirmed a v ariety of already
identified challenges like massi v e byproduct formation, impeded phase separation and surfactant
accumulations (Sec. 5.1, (Illner et al., 2016b)). T o tackle these, se veral modifications ha ve been
implemented during the course of this thesis reg arding the reaction section, recycle, and settler
design. T o allo w for an insight into the technical realization of mini-plant system, firstly the
general design is introduced.
4.1.1 General Rema rks on Design Sp ecifications
The mini-plant itself is installed in a three storeys high housing and set up in a modular w ay to
allo w for easy reconfiguration and integration of ne w equipment. According the chemical matrix,
tanks and pipes are made of stainless steel of type EN 1.4404 or EN 1.4571. PTFE, FFKM, or
FKM are used as sealing compounds. The total plant volume of the main reactor , settler , and
recycle section is gi v en with 2.35 L, while the total liquid volume is 1.45 L. A detailed list of
container dimensions and the determined v olume of the piping system is gi ven in the appendix
in T ab . B.2. A simplified process flo w diagram of the mini-plant is depicted in Fig. 4.2 for which
the subsequent naming of units and sensors applies.
66

4.1 Mini-Plant Design
Alkene Alkene
Catalyst Catalyst
Surfactant Surfactant M S IRC
150 ba rg
M
SIRC
100 ba rg
FI
H 2
PC
PC
PC
PC
CO
FFC
PC
CO
&
H 2
FI
PC
TIRC
PIC
TIRC PI
FI C
FI C
FI C
LIA-
TI
LIA-
LIA-
Exhaust Exhaust
PI
B01 Feed tank: alkene
B02 Feed tank: catalys t sol.
B03 Feed tank: surfactant
B04 Product tank
C01 Rea c tor
P01/02/03 Feed pum ps
P05/07/06 Recycle pump
water / mix / oil
QI02/03 Raman probe
QI01 Micro GC sampl ing
VS1/2/3/4 Sampling valve
W01/02 Thermostat
X01 Settler with gauge glass
X04 Oil phase buffer ta nk
Feed Section Product Section High Pressure S ection
P05
X01
C01
W01
W02
B03
B02
B01
VS4
VS2 VS3
FI C
P07
FI C
P06
QI
QI
VS1
QI
D
C
B
A
4 3 2 1
D
C
B
A
4 3 2 1
D
C
B
A
4 3 2 1
D
C
B
A
4 3 2 1
T e c h n i s c h e U n i v e r s i t ä t B e r l i n
P r o c e s s D y n a m i c s a n d
O p e r a t i o n s G r o u p
D a t e : 0 1 . 0 3 . 2 0 1 9
S i m p l i f i e d F l o w s h e e t
M i n i - P l a n t f o r H o m o g e n e o u s C a t a l y s i s
i n L i q u i d M u l t i p h a s e S y s t e m s
R e sp o n si b i l i t y
M . I l l n e r E n t i r e P l a n t
S h e e t V e r s i o n 6 . 6
Te c hnis c he U ni ver s itä t Be r lin
Proce ss Dy n ami cs and
Op eration s G r oup
Date: 01.03.2 019
Si mplifi ed Flo w sh eet
Mi ni-Pl ant for H omogeneous Cataly s is
in L iqu id Multi phase S y stems
Responsibility
M. Illne r En tire Pl ant
Sheet Versi on 6.6
X04
B04
TI
FI C
FI
03
09
02
01
01
04
03
02
06
09
05
08
02
18 02
17
19
03
01
01
06
02
01
12
11
P03
P01
P02

Fig. 4.2: Simplified process flo w diagram of the mini-plant.
The setup is di vided into three main sections, the
feed section
, a
high pr essur e section
holding
reactor , settler , and the recycle section, as well as the
pr oduct section
. Due to the presence of
synthesis gas and flammable substances, the mini-plant fully complies to A TEX zone 2 spec-
ifications. Hence, all equipment used inside the plant complies with respectiv e specifications
and temperature classes for 1-dodecene and synthesis gas. The mini-plant’ s housing is equipped
with a ventilation system to a v oid accumulation of synthesis gas in the room. The entire internal
plant v olume can be fully inertized with nitrogen. This is necessary since the catalyst decom-
poses under contact with oxygen and the formation of explosi v e mixtures of gas is to be av oided.
Additionally , a v acuum pump is installed for pur ging gas from the system.
F eed Section
The basement area of the plant holds the feed section with containers for 1-dodecene (
B01
),
catalyst solution (
B02
), and surfactant (
B03
).
B03
is stirred and heated to ensure homogenization
and reduced viscosity of the technical grade surfactant. The respectiv e pumps
P01
to
P03
feed
the substances into the reactor . Additionally , gas containers for either syngas or pure gasses CO
and H 2 are supplied and fed to the reactor via pressure regulation and ratio control.
67

4 De velopment of Strate gies for Process Design & Operation
High Pressure Section
The high pressure section of the plant is sketched and depicted in Fig. 4.3. The hydroformylation
reaction is carried out in reactor
C01
, which is equipped with a heating jacket and a gassing stirrer
with a maximum speed of 2000 rpm. A liquid drain is installed at approx. 40 % of the reactor
height. The do wnstream settler
X01
is designed as a horizontal cylinder and equipped with a
heating jacket to allo w for temperature adjustment. For rec ycling of respecti ve microemulsion
phases, three indi vidual recycle streams are installed and operated by pump
P05
for the aqueous
phase,
P06
for the oil phase, and
P07
for the emulsion phase. Additionally , a buf fer tank
X04
for the recycl e of the oil phase is installed. Both, reactor and settler are equipped with
thermostats
W01, W02
for temperature control. In general, the high pressure section is designed
for pressures of up to 32 bar gauge and temperatures up to 120
◦ C
, while for the reactor pressures
of 150 bar and temperatures of 250 ◦ C are permissible.
Al kene Al kene
Ca ta ly st Ca ta ly st
Su rf act an t Su rf act an t M S IRC

15 0 ba rg
M
SI RC
10 0 ba rg

FI
H 2
PC
PC
PC
PC
CO
FFC
PC
CO
&
H 2
FI
PC

TIRC
PI C
TIRC PI
FI C

FI C
FI C
LI A-
TI
LI A-
LI A-
E xhaus t E xhaus t
PI
B01 F eed ta nk: al ke ne
B02 F eed ta nk: ca ta ly st so l.
B03 F eed ta nk: s urfa c tan t
B04 P roduc t ta nk
C 01 Re a cto r
P 01/ 02/ 03 F eed pum ps
P 05/ 07/ 06 Re cy cle pum p
wa te r / mi x / oi l
QI 02/ 03 Ra ma n probe
QI 01 Mi c ro GC sa mp li ng
VS 1/ 2/ 3/ 4 S amp li ng va lv e
W01/02 Th er mo st at
X0 1 S ett ler wi th ga uge gl as s
X 04 O il pha se buffe r ta nk
Fe ed Se ct io n Pr od uc t Se ct io n Hi gh P re ssu re S ect io n

P0 5
X0 1
C0 1
W0 1
W0 2

B0 3
B0 2
B0 1

VS 4
VS 2 VS 3
FI C
P0 7
FI C
P0 6
QI
QI
VS 1
QI

D
C
B
A
4321
D
C
B
A
4321
D
C
B
A
4321
D
C
B
A
4321
Te c hni sc he Un iv er si tät Be rl in
Pr oc es s Dy na mi cs and
O per ati ons Gr oup
Da te : 01.03.2019
Si mp li fi ed Fl ow s heet
Mi ni -P l ant fo r Ho m ogeneous Cata ly si s
in Li qu id Mu lt i phas e Sy st em s
Re sp on si bi li ty
M. Il ln er En ti re Pl ant
Sh ee t Ve rs i on 6. 6
Te c hni sc he Un iv er si tät Be rl in
Pr oc es s Dy na mi cs and
O per ati ons Gr oup
Da te : 01.03.2019
Si mp li fi ed Fl ow s heet
Mi ni -P l ant fo r Ho m ogeneous Cata ly si s
in Li qu id Mu lt i phas e Sy st em s
Re sp on si bi li ty
M. Il ln er En ti re Pl ant
Sh ee t Ve rs i on 6. 6

X0 4

B0 4

TI

FI C

FI

03
09
02
01
01
04
03
02

06
09
05
08
02
18 02
17

19

03
01
01
06
02

01
12
11
P0 3
P0 1
P0 2

Al kene Al kene
Ca ta ly st Ca ta ly st
Su rf act an t Su rf act an t M S IRC
15 0 ba rg
M
SI RC
10 0 ba rg
FI
H 2
PC
PC
PC
PC
CO
FFC
PC
CO
&
H 2
FI
PC
TIRC
PI C
TIRC PI
FI C
FI C
FI C
LI A-
TI
LI A-
LI A-
E xhaus t E xhaus t
PI
B01 F eed ta nk: al ke ne
B02 F eed ta nk: ca ta ly st so l.
B03 F eed ta nk: s urfa c tan t
B04 P roduc t ta nk
C 01 Re a cto r
P 01/ 02/ 03 F eed pum ps
P 05/ 07/ 06 Re cy cle pum p
wa te r / mi x / oi l
QI 02/ 03 Ra ma n probe
QI 01 Mi c ro GC sa mp li ng
VS 1/ 2/ 3/ 4 S amp li ng va lv e
W01/02 Th er mo st at
X0 1 S ett ler wi th ga uge gl as s
X 04 O il pha se buffe r ta nk
Fe ed Se ct io n Pr od uc t Se ct io n

Hi gh P re ssu re S ect io n

P0 5
X0 1
C0 1
W0 1
W0 2
B0 3
B0 2
B0 1
VS 4
VS 2 VS 3
FI C
P0 7
FI C
P0 6
QI
QI
VS 1
QI
D
C
B
A
4321
D
C
B
A
4321
D
C
B
A
4321
D
C
B
A
4321
Te c hni sc he Un iv er si tät Be rl in
Pr oc es s Dy na mi cs and
O per ati ons Gr oup
Da te : 01.03.2019
Si mp li fi ed Fl ow s heet
Mi ni -P l ant fo r Ho m ogeneous Cata ly si s
in Li qu id Mu lt i phas e Sy st em s
Re sp on si bi li ty
M. Il ln er En ti re Pl ant
Sh ee t Ve rs i on 6. 6
Te c hni sc he Un iv er si tät Be rl in
Pr oc es s Dy na mi cs and
O per ati ons Gr oup
Da te : 01.03.2019
Si mp li fi ed Fl ow s heet
Mi ni -P l ant fo r Ho m ogeneous Cata ly si s
in Li qu id Mu lt i phas e Sy st em s
Re sp on si bi li ty
M. Il ln er En ti re Pl ant
Sh ee t Ve rs i on 6. 6
X0 4
B0 4
TI
FI C
FI
03
09
02
01
01
04
03
02
06
09
05
08
02
18 02
17
19
03
01
01
06
02
01
12
11
P0 3
P0 1
P0 2

Reactor
Settler
Recycles
X04
W ebcam

Fig. 4.3: Simplified process flo w diagram and picture of the mini-plant’ s high pressure section.
68

4.1 Mini-Plant Design
Pro duct Section
The product drain is realized with a control v alve connected with the le vel re gulation of buf fer
tank
X04
. Thus, a continuous operation of the plant can be established with a certain feed rate
of alkene and respecti ve drain to wards product tank B04 . Therein, also the degassing of syngas
is conducted at a reduced internal pressure of 2 bar . All vented g asses from the plant are led into
a collection tank B06 for separation of condensables and then vented to the atmosphere.
4.1.2 Redesign of Reaction Section
In Section 3.3 se veral influences on the reaction performance were identified. These are mainly
related to the catalytic system and formed equilibria of catalytically acti ve species, which partly
depend on the concentrations of CO and H
2
in the liquid phase. Regarding this, the designated
reaction performance can be ensured firstly by proper gassing and thus dissolution of syngas.
Secondly , shifts of the gas phase concentrations are to be av oided to maintain the operational
setpoint of an equimolar mixture of CO and H 2 . Despite the excellent emulsification properties
of
MES
and identified minor influence of stirrer speed on the reaction performance, the stirrer
configuration is re vised. Due to a v ariety of sensors and internal piping the stirrer operation
in the used 1.5 L autocla ve is se verely impeded and the formation of dead zones, especially of
separated oil, were identified in preliminary plant experiments.
Reactor modifications thus include the insertion of two Rushton turbines, one located at the
lo wer end of the shaft and a second roughly 5 mm belo w the total liquid le vel of the reactor .
Additionally , baffles are positioned close to the stirrers to a v oid v ortices. This way suf ficient
mixing of the
ME
can be ensured. Additionally , the gassing with syngas and distrib ution of gas
b ubbles in the mixture is improv ed. T o ensure a specified syngas composition in the reactor , an
additional continuous gas pur ge has been implemented. This is augmented with a micro-GC to
constantly monitor the composition of the gas phase and adjust the gas pur ge. A respectiv e P&ID
is found in Fig. B.8. Moreov er , the reactor is equipped with a semi-automated sampling system.
The sampling from the reactor is crucial to determine the plant’ s operational state. Using a pipe
connection for sampling, dead volume needs to be pur ged for sampling of liquids. Ho wev er ,
since phase separation also occurs in the sampling pipe, it is virtually impossible to purge dead
v olume and afterwards draw a homogeneous sample from the reactor manually . In contrast, the
designed sampling system enables the simultaneous pur ge of dead volume from the sampling
pipe connection and loading of a homogeneous sample into a sample loop using a tailored flo w
pattern and 6-way cross flo w v alve. Pipe dimension are optimized to withdraw minimum liquid
content per sample. A dead volume of 9 mL and a 0.2 mL sample loop can thus be realized. The
designed implementation and remarks on its usage are gi ven in Sec. B.1.
69

4 De velopment of Strate gies for Process Design & Operation
4.1.3 Redesign of Settler and Recycle
First deliberations for designing a settler unit for the application in multiphase media were
initially proposed by D. Müller et al. (2015). A ke y feature is the modular design, which enables
the fast adaption of the internal v olume, application of internals, or additional sensors. Howe v er ,
concerning the unit requirements gained from the system and operability analysis of the reaction
and microemulsion system an adequate design is proposed:
Especially the lar ge unit v olume and corresponding residence times of the
ME
in the settler is
prone to trigger byproduct formation due to an altered catalyst state, as described in Sec. 3.3.
Thus, the settler design is initially set to the configuration sho wn in Fig. 4.4. Minimizing the
internal v olume, 0.45 L total liquid v olume and an inner diameter of 55 mm are chosen. Three
phase drains are applied to allo w for the recycling of respecti v e microemulsion phases and
a central gauge glass is installed to enable observ ation of the phase separation. T emperature
control is guaranteed by a Pt100 sensor and an implemented heating jacket, fed by a thermostat.
F e e d
W a t e r
E m u l s i o n
O i l
G a u g e g l a s s

Fig. 4.4: Modular settler configuration with two end caps and a central gauge glass.
In Fig. 3.4.4 it is also discussed, that internals like knitted wire meshs are prone to trigger
surfactant accumulation during continuous operation. Consequently these are excluded from the
settler design. Additionally , it is sho wn, that initial phase separation of the microemulsion before
entering the settler se verely hinders operation stability due to undefined separation states and
possible formation of dense surfactant layers or liquid crystals. The connecting pipe between
reactor and settler is thus equipped with a heating jacket, positioned closely to the settler inlet.
This setup is also depicted in Fig. 4.2 and includes an additional temperature sensor at the settler
inlet to enable observ ation or control. The heating jacket is supplied with thermo-oil from the
settler’ s thermostat in an interconnected loop. Initially , co-current flo w of the thermo-oil is
preferred to ensure suf ficient pre-heater operation and av oid increased surfactant concentrations
in the oil phase at the settler outlet.
70

4.1 Mini-Plant Design
Realization of a Mixer-Settler Unit
Maintaining a stable separation operation also in volv es adjusting the separation conditions for
the
MES
according a small operation region, which is also sensitiv e to concentration shifts.
The description of the separation beha vior and the empirical model formulation in Sec. 4.3.6 is
performed and virtually only possible for the separation process starting from the fully emulsified
system. The ideal design of the separation unit then must allo w for the immediate separation of
the mixed emulsion at the optimal separation temperature.
T o realize this, the inte gration of a mixing zone and an optimized heating zone into the settler
is proposed. Here, Fig. 4.5 depicts a second de veloped mix er -settler design. A stirrer module is
installed to re-emulsify and preheat the entering mixture to the optimal separation temperature.
Again a Rushton turbine is implemented as a stirrer . Baf fles are omitted due to the ov erall small
v olume of the mixing compartment. V ia an ov erflo w weir , the mixture then enters the heating
module, which is equipped with a heating jacket and an internal heating coil to a v oid radial
temperature gradients. In this module, the separation of the emulsion takes place, whose current
state is then observed in a subsequent g auge glass. Three phase drains allo w for recycling of the
indi vidual phases. The ov erall v olume of the separation zone is approx. 650 mL with additional
400 mL mix ed zone. Thus, a residence time well belo w 1 h for the separated system can be
ensured to a void the formation of inselecti ve catalyst species, as discussed earlier . Additionally ,
both mixing and separation compartment are indi vidually equipped with a gas inlet connected
to the reactor gas phase, enabling pressure equilibration and gas re-feed.
Feed
Phase Drain
Module
W ater
Emulsion
Oil
Heating
Module
Stirrer
Module
Gauge glass

Fig. 4.5:
Ne w settler design with mixing section, separation section with internal heating coil and up-
dated phase drains. A gauge glass is installed for phase separation observ ation.
71

4 De velopment of Strate gies for Process Design & Operation
Recycle Redesign
All three phases of the microemulsion must be rec ycled indi vidually to be able to adjust their
ratio of recycling to the current phase separation state. Hence, concentration shifts in reactor and
settler can be a voided. The dimensioning of respecti ve pumps is done according to the e xpected
fractions of phase v olumes from the systematic analysis of the phase behavior . The piping is
then equipped with additional heating elements to a void blockage and to reduce the viscosity of
the emulsion phase. Since the recycled phases sho w dif ferent fluid properties, such as densities
(754.67 g/L for 1-dodecene and 993.79 g/L for water at 25
◦ C
(AspenProperties V10, 2019)) and
viscosities (0.8 mPas for 1-dodecene, 13.7 mPas for emulsion phase, both at 60
◦ C
) check v alves
are supplied for each recycle line close to the rec ycle mixer to a v oid backflo ws and backlog.
4.2 Pro cess Automation and Analytics
For the de velopment of model-based strategies for process operation and control, information
on a vailable sensors, actuators, and analytically accessible information from the plant is vital.
Hence, this section presents the supplied process automation system, positioning and properties
of implemented sensors and actuators, and the v ariety of tested and applied analytics to improv e
the a vailability of vital concentration information. Furthermore, special attention is gi ven to the
communication structure of the plant and bi-directional data handling between the automation
system and external softw are frame works for state estimation and dynamic optimization.
For later presentation of results and plant measurements, information on sensor accuracy or
measurement v ariances from this chapter are used to calculate measurement errors. These are
generally presented as standard de viation
σ
. Further information on handling and calculation of
measurement errors can be found in Sec. B.3.
4.2.1 Pro cess Monito ring and Control
For reliable and safe process operation, the mini-plant is fully automated with the industrial-
grade process automation controller system
Siemens SIMA TIC S7-400
and respecti ve process
control software
Siemens SIMA TIC PCS 7 V7.1
. Used measurements and actuators for pro-
cess monitoring and control actions therein are listed in T ab . 4.1 and T ab . 4.2 together with
information of applicable v alue ranges, control action, and measurement errors. The follo wing
discussion focuses on de vices relev ant for the mini-plant model functionality and related state
estimation.
72

4.2 Process Automation and Analytics
T ab. 4.1:
Installed sensor de vices in the mini-plant. Information is gi ven on measurement range, in-
tended use, and measurement error .
T ag Measur ement Range Application Err or
FI01 Mass flo w 8 . . . 400 g h − 1 Syngas feed 4.5 % 1,3
FICA02 Mass flo w 80 . . . 4000 g h − 1 Alkene feed 4.7 % 1,3
FICA03 Mass flo w 10 . . . 1000 g h − 1 Catalyst feed 4.7 % 1,3
FICA04 Mass flo w 20 . . . 200 g h − 1 Surfactant feed 4.7 % 1,3
FICA05 Mass flo w 80 . . . 4000 g h − 1 Emulsion recycle 4.7 % 1,3
FI06 Mass flo w 60 . . . 6000 g h − 1 T otal recycle 4.7 % 1,3
FI07 Mass flo w 40 . . . 4000 g h − 1 Product stream 4.7 % 1,3
FICA08 Mass flo w 10 . . . 1000 g h − 1 W ater recycle 4.7 % 1,3
FICA09 Mass flo w 40 . . . 4000 g h − 1 Oil recycle 4.7 % 1,3
LIA01 Le vel 0 . . . 100 % Alkene feed tank 0.5 - 13.4 % 2,4,5
LIA02 Le vel 0 . . . 100 % Catalyst feed tank 0.5 - 8.9 % 2,4,5
LIA02 Le vel 0 . . . 100 % Surfactant feed tank 0.5 - 13.4 % 2,4,5
LICA06 Le vel 0 . . . 100 % Oil phase b uf fer tank 0.7 - 20.0 % 2,4,5
LIA+09 Lev el 0 . . . 100 % Product tank 4.5 % 2,5
PICA06/18 Pressure 0 . . . 25 bar Reactor - Settler 0.5 % 2,6
TIRC01 T emperature -50. . . 250 ◦ C Reactor 0.5 % 7,8
TIRC02 T emperature -50. . . 250 ◦ C Settler 0.5 % 7,8
TIRC03 T emperature -50. . . 250 ◦ C Settler inlet 0.5 % 7,8
TI04 T emperature -50. . . 250 ◦ C W ater rec ycle 0.5 % 7,8
TI05 T emperature -50. . . 250 ◦ C Product stream 0.5 % 7,8
TI06 T emperature -50. . . 250 ◦ C Catalyst feed 0.5 % 7,8
TI07 T emperature -50. . . 250 ◦ C Alkene feed 0.5 % 7,8
TI08 T emperature -50. . . 250 ◦ C Surfactant feed 0.5 % 7,8
TI13 T emperature -50. . . 250 ◦ C Oil rec ycle 0.5 % 7,8
TI14 T emperature -50. . . 250 ◦ C Emulsion rec ycle 0.5 % 7,8
1 Of actual v alue plus zero-point stability of 6 g h − 1 , expect for FI01 and FICA02 (0.2 g h − 1 )
2 Error related to total range
3 Source: Calibration data and information from contacted manufacturer (Bronkhorst)
4 Error increases gradually at the end points of the measurement range
5 Source: (Endress+Hauser, 2011a; Endress+Hauser, 2011b; E.L.B., 2011)
6 Source: (WIKA, 2009)
7 Measurement error with respect to actual v alue. Additional of fset of 0.3 ◦ C applies
8 Source: Information from contacted manufacturer (TC GmbH)
Se veral coriolis flo w sensors are installed in the plant to monitor and operate feed and recycle
streams. Their usage for direct control of the pump speeds ho wev er is disregarded for plant oper -
ations, since the application of coriolis flo w sensors for MES was found to be unreliable. Since
73

4 De velopment of Strate gies for Process Design & Operation
piston pumps are implemented, desired setpoint flo w rates are correlated with pump driv e speeds
and a kno wn displacement volume. Flo w sensor information is ho we v er used as additional
information for state estimation and data reconciliation. Le vel sensors are installed in all feed
tanks, tank X04, and the product tank and pro vide redundant measurements on respecti ve liquid
flo w rates. The pressure of the high pressure section is monitored with PICA06 and PICA18
enabling also control.
T emperature of reactor and settler are monitored with TIRC01 and TIRC02 respecti vely and
controlled using corresponding thermostats W01 and W02. Additionally , temperature measure-
ments are applied in all main process paths to control trace heating elements and enable the
calculation of fluid properties, such as component densities. Surfactant feed tank stirrer SIRC01
and reactor stirrer SIRC02 are both operated at constant speeds of 30 % and 25 % of maximum
speed respecti vely . Thus, sufficient mixing of surf actant and microemulsion, as well as proper
gassing in the reactor are ensured.
T ab. 4.2:
Installed actuators in the mini-plant. Information is gi ven on application, range, and intended
control action.
T ag Actuator Range Effect
P01 Pump 0 . . . 1100 mL h − 1 Alkene feed
P02 Pump 0 . . . 400 mL h − 1 Catalyst feed
P03 Pump 0 . . . 400 mL h − 1 Surfactant feed
P05 Pump 0 . . . 1100 mL h − 1 Emulsion recycle
P06 Pump 0 . . . 1100 mL h − 1 Oil recycle
P07 Pump 0 . . . 400 mL h − 1 W ater recycle
R V1 Control v alve 0 . . . 100 % Le vel X04
R V6 Control v alve 0 . . . 100 % Pressure reactor
W01 Thermostat 20 . . . 120 ◦ C T emperature reactor
W02 Thermostat 20 . . . 120 ◦ C T emperature settler
The automation concept and visualization of the entire mini-plant is realized in Siemens SIMA TIC
PCS 7. Hence, a base le v el automation has been realized including alarm logging, notifications,
as well as sequential routines for safe shut-do wn in case of se vere system failure or critical
alarms. Due to safety restrictions, this base automation is not af fected by any applied optimal
process control method and all control loops adhere to specified limitations of process v alues.
The graphical user interface of the automation system is e xemplarily sho wn in Fig. 4.6. Here,
the main high pressure section is depicted, where control actions regarding reaction pressure and
temperature, settler temperature, or recycle ratio can be made. Additionally , a webcam feed of
the phase separation state including le vel detection and concentration soft-sensor is pro vided. In
an upper task bar other visualization frames can be selected to operate dif ferent parts of the plant,
such as the feed section, plant inertization, permission handling, or general status observ ation.
74

4.2 Process Automation and Analytics
Fig. 4.6:
V isualization of the process automation of the mini-plant. Main control frame for the high
pressure section is sho wn.
4.2.2 Automated Phase Level Detection
From the systematic system analysis, the observation of the de v eloped fractions of the phase
v olumes in the settler was identified as additional mandatory measurement to enable the ap-
plication of a soft-sensor for the surfactant concentration. For this purpose, an A TEX zone 2
specified high definition webcam is installed. It is adjusted on the settler gauge glass and an
opaque shielding is applied together with a spot light to guarantee defined lighting conditions.
For image e valuation and calculations, an o wn implementation in C++/Python is used (Alzate,
2018). In general, the dif ferent coloring and opacity of existing phases is e xploited. The program
sequence is depicted in Fig. 4.7 and starts with reading a frame from the webcam and cropping
the image to the region of interest. It is smoothened and filtered with a Gaussian filter and
then transformed into
H
ue
S
aturation
V
alue (
HSV
) color space.
HSV
is especially suitable
here, since it is device independent and widely robust re garding changing lighting conditions
(Ibraheem et al., 2012). The color detection is then sequentially performed on the image for the
identification of all rele v ant phases. Necessary
HSV
color thresholds are provided from initial
calibration. Additionally , a user interface has been programmed for on-the-fly re-calibration
75

4 De velopment of Strate gies for Process Design & Operation
in case the optical properties of the
MES
change. In the subsequent le vel detection step, the
extent of the identified areas is then determined using cann y edge detection on horizontal lines
(Canny, 1986). Finally , detected lev el fractions are con verted into v olume fractions using the
geometric information of the settler . The accuracy of this approach is generally considered to
be high, as has been seen within first tests. Ho wev er , a conserv ativ e assumption of a maximum
measurement error of 5 % of the current v alue is assumed.
Read RGB image
from webcam
Start
Crop to ROI
Convert to HSV
scale
Color detection
Phase =
Water?
All
phases
found?
Detect ROI (Canny)
Detect water level
(Canny)
Detect water level
(Canny)
Volume fraction
calculation
yes
yes
no
no
Done
Oil
Surf. layer
Emulsion
W ater

Fig. 4.7: Image processing script for detection of phase v olume fractions and exemplary results.
4.2.3 Implemented and Applied Analytics
Due to the comple xity of the chemical matrix of the
MES
, a multitude of analytical methods has
been tested and further de veloped to enable tracking of reaction performance and distrib ution co-
ef ficients for the phase separation. In the follo wing, respecti v e analysis methods and procedures
76

4.2 Process Automation and Analytics
for sample preparation are explained. Additionally , T ab . 4.3 pro vides a correlation matrix on
type and online/of fline applicability of used methods and the measurability of substances therein.
This first of all states the poor or impossible measurability of the surfactant and w ater .
T ab. 4.3:
Applied analytics and detectable substances. + good measurability; -no quantification possible.
1 Raman spectroscopy only applicable at constant optical properties of the mixture.
` ` ` ` ` ` ` ` ` ` ` `
Component
Method of fline online
GC liquids ICP-OES Raman 1 GC gasses
1-dodecene + - +
iso-dodecene + - +
T ridecanal + - +
iso-tridecanal + - +
Dodecane + - -
Marlipal ® 24/70 - - -
W ater - - -
Rhodium catalyst - + -
SulfoXantPhos - + -
CO, H 2 , N 2 , O 2 +
Gas Chromatography
From all tested methods, gas chromatography for liquids of fers the best applicability and is thus
lar gely used. Ne vertheless its application still requires adv anced procedures:
The sample composition and especially the presence of water and surfactant ha ve a lar ge influ-
ence on the analysis results. Y et, onl y oily substances are traceable. T wo configurations are
used for liquid samples from the system: A He wlett Packard HP 6890 GC equipped with a 1 m
fused silica pre-column with 0.32 mm inner diameter and a separation column Agilent HP-5
(crosslinked 5% PH ME Siloxan, 30 m length, 0.32 mm inner diameter , 0.15
µm
) is used for
aqueous and surfactant rich samples. Oil phase samples are analyzed with an Agilent 7890A
GC without pre-column and the same HP-5 column. For both, the analysis method is described
in Sec. B.2
Sample preparation entails addition of nonane as internal standard (5 % of sample mass). Pre-
pared samples are then fully dissolved in iso-propanol (dillution f actor 6:1). A calibration has
been performed for three dif ferent application cases and expected concentration ranges: reactor ,
oil, and w ater phase sampling. Corresponding results are listed in T ab . B.3. Resulting chro-
matograms are e v aluated using an o wn implementation in Matlab . Here, the detector signal ov er
time is analyzed and existing peaks are inte grated. The assignment of components to peaks is
then done based on expected retention time interv als and specific properties of component peaks
(fronting/tailing). Afterwards, sample preparation and internal standard information is used for
77

4 De velopment of Strate gies for Process Design & Operation
correction before calculated peak areas are con verted into mass fractions. Result data is then
provided via an OPC D A interface in Matlab . This extensi ve procedure is necessary , because
retention times of component are highly dependent on the chemical matrix of the samples. Thus,
shifts are present with, e.g., increasing con version in the mini-plant.
In addition, a micro-GC is applied to sample the composition of the gas phase of the reactor
online. Information on that is found in Sec. B.2.
Raman Sp ectroscop y
Spectroscopic analytical methods of fer real-time and in-situ measurement of the composition
of a system. This is highly desired for the dynamic system and enables fast model updates.
Reg arding this, Raman spectroscopy is in v estigated and applied for mini-plant operations. T o set
up adequate calibration models for spectrum analysis, a test stand for the hydroformylation of
1-dodecene in microemulsions has been de veloped in collaboration with the Federal Institute for
Materials Research and T esting (B AM). For reference measurements, se veral analytical tools,
such as high field
N
uclear
M
agnetic
R
esonance (
NMR
),
GC
, and UV/vis hav e been implemented
(see flo w sheet in Fig. B.9). Satisfactory calibration models ha ve been de veloped for 1-dodecene,
its isomers, and tridecanal. Further information on the setup, applied multi variate data analysis,
and model de velopment can be found in our o wn contrib utions (Me yer et al., 2017a; Paul et al.,
2017).
P endant Drop Surface T ension Measurements
The analysis of surface tension is applied to determine the
cmc
of surfactant containing mixtures.
This is rele vant for the de v elopment of the phase separation model. W ithin this work, the
pendant-drop method is used, in which the surface or interfacial tension is determined via the
geometrical dimensions of a drop hanging from the exit of a capillary . The system DataPhysics
OCA 20 with automated dosing is applied. The droplet size is captured with a digital camera
and surface or interf acial tensions are calculated using the method described by (Jennings et al.,
1988).
4.2.4 Communication Structure and Data Management
The tar geted plant operation and application of model-based strate gies for process control ex-
hibits the necessary exchange of data between plant automation system, mini-plant equipment,
and optimization platforms. Regarding this, a communication structure with dif ferent communi-
cation layers has been implemented. This is shown in Fig. 4.8 together with a flo w diagram of
78

4.2 Process Automation and Analytics
transferred information and used communication protocols for process automation. The aim is
to a void manual data transfer , enable information exchange in real-time, and ensure a vailability
of necessary data at an y element in volv ed in the calculation of optimal plant trajectories. Three
main communication and data exchange c ycles are identified for the plant and discussed:
Mini-Plant – Distributed Control System
The
DCS
Siemens SIMA TIC PCS 7 is applied for mini-plant automation and thus measurement
v alues, sensor status information, and control actions are bidirectionally exchanged. Process data
is then directly accessible on the
OS
. T o ensure the av ailability of online data from the plant, an
OPC D A (https://opcfoundation.org/) server is operated, providing read and write capabilities for
process v alues, control setpoints, and sensor status information. This way , platform independent
access on plant data and redundant archi ving of process data in Matlab is possible.
Distributed Control System – Auxilia ry Equipment / Senso r
Se veral smart sensors are applied to generate additional information on the plant’ s state. In
case of applied GC analysis, concentration data is also provided within the process control
system and stored with respecti ve timestamps. For this purpose, a network connection of the
GC result analysis station with the
DCS
is established via OPC. This procedure is analogous
for the implemented image processing and surfactant soft-sensor . An external
W ebcam Server
is installed, which operates the webcam in the mini-plant via Ethernet and performs the lev el
detection described in Sec. 4.2.2. The current e valuation is then mer ged with real-time informa-
tion on the settler temperature to calculate the corresponding mixture composition. Information
on the fractions of phase v olumes and concentrations is then written back to the plant via OPC.
Additionally , current webcam frames and two
K
ey
P
erformance
I
ndicator (
KPI
) plots are pro-
vided to help plant operators to e v aluate the current settler status of operation. These images are
transferred to the OS via Ethernet and displayed in the DCS.
Distributed Control System – Optimizer
T o apply online-optimization on the mini-plant, an external
Optimizer
workstation is set up. T o
identify the current plant’ s state, data on process v ariables and controls are read via OPC from
the
OS
and applied for mo ving horizon state estimation. This comprises also soft-sensor and
analytical data. The identified plant state is then used to calculate an optimal plant trajectory for
the next 4 hours. The results are finally transferred back to the
OS
, which then applies gained
optimal trajectories of setpoints on respecti ve controllers.
79

4 De velopment of Strate gies for Process Design & Operation
Mini-plant
Plant AS
CPU & Comm
Profibus
DP
Industrial
Ethernet
Optimizer
State Estimation
& D-RTO
Engineering
Station
Operator
Station
OPC Server
Matlab
Archiver
Webcam Server
Level Detection
& Soft Sensor
Analytics
Station
Evaluate GC
Chromatograms
TCP/IP & OPC Communication
Field devices

Mini-plant
DCS
▪ Monitoring
▪ Control
▪ Archive
▪ Conc. data
▪ Levels
▪ Image
▪ KPI
Analytics
Station
Webcam Server
Optimizer
▪ State
estimation
▪ D-RTO
GC Data
▪ Plant data
▪ Archive
▪ Plant data
▪ Optimal
setpoint
▪ Conc. data
Webcam
image
▪ Sensor data
▪ Sensor state
▪ Apply
setpoints
Communication type
▪ Plant bus
comm unicatio n
▪ TCP/IP
▪ OPC data objects

Fig. 4.8:
Realized communication structure for process automation (top), information exchange and used
communication protocols between mini-plant,
DCS
, additionally smart sensors, and model-
based optimizer (bottom).
80

4.3 De velopment of the Dynamic Process Model
4.3 Development of the Dynamic Pro cess Mo del
In this section, the dynamic model of the mini-plant for hydroformylation is presented. The two
major features therein are an adapted mechanistic macro kinetic model for the hydroformylation
reaction and a semi-empiric model for the dynamic three-phasic separation of
MES
s. The
modeling itself has been carried out in the modeling en vironment MOSAICmodeling
1
. The
de veloped D AE model and a discretized version are a v ailable as pdf 2 .
4.3.1 General Structure and Scop e of Mo deling
In general, the model dev elopment focuses on model applicability for state estimation and
dynamic (online) optimization. Hence, rele vant dynamic phenomena within the mini-plant are
to be entailed, b ut keeping model complexity as as lo w as possible. Due to the high complexity
of the system and missing thermodynamic descriptions theoretically based predicti ve models
are not attainable. Thus, the goal of the modeling is to find valid representations of identified
operation regions for certain phenomena and the according dynamics for the transitions between
operation modes. This includes:
■ Overflo w drainage of tanks with outlet activ ation
■ Kinetic model including byproduct formation and reaction de/acti v ation through syngas
■ Three-phasic ME separation and respecti ve component distrib ution
■ Switching of settler outlet concentrations, according to separation state
■ Three indi vidual recycles with dead time beha vior regarding concentration changes
Follo wing the outlines in Sec. 2.4, the applied modeling techniques will focus on r educed first
principles representations of mini-plant elements comprising the MESH(I) systematics and semi-
empiric representations of rele v ant phenomena, especially the phase separation beha vior .
T o guide the discussion, Fig. 4.9 provides a modeling scheme, which holds the crucial ele-
ments of the technical mini-plant system rele v ant for modeling. Additionally , applied indices of
components i (T ab . 4.4), units u , streams s (T ab . C.4) are introduced.
In the follo wing, the focus is laid on key model functionalities and no vel implementations.
Hence, general definitions of equations, such as mass balances, sum relations, and auxiliary
equations are summarized in the appendix C.2 and are not outlined here.
1 http://mosaic-modeling.de/
2 http://mosaic-modeling.de/?attachment_id=3489, http://mosaic-modeling.de/?attachment_id=3487
81

4 De velopment of Strate gies for Process Design & Operation
Settler
11
13
1
2
3
4
5
6
F 4
F 16
18
9
8
F 8
F 6
F 5
F 12
21
22
7
17
F 19
F 15
F 7
10
F 9
F 11
Recycle Section
Feed Section
Reactor
Product Section
F 10 12
F 13
Sample
Oil
Sample
Reactor
F 1
F 2
F 3
Syngas
Oil pipe
volume
15
Em . pipe
volume
16
Water
pipe
volume
F 17
F 18
19
20
Sample
Mix
Sample
Water
F 14
14
3
2
1
22

Fig. 4.9:
Model scheme for the hydroformylation mini-plant. All relev ant and modeled units, streams,
and phases are depicted. Information on stream and unit indices can be found in T ab . C.4.
T ab. 4.4: Indexing of substances.
Component Index i Component Inde x i
1-dodecene 1 CO 7
iso-dodecene 2 N 2 8
iso-tridecanal 3 H 2 O 9
dodecane 4 Precursor: Rhacac[CO] 2 10
n-tridecanal 5 Lig and: SulfoXantPhos 11
H 2 6 Surf actant: Marlipal ® 24/70 12
Assumptions
Basic assumptions for the model formulation are presented in the follo wing. Predominantly ,
these are necessary to keep the model comple xity lo w and ensure usability for optimization.
■ Neglect momentum balances, pr essur e drop, valv es, and internals:
Only reactor pres-
sure is of rele v ance. Flo w data is widely measured.
■ Neglect energy balances and heat losses on units and pipes:
For the scope of model-
ing reactor and settler temperature are most important, which can be controlled tightly .
T emperatures of flo ws are measured at relev ant points in the mini-plant.
■ Pumps operate independent of pr essur e drop and counter pr essure:
Piston pumps are
operated well belo w max. pressure and hold linear characteristics (Bran+Luebbe, 2011).
■ Reaction only takes place in the r eactor (Fig. 3.4)
■ T anks ar e assumed as cylinders
82

4.3 De velopment of the Dynamic Process Model
■ T anks ar e ideally mixed:
Stirrer used for reactor and surfactant tank. High mutual
solubility of components in other tanks.
■ Neglect vapor -liquid-equilibria f or non gaseous substances:
Rele v ant v apor pressures
are lo w with respect to operation conditions and gas phase pur ge is v ery small with
10 g h − 1 . Hence only gas solubility is considered.
■ Gas dissolution f or gases in liquids : Instantaneous equilibrium is assumed.
Fluid Prop erties
Based on the made assumptions and simplifications for modeling of the mini-plant, only the
compound densities are stated as rele vant fluid properties. The calculation of densities is done
separately for liquids and gasses. The former are computed via DIPPR105
3
, which depends
solely on temperature and parameters
a i . . . d i
. The latter uses a polynomial expression with
parameters a i . . . e i depending on temperature and pressure:
ρ ( T ) L = a i
( b i ) 1 +( 1 − T
c i ) d i · M i (4.1)
ρ ( T , p ) V = ( a i + b i · T + c i · p + d i · ( T ) 2 + e i · T · p ) · M i (4.2)
Rele v ant parameters for each model component
i = 1 . . . 12
ha ve been fitted to e xperimental
data or database correlations. Parameter data and data sources are listed in T ab . C.22. Here,
1-dodecene and its isomers, as well as tridecanal and its isomers are each handled as a single
pseudo compound due to the lack of data. Also, all compounds of the catalyst solution are
treated as pure water . In general, mixing effects on the density are omitted for this model and
mixture densities are solely obtained from weighted pure substance densities. This assumption
has been tested to satisfactory results with test mixtures of the microemulsion system.
Mo deling of Dynamics, Reliable Switchovers, and Controller Equations
T o capture the dynamics of the process for , e.g., the start-up procedure and discrete switching
between the phase separation states, se veral switching functionalities need to be implemented.
Reg arding this, twice differentiable sigmoidal functions are introduced in Sec. 2.4.2 and are used
as triggers for the acti v ation of model functionalities. For e xample, the reactor outlet flo w is only
acti ve, if the liquid le vel exceeds the le v el of the o v erflo w pipe inlet. The difference between
the actual le vel (
Lvl
) and the o verflo w setpoint (
Lvl SP
) is then used as ar gument for the sigmoid
to enable switching functionality . Howe v er , with outlet stream acti v ation,
Lvl
is regulated to
3 DIPPR Project 801, https://www .aiche.org/dippr/projects/801 [Access: 2019-03-04]
83

4 De velopment of Strate gies for Process Design & Operation
be at
Lvl SP
. F or this case,
Sigmoid ( L vl SP − Lvl ) ≈ 0 . 5
applies. Thus, a
0 → 1
switching of
model elements is not ensured and drawbacks such as violated component mass balances and
continuously jumping or hanging triggers occur (see standard case in Fig. 4.10).
01 02 03 04 05

06

0
Ti me in mi n
1. 5
1.55
1. 6

Le ve li nd m

standard augmen ted
01 02 03 04 05

06

0
Ti me in mi n
0
0. 5
1
Tr i gge r
standard augmen ted

Lvl SP
F out = K(Lvl – Lvl SP * - Slack)
Lvl SP*
Slack
Trigger = f(Lvl – Lvl SP * )

Fig. 4.10:
Left: comparison of the trigger functionality for the standard and augmented sigmoidal
function implementation. T rigger switching at a setpoint le vel of 1.5 dm desired. Right: le vel
setpoint and slack configuration.
T o ov ercome this hurdle, the implementation of sigmoidal functions for outlet flo w triggering is
augmented with a Sl ack v ariable:
T RI G = 1
2 +
1
2 · ( Lvl − Lvl S P ∗ )
√︁ ( Lvl − Lvl S P ∗ ) 2 + ε (4.3)
F Ou t = K P · ( Lvl − Lvl SP )
⏞ ⏟⏟ ⏞
Pr o port ional P art
+
I n t e gral P art
⏟ ⏞⏞ ⏟
K I · I (4.4)
d I
d t = ( Lvl − Lvl SP ) · T RI G (4.5)
Lvl SP = L vl SP ∗ + S l ack (4.6)
Firstly , the trigger function is defined, applying a modified setpoint lev el
Lvl SP ∗
for the ar gument.
The outlet stream definition
F Ou t
could be formulated according T orricelli’ s law or a control
equation. For model simplification, a PI-controller is proposed for
F Ou t
. This ensures the
physically dri ven increase or decrease of the outlet stream according to the current superele v ation
of the le vel and like wise a voids lasting control errors of the tank le vels (inte gral behavior). A
general definition for
F Ou t
and the necessary dif ferential equation for the dif ferential part
I
of
84

4.3 De velopment of the Dynamic Process Model
the controller is gi ven in Eq. (4.4)-4.5. The dif ferential part of the controller equation is then
augmented with the respecti ve trigger , serving as anti-windup .
W ith the augmented control deviation
( Lvl − Lvl S P ∗ − Sl ack )
of the PI-controller , the outlet
stream ensures a le vel
Lvl SP ∗ + S l ack
– the desired setpoint in the tank. Ho wev er , the ar gument
of the trigger already switches to acti ve at the lo wer le vel
Lvl SP ∗
. Figure 4.10 emphasizes this
stabilization of the switching functionality . For the augmented case, the sigmoidal function stays
at the desired v alue of 1 and in contrast to the standard case, the respectiv e tank le v el stabilizes at
the desired setpoint
L SP = 1 . 5
dm. Additionally , min-operators , already introduced in Eq. (2.4),
are applied on F Ou t to enforce strictly positi ve (and physically v alid) outlet flo ws.
This strategy is applied throughout the model and results in significantly impro ved accurac y ,
since non-physical o verfull tanks, negati ve flo ws, or wrongly truncated concentrations are
a voided.
4.3.2 F eed Section
According to the mini-plant sections, firstly the feed section is modeled follo wing Fig. 4.11.
Feed Section
1
2
3
4
5
6
7
F 1
F 2
F 3
3
2
1
F 5
F 4

Fig. 4.11: Model scheme for the feed section.
The feed tanks itself (units 1-3) are implemented to link the a vailable le v el sensor data from
the plant to the model and allo w for additional v alidation of feed streams
F s = 1 ... 3
. Additionally ,
the feed streams are mer ged together with the total rec ycle stream
F s = 4
in unit 7, the feed
mixer . Follo wing the proposed modeling systematics the component mass balances (
M
ass
B alance (MB)) are set up for feed tanks and feed mixer (Eq. (C.13)-C.14).
85

4 De velopment of Strate gies for Process Design & Operation
The total feed streams
F s = 1 ... 3
result from the feed pump operation, represented by the application
of desired setpoints
F SP
s = 1 ... 3
. As a special feature, the feed streams are modified to account for all
rele v ant operation modes of the plant: start-up/filling, full recycle, and the continuous operation.
Hence, continuous transitions are enabled. Here,
T RI G Pl an t F il l
Rec
identifies and switches on the
filling status of the plant.
F C on t i
Feed
then represents the total feed to the plant in continuous operation
mode, while
w i , Cont i
Feed
are multipliers to adjust the total feed stream composition. The latter are
specified according to the current calculated composition of the oily product stream.
F s = F S P
s · ( 1 − T RI G Pl an t F il l
Rec ) + F C on t i
Feed · w C ont i
Feed , s · T RI G Pl an t F il l
Rec , s = { 1 . . . 3 } (4.7)
4.3.3 First Principles Mo del of the Reacto r Unit
W ith the implementation of the hydroformylation kinetics, the reactor is one of the most impor-
tant model elements. The modeling is done according to Fig. 4.12 and is subdivided into the
actual unit modeling and the formulation of a suitable reaction kinetic in the next section.
9
8
F 6
F 5
F 7
Reactor
Syngas
L u=9
V u=8
V u =9
F 8
Sample
Reactor
Outlet pipe
50% Liquid Level

Fig. 4.12: Model scheme for the reactor .
T wo hold-ups are rele vant for his unit, the liquid phase, where the hydroformylation reaction
takes place and a g as phase. The mass balance in Eq. (4.8) cov ers gas and liquid phase, rele vant
streams, a liquid sample pur ge
F Reac t or
Sam pl e , i
, and the rele vant reaction rates
r i
of components. Addi-
tionally , the total component hold-up is subdi vided according the e xisting phases:
d H U React or
i
d t = F s = 6 , i − F s = 7 , i + F s = 5 , i − F s = 8 , i − F React or
Sam pl e , i + M i · L u = 9 · A u = 9 · r i (4.8)
H U React or
i = H U L
u = 9 , i + H U V
u = 8 , i (4.9)
The liquid drain from the reactor
F s = 8
is acti v ated using the afore described sigmoidal switching
functions on the le vel
L u = 9
(see right image in Fig. 4.12). The drain is thus set to be the sum of
86

4.3 De velopment of the Dynamic Process Model
the reactor feed and
F Lvl
u = 9
. The latter is an o v erflo w regulation stream, designed as a PI-controller
according to the deliberations in Sec. 4.3.1.
F s = 8 = T RI G Lvl
u = 9 · F s = 5 + F Lvl
u = 9 (4.10)
Related to the gas phase, the inlet and outlet streams
F s = 6
,
F s = 7
are gi ven belo w .
F C on t i
s = 7
is a
design v ariable, setting the purge of gas phase from the reactor . This purge is required since the
solubility of H
2
in the reaction mixture is higher than the solubility for CO. Hence, shifts in the
composition of the gas phase are e xpected for a continuous operation.
F s = 6
then specifies the
feed stream of gas to the plant as the sum of pur ge stream setpoint and a regulation stream
F Gas
u = 8
.
This is again defined as PI-controller , working on the de viation between current reactor pressure
and its setpoint
p SP
u = 8
. Using Eq. (4.14) - 4.16, the gas feed composition can be switched between
nitrogen and syngas feed, using an acti v ation trigger T RI G Gas
u = 8 .
F s = 6 = F Gas
u = 8 + F C on t i
s = 7 (4.11)
F s = 7 = F C ont i
s = 7 (4.12)
p SP
u = 8 = p St art + T RI GGE R Lvl
u = 9 · p Cont i (4.13)
w s = 6 , i = 6 = w SP
s = 6 , i = 6 · T RI G Gas
u = 8 (4.14)
w s = 6 , i = 7 = w SP
s = 6 , i = 7 · T RI G Gas
u = 8 (4.15)
w s = 6 , i = 8 = 1 − T RI G Gas
u = 8 (4.16)
The reactor pressure itself is calculated from the hold-up of the gas phase using the ideal gas la w .
Accordingly , the partial pressures for CO and H 2 are calculated in Eq. (4.18).
N i
∑
i = 1
( H U V
u = 8 , i
M i
) = p u = 8 · 100 · ( V React or − A u = 9 · ( L u = 9 ))
R · T u = 9 (4.17)
H U V
u = 8 , i
M i
= p V
i · 100 · ( V Reac t or − A u = 9 · ( L u = 9 ))
R · T u = 9 wi t h i = { 6 , 7 } (4.18)
This simplified approach is chosen, since only pressure and composition of the gas are rele v ant,
assuming still moderate pressures. Also, other compounds are rather high boiling liquids applied
at a reaction temperature of 95
◦ C
. No significant partial pressures of these substances in the gas
phase are expected. W ith a suf ficiently large gas pur ge, the composition is then assumed to be
constant, while physically correct mole numbers of gasses for g as hold-up and streams are not
of interest. Ho wev er , the gas solubility is rele v ant and its modeling described in Sec. 4.3.5.
87

4 De velopment of Strate gies for Process Design & Operation
4.3.4 Development of an A dapted Kinetic Mo del
W ithin this work, the proposed workflo w for the formulation of adapted kinetic models for
systems with inherent additional influences (see Sec. 2.3.4) is applied to generate a suitable
kinetic model and gain a suf ficiently precise representation of the reaction kinetics. This is
mandatory since up to no w no suitable kinetic models for reactions in microemulsions are
documented in the literature.
Step 0: Mo del and Data A cquisition
W ithin the Collabor ative Resear c h Center InPR OMPT , extensi ve studies on the reaction mech-
anism of the hydroformylation of 1-dodecene ha ve been conducted. This resulted in the reac-
tion network presented in Fig. 3.1 and a detailed (semi-)mechanistic kinetic model deri v ed by
(Kiedorf et al., 2014). The deriv ation of respecti ve rate equations Eq. (4.19) - 4.24 for tar get
and side reactions is based on a general methodology e xplained by Murzin et al. (2016) and
considers the W ilkinson catalytic c ycle (Fig. 2.11). The rate index
r = { 1 . . . 6 }
denotes the
reaction path according to Fig. 3.1 with 1:
r I so
, 2:
r H yd A
, 3:
r H yd B
, 4:
r H y f oA
, 5:
r H y f oB
, 6:
r H y f o C
. Concentrations
c u = 9 , i = 1 ... 5
then represent the reactants: 1-dodecene (1), iso-dodecene
(2), iso-tridecanal (3), dodecane (4), and tridecanal (5). Additionally , Eq. (4.25) giv es a rate
expression for the catalyst pre-equilibria, defining the concentration of acti ve catalyst at current
precursor concentration
c ca t
and dissolved g as concentrations of H
2 c u = 9 , i = 6
and CO
c u = 9 , i = 7
.
r 0
r = 1 = ψ ca t ·
k re f
r = 1 · exp ( − E r = 1
R · ( 1
T u = 9 − 1
T re f )) · ( c u = 9 , i = 1 − c u = 9 , i = 2
K eq
r = 1 )
( 1 + K r = 1 , e = 1 · c u = 9 , i = 1 + K r = 1 , e = 2 · c u = 9 , i = 2 ) (4.19)
r 0
r = 2 = ψ ca t · k re f
r = 2 · exp ( − E r = 2
R · ( 1
T u = 9 − 1
T re f )) · c u = 9 , i = 2 · c u = 9 , i = 6 (4.20)
r 0
r = 3 = ψ ca t ·
k re f
r = 3 · exp ( − E r = 3
R · ( 1
T u = 9 − 1
T re f )) · c u = 9 , i = 2 · c u = 9 , i = 6 − c u = 9 , i = 4
K eq
r = 3
( 1 + K r = 3 , e = 1 · c u = 9 , i = 1 + K r = 3 , e = 2 · c u = 9 , i = 4 + K r = 3 , e = 3 · c u = 9 , i = 6 ) (4.21)
r 0
r = 4 = ψ ca t · k re f
r = 4 · exp ( − E r = 4
R · ( 1
T u = 9 − 1
T re f )) · c u = 9 , i = 2 · c u = 9 , i = 6 · c u = 9 , i = 7 (4.22)
r 0
r = 5 = ψ ca t · k re f
r = 5 · exp ( − E r = 5
R · ( 1
T u = 9 − 1
T re f )) · c u = 9 , i = 1 · c u = 9 , i = 6 · c u = 9 , i = 7
( 1 + K r = 5 , e = 1 · c u = 9 , i = 1 + K r = 5 , e = 2 · c u = 9 , i = 5 + K r = 5 , e = 3 · c u = 9 , i = 6 ) (4.23)
r 0
r = 6 = ψ ca t · k re f
r = 6 · exp ( − E r = 6
R · ( 1
T u = 9 − 1
T re f )) · c u = 9 , i = 1 · c u = 9 , i = 6 · c u = 9 , i = 7 (4.24)
ψ ca t = c ca t
( 1 + K cat , e = 1 · c u = 9 , i = 7 + K cat , e = 2 · c u = 9 , i = 7
c u = 9 , i = 6 ) (4.25)
K eq
r = exp ( ∆ G r
R · T u = 9 ) wi t h r = { 1 , 3 } (4.26)
∆ G r = 3 = ( − 126 . 28 + 0 . 13 · T + 6 . 8 · ( 10 ) − 6 · ( T ) 2 ) · ( 10 ) 3 (4.27)
88

4.3 De velopment of the Dynamic Process Model
The component reaction rates
r i
in Eq. (4.28)-Eq. (4.32) are expressed as the summarized reaction
rates
r r
and are applied for mass balances (Eq. (4.8)). For no w , a batch reactor system (lab) is
considered for later parameter estimation. This initial model is denoted as
M od el 0
further on.
1 − dodecene : d c i = 1
d t = r i = 1 = − r 0
r = 1 − r 0
r = 3 − r 0
r = 5 − r 0
r = 6 (4.28)
iso − dodecene : d c i = 2
d t = r i = 2 = r 0
r = 1 − r 0
r = 2 − r 0
r = 4 (4.29)
iso − tridecanal : d c i = 3
d t = r i = 3 = r 0
r = 4 + r 0
r = 6 (4.30)
dodecane : d c i = 4
d t = r i = 4 = r 0
r = 2 + r 0
r = 3 (4.31)
tridecanal : d c i = 5
d t = r i = 5 = r 0
r = 5 (4.32)
The model provides fi ve liquid reactant concentrations
c i = 1
-
c i = 5
as measurable model states.
T emperature, catalyst concentration, and pressure are then relev ant controls or input v ariables.
Hereof, reaction pressure is linked to the gas solubility model in Sec. 4.3.5 to pro vide concen-
trations of CO and H
2
in the liquid phase. The multitude of adjustable kinetic parameters can
be classified as: acti vation ener gies
E r
, pre-exponential f actors
k re f
r
, reaction equilibrium coef fi-
cients
K eq
r
, related to the respectiv e Gibbs reaction energies
∆ G r
, and lumped inhibition constants
K r , e
, which are linked to reactant concentrations. This totals in 23 parameters for which Kiedorf
et al. (2014) pro vide an initial set estimated from e xperimental data for the hydroformylation of
1-dodecene in a thermomorphic solvent system, using BiPhePhos as lig and.
Step 1: Kinetic Info rmation and P a rameter Estimation fo r Model 0
Ha ving prepared
M od el 0
, it is no w verified that the initial model structure is suitable for the ne w
application case: hydroformylation in MES using SulfoXantPhos as ligand.
Hereof, kinetic e xperiments were conducted with v ariation of the afore mentioned inputs
T u = 9
,
c ca t
, and
p u = 9
. Dynamic trajectories of the concentrations
c i = 1
-
c i = 5
are tracked with sampling
and
GC
analysis (see T ab . C.5 for list of experiment designs). The experiments were performed
by T obias Hamerla and T obias Pogrzeba at Department of Chemistry , T echnische Univ ersität
Berlin, using a 100 mL batch autocla ve. Information on catalyst preparation, the e xperimental
setup, and analytics can be found elsewhere (Pogrzeba et al., 2015; Pogrzeba et al., 2017a).
Subsequently , a parameter estimation is performed on the gathered kinetic data using
M od el 0
.
For this purpose, the frame work for parameter estimation with subset selection and quantification
of rele vant uncertain parameters (Sec. 2.4.3) is used. The implementation is done in Matlab,
using lsqnonlin as solver for optimizatio (
PE
) and ode15s for solving the
O
rdinary
D
if ferential
E quation (system) (ODE) (see Sec. C.2 for additional information):
89

4 De velopment of Strate gies for Process Design & Operation
Initially , parameter initials and bounds are specified. Here, the parameter set presented by
Kiedorf et al. (2014) is used with rather lar ge intervals around the parameter initials to guarantee
a feasible solution of the
PE
problem. Due to the non-linearity of the model, a global solution of
the parameter estimation cannot be guaranteed and se veral local minima are possible depending
on parameter initials. Thus, a parameter sampling is applied, using the Hammersle y Sequence
Sampling (Hammersley , 1960; Diwekar et al., 1997). It is carried out on logarithmic scaling
to ensure an appropriate distrib ution of the samples in the parameter space, since bounds are
spanning se veral orders of magnitude. In total 5000 samples are tested on a pre-run of lsqnonlin
with a maximum of fi ve iterations. The sample sho wing the smallest
RSME
is then used for the
actual parameter estimation. The applied parameter bounds, the calculated best initial guess, and
the final set of estimated parameters can be found in T ab . C.6.
The final solution is obtained with an
RSME
of 0.0229 and visualized in Fig. 4.13 through a
0
1
2
3

01234

0
0.1
0.2
0
1
2
3

01234

0
0.1
0.2
Concentration / mol L -1
Concentration / mol L -1
Reaction time / h Reaction time / h
0
1
2
3

01234

0
0.1
0.2
Concentration / mol L -1
Reaction time / h
T=80°C, p=15bar , c Cat =0.25g/L

01234
0
1
2
3

1-do decen e
iso-do decen e
iso-tridecana l
do decane
tridecanal
T=95°C, p=15bar , c Cat =0.25g/L
T=95°C, p=15bar , c Cat =0.5g/L

Fig. 4.13:
Comparison of experimental data (symbols) and
M od el 0
(lines) for v aried temperatures and
catalyst concentrations. Further e xperimental conditions ha ve been kept constant. Max. error
from max. analytical error: ± 0 . 08 mol L − 1 .
90

4.3 De velopment of the Dynamic Process Model
comparison of experimental da ta and model kinetic trajectories for dif ferent reaction tempera-
tures and catalyst concentrations. In each case, a very good agreement is found and it can be
stated, that
M od el 0
generally is suitable to represent the kinetics of the hydroformylation in the
reg arded microemulsion system. Howe v er , this statement is limited to the varied set of inputs for
M od el 0
(
T u = 9
,
c ca t
,
p u = 9
) and a fixed composition of the microemulsion used for experiments.
Step 2: Influence Identification
The ne xt step of the workflo w deals with the analysis of the desired application and identification
of additional influences on the reaction performance not incorporated in
M od el 0
. This has
already been done in Sec. 3.3 with respect to the applied microemulsion system and application
in a mini-plant. From this discussion, the surfactant concentration
γ
and the ligand to metal ratio
L : M
are sho wn to be most rele v ant. V ariations of both are typically eminent for the mini-plant
operation with internal recycles. The relev ance of these additional influence factors becomes
apparent in Fig. 4.14. Here, the amount of surfactant and the ligand to metal ratio are changed
and compared to the model trajectories obtained from
M od el 0
. In both cases, large de viations on
the concentration profiles of 1-dodecene and tridecanal are obvious. Additionally , the increased
isomerization for L : M = 2 is not cov ered by the model.
0
1
2
3

01234

0
0.1
0.2
0
1
2
3

01234

0
1
2
Concentration / mol L -1
Concentration / mol L -1
Reaction time / h Reaction time / h
γ=4% L:M=2

0 0.5 1 1.5 2 2.5 33 .5 4
0
2
4

1-do decen e iso-do decen e iso-tridecanal do decan e tridecanal

Fig. 4.14:
Comparison of experimental data (symbols) and
M od el 0
(lines) at ligand and surfactant
concentrations dif fering from standard standard mixture composition (
γ 0 = 8 %
,
LM = 4
).
Max. error from max. analytical error: ± 0 . 08 mol L − 1 .
Step 3: Mo del Up date and P a rameter Estimation fo r Model real
No w , the ef fect of these additional influences is quantified with additional kinetic experiments.
Mathematical formulations to adapt reaction rate equations in M od el 0 are deri ved thereof:
91

4 De velopment of Strate gies for Process Design & Operation
Surfactant concentration
: Figure 3.6 sho ws an increase of con version o ver the surf actant
concentration. It is assumed, that all reaction rates are increased uniformly , since the ov erall
reaction selecti vity does not change. A suitable po wer law e xpression is therefore giv en. Therein,
c i = 12
is the surfactant concentration in
mol L − 1
and
n Sur f ac t ant
an adjustable parameter to account
for disproportionality:
r r = c n Sur f ac t ant
i = 12 · r 0
r (4.33)
Ligand to metal ratio
: In this case, a step-wise transition of the reaction performance is ob-
served (Fig. 3.3). Belo w a critical ratio, 1-dodecene con v ersion increases by a factor of roughly
3. At the same time, selecti vity drops significantly . This effect is due to the already discussed
shifting catalyst equilibria. A closer look at kinetic data re veals that isomerization of 1-dodecene
is increased by a factor of roughly 6. Concurrently , also the hydroformylation reactions are
accelerated (terminal and branched aldehyde), yet less pronounced. T o account for this, the
implementation of a sigmoidal function into the kinetic model is proposed:
r r = ( 1 + k LM
r
1 + exp ( − ( K LM − n i = 11 , u = 9
n i = 10 , u = 9 ) · P t r ig
r ) ) · r 0
r (4.34)
Here,
n i = 11 , u = 9
and
n i = 10 , u = 9
are the amount of ligand and catalyst precursor , respectiv ely .
K LM
is an adjustable parameter defining the thr eshold of the ligand to metal ratio, at which the
sigmoidal takes action.
k LM
r
then represents the enhancement factor for the corresponding rate
equation and
P t r ig
r
then adjusts the slope of the sigmoidal function. This adaption is then applied
on the rate equations for the isomerization and all hydroformylation reactions. Thus, together
with Eq. (4.33) a ne w adapted kinetic model
M od el real
for the hydroformylation of 1-dodecene
in microemulsions is obtained, containing six additional adjustable parameters:
r ∗
r 1 = c n Sur f ac t ant
i = 12 · ( 1 + k LM
r
1 + exp ( − ( K LM − n i = 11 , u = 9
n i = 10 , u = 9 ) · P t r ig ) ) · r 0
r 1 , r 1 = { 1 , 4 , 5 , 6 } (4.35)
r ∗
r 2 = c n Sur f ac t ant
i = 12 · r 0
r 2 , r 2 = { 2 , 3 } (4.36)
Subsequently , another parameter estimation is performed on the updated set of experiments
listed in T ab . C.7. The procedure is analogous to the
PE
for
M od el 0
, using the final estimated
parameters for
M od el 0
as initials. For the six ne wly introduced parameters, initials and assump-
tions on bounds are deri ved from e xperimental data. The applied parameter bounds and best
initial set retrie ved from Hammersle y sampling are again listed in the appendix (T ab . C.8). At
this point it has to be noted, that the
PE
is again performed on the full set of parameters to
account for the structural change in the kinetics and additional experimental data.
92

4.3 De velopment of the Dynamic Process Model
The final solution is obtained with an
RSME
of 0.0313 and T ab . C.9 listing the obtained pa-
rameters together with information on their identifiability . The final acti ve set of parameters
conclusi vely includes
K cat , e = 1
, which influences the amount of av ailable acti ve catalyst in the
system and thus all reaction rates. W ith
K r = 5 , e = 2
, an inhibition factor is also included, since it
influences the predominant hydroformylation reaction rate. Ho wev er , due to the rather small
set of e xperimental data and high parameter colinearity , the number of identifiable parameters
is very small and thus it is not surprising that the obtained parameter v alues differ significantly
from the ones found for
M od el 0
. Interestingly though, obtained acti vation ener gies are still
in physically reasonable orders of magnitude and in good accordance to the v alues found by
(Kiedorf et al., 2014). Especially , the acti vation ener gy for the hydroformylation of the terminal
olefin obtained with
E r = 5 = 57 . 9 kJ mol − 1
is in good accordance with literature data collected
for the applied catalyst in microemulsions (59
kJ mol − 1
, (Pogrzeba et al., 2017a)) and single
phase systems (57.12 kJ mol − 1 , (Bhanage et al., 1997)).
T o visualize these results, Fig. 4.15 and Fig. 4.16 are provided. Both plots demonstrate the
successful adaptation of the initial kinetic model. The influences of surfactant and ligand to
metal ratio are displayed adequately and de viations between model prediction and experiments
are widely belo w 10 %. Ho wev er , concentrations of iso-dodecene are slightly underestimated
for the reference case (
T = 95 ◦ C
) and reduced surfactant concentration (
γ = 4
%). This is partly
explained by e xperiments not depicted here, sho wing the opposite deviation. In case of reduced
ligand to metal ratio
L : M = 2
,
M od el real
gains an adequate prediction of highly increased
isomerization.
0 0.5 1 1.5 2 2.5

Ex pe ri me nt :
c
i
in mol/L

0
0.5
1
1.5
2
2.5

Mo del: c

i

in mol/L

+10%
-10% T=95°C
Ȗ =4%
L:M=2

00 .5 11 .5 22 .5 33 .5 4
0
2
4

1-do decene
iso-do decen e
iso-tridecanal
do decane
tridecana l

Fig. 4.15:
Parity plot adapted kinetics:
T = 95 ◦ C
reference experiment,
L : M = 2
v ariation ligand to
metal ratio, γ = 4 % v ariation surfactant concentration. Max. error: ± 0 . 08 mol L − 1 .
93

4 De velopment of Strate gies for Process Design & Operation
0
1
2
3

01234

0
0.1
0.2
0
1
2
3

01234

0
1
2
Concentration / mol L -1
Concentration / mol L -1
Reaction time / h Reaction time / h
0
1
2
3

01234

0
0.1
0.2
Concentration / mol L -1
Reaction time / h
γ=4%
T=95°C L:M=2

01234
0
0.5
1
1.5
2
2.5

1-do decen e
iso-do decen e
iso-tridecana l
do decan e
tridecanal

Fig. 4.16:
Comparison of experimental data (symbols) and the adapted kinetic model (lines) for e xperi-
ments:
T = 95 ◦ C
reference experiment,
L : M = 2
v ariation of ligand to metal ratio,
γ = 4
%
v ariation of surfactant concentration. Max. error from max. analytical error:
± 0 . 08 mol L − 1
.
4.3.5 Gas Solubilit y Mo del
CO and H
2
are main reactants for the hydroformylation reaction. Hence, their solubility in the
liquid phase of the reactor needs to be modeled adequately . Regarding this, Bernas et al. (2010,
p. 618) state, that “org anic synthesis reactions are in most cases considered to be slo w or very
slo w compared to the dif fusion processes. In the ultimate case, this means that the gas–liquid
reaction system can be treated as a pseudo-homogeneous system; just the solubilities of the
gas-phase components are included in the model”. An according model formulation for the
hydroformylation of propene, based on reacti ve films, yielded v ery good accordance of model
simulations and experimental data. F or long-chained alkenes, e ven lo wer reaction rates are found
and thus kinetic limitations should be dominant and mass transfer can be omitted (W ender et al.,
1956). For the system at hand, this is confirmed with Fig. A.2, since the reaction rate is found to
be independent from the gassing stirrer speed.
94

4.3 De velopment of the Dynamic Process Model
From a thermodynamic point of vie w , the gas liquid equilibrium can be modeled using Henry’ s
law for all g asses in each liquid component. Howe v er , literature data are limited to H
2
and
CO solubility in water at high pressure. For CO, solubility data are only partially a vailable for
some oil phase compounds (V ogelpohl et al., 2013; V ogelpohl et al., 2014). Moreov er , the effect
of microemulsion structure and phase beha vior on gas solubilities is widely unkno wn. Thus
in vestigations on the gas solubility in the
MES
ha ve been carried out in cooperation with Max
Lember g, Department of Biochemical and Chemical Engineering, TU Dortmund. Therein, the
gas solubility has been measured for v arying concentrations
α
,
γ
, and product content
Y
, as well
as state v ariables T and p . Hence, the solubility is determined as a mixture property:
Exp erimental Pro cedure
The experiments were performed using a vie w cell for high pressures up to 400 bar . The system
is temperature-controlled and allo ws for variable v olumes between 27.5 and 59.6 mL. A detailed
description of the test stand, sample preparation, and experimental procedure can be found in
(V ogelpohl et al., 2013; Bardas, 2015). T wo series of e xperiments were performed for CO
and syngas respecti vely , varying the mixture composition according to T ab . 4.5. F or each
mixture, at minimum two pressure setpoints between 10 and 30 bar were tested at 85
◦ C
and
105
◦ C
. Microemulsion mixtures were prepared from distilled water , 1-dodecene, tridecanal, and
Marlipal ® 24/70. Syngas is used in a molar composition of 1:1.
T ab. 4.5: Composition of prepared samples for gas solubility experiments.
Exp. 1 2 3 4 5 6 7 8 9 10 11
α / % 50 50 50 50 50 50 50 40 40 60 60
γ / % 8 8 8 10 6 10 6 10 6 10 6
Y / % 0 20 40 0 0 40 40 40 40 40 40
Note that due to experimental limitations, the solubility of hydrogen cannot be measured directly .
Instead, it is deri ved from the syngas solubility , assuming a constant molar ratio of CO and H
2
of 1:1 and no interactions in the liquid phase:
x L
Syngas ( p u = 8 ) = x i = 7 ( p V
i = 7 ) + x i = 6 ( p V
i = 6 ) (4.37)
Mo del F o rmulation
The collected data are in the appendix (T ab . C.10, T ab . C.11) and further used to deri ve an
empirical model for the mole fractions of CO and H
2
. Fi ve inputs
α
,
γ
,
Y
,
T
, and
p
are gi ven, for
which a polynomial model is formulated. F or this, firstly the gathered data is e valuated re garding
95

4 De velopment of Strate gies for Process Design & Operation
the v ariation of a single input at constant remaining variables to identify appropriate polyno-
mial orders. This is subsequently done for all inputs and combinations of inputs. Respecti ve
polynomial parameters are obtained from a nonlinear regression in Matlab and finally the model
structure is tested for o verfitting using additional test data. Subsequently , the following model
structure is obtained:
x i = 7 = p V
i = 7 · P i = 7 , Sol = 1 + T · P i = 7 , Sol = 2 + α · P i = 7 , Sol = 3 + γ · P i = 7 , Sol = 4 + Y · P i = 7 , Sol = 5
+ γ 2 · P i = 7 , Sol = 6 + Y 2 · P i = 7 , Sol = 7 + p V
i = 7 · T · P i = 7 , Sol = 8 + p · α · P i = 7 , Sol = 9
+ p V
i = 7 · γ · P i = 7 , Sol = 10 + p V
i = 7 · Y · P i = 7 , Sol = 11 + T · α · P i = 7 , Sol = 12
+ T · γ · P i = 7 , Sol = 13 + T · Y · P i = 7 , Sol = 14 + α · Y · P i = 7 , Sol = 15 (4.38)
x i = 6 = p u = 8 · P i = 6 , Sol = 1 + T · P i = 6 , Sol = 2 + α · P i = 6 , Sol = 3 + γ · P i = 6 , Sol = 4 + Y · P i = 6 , Sol = 5
+ ( γ ) 2 · P i = 6 , S ol = 6 + ( Y ) 2 · P i = 6 , S ol = 7 + p u = 8 · T · P i = 6 , Sol = 8 + p u = 8 · α · P i = 6 , Sol = 9
+ p u = 8 · γ · P i = 6 , Sol = 10 + p u = 8 · Y · P i = 6 , Sol = 11 + T · α · P i = 6 , Sol = 12
+ T · γ · P i = 6 , Sol = 13 + T · Y · P i = 6 , Sol = 14 + α · Y · P i = 6 , Sol = 15 ) − x i = 7 (4.39)
wi t h α = α s = 8 , γ = γ s = 8 , Y = Y s = 8 , T = ( T u = 9 − 273 . 15 )
The regression results are sho wn in the 3-D plot in Fig. 4.17. Here the experimental data
is sho wn together with the model for CO and H
2
. Additionally , parity plots are supplied in
Fig. 4.18. These indicate an acceptable model accurac y , especially under consideration of test
data used for model v alidation. Finally , obtained parameters are gi ven in T ab . C.12.
T in °C
0
50
2
x Gas in g/g
× 10 -3
4
100
6
0
p i n bar
10
20
150 30
CO M o del
Syn ga s Mo del
Syn ga s Da ta
CO D at a

Fig. 4.17: Comparison of modeled solubility of CO and syngas with e xperimental data.
96

4.3 De velopment of the Dynamic Process Model
0123456 7
Exp. Data : x in g/g
L
C O ×
10
-3
0
1
2
3
4
5
6
7
Mo del: x in g/g
L
C O
× 10 -3
+5%
-5%
PE S et
T est S et

0 0.5 1 1.5 2 2.5 3
Mo del: x in g/g
L
S yn g a s ×
10
-3
0
0.5
1
1.5
2
2.5
3
Mo del: x in g/g
L
S yn g a s
× 10 -3
+5%
-5%
PE S et
T est S et

Fig. 4.18: Parity plot for the solubility of CO and syng as in the reaction mixture.
4.3.6 Phase Sepa ration Mo del and Soft-Senso r Development
Follo wing the system analysis in Sec. 3.4, a feasible mini-plant operation is only possible for
maintaining three-phasic separation of the microemulsion. As a ke y element of this thesis, a
respecti ve phase separation model is deri ved to track this separation operation re gion and de-
scribe the component distrib ution therein. Hence, a profound description of phase compositions
and v alid component mass balances are mandatory for such a model. One core part for that is a
soft-sensor to account for missing measurements of rele v ant states in the mini-plant (Sec. 3.4.5).
The general modeling approach is depicted in Fig. 4.19 and combines the specific features of the
three-phasic separation of the microemulsion deri ved from theory (Sec. 2.1.3) and the analysis of
the actual system beha vior . The fundamental idea is that for gi v en constant temperature and pres-
sure and occurring three-phasic miscibility gap, the component concentrations in the forming
phases are constant. Only the fractions of phase volumes are changing according to the initial
position within the miscibility gap (see top left in Fig. 4.19). W ith this idea in mind, optically
accessible phase le vels can be correlated with the inte gral concentrations of the mixture (phase
state soft-sensor). In a second step, the composition of the indi vidual phases is to be specified.
The main idea here is to focus on the excess phases, thus the upper oily and lo wer aqueous phase
of the separated ME. The composition of these phases is dominated by the two-phase oil-water
system and the exceptionally lo w surfactant concentrations defined by the cmc.
Combining these two approaches results in a fully determined model for the phase separation.
Rele v ant component concentrations used in the model adhere to the set of rele v ant sensiti ve influ-
ences on the phase separation system from T ab . 3.5: oil to water ratio
α
, surfactant concentration
γ
, and yield
Y
. These are used to uniquely identify the mixture composition for application of
the phase separation model. In mini-plant model nomenclature those are gi ven as:
97

4 De velopment of Strate gies for Process Design & Operation
α s = 8 · (
5
∑
i = 1
w s = 8 , i + w s = 8 , i = 9 ) =
5
∑
i = 1
w s = 8 , i , γ s = 8 = w s = 8 , i = 12 (4.40)
Y s = 8 ·
5
∑
i = 1
w s = 8 , i = w s = 8 , i = 5 , Y mol
s = 8 ·
5
∑
i = 1
w s = 8 , i
M i
= w s = 8 , i = 3
M i = 3 + w s = 8 , i = 5
M i = 5 (4.41)
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Fig. 4.19: General approach for the de velopment of the phase separation model.
General assumptions for this phase separation model include disreg arding separation dynamics,
since the observed phase separation time is rather small in the operation re gion. Thus only
the final equilibrium state is of interest and assumed to be reached in the settler . Additionally ,
radial or axial temperature gradients are omitted, since specific heating zones are supplied for
the settler and the ratio of heat transfer area to v olume is high.
Due to the lack of a vailable fundamental models and with widely unkno wn fluid properties,
empirical models based on e xperimental data are employed to describe observ ed system beha vior .
In accordance with deliberations in Sec. 2.4.1 polynomial models are generated, since the amount
98

4.3 De velopment of the Dynamic Process Model
of a vailable data is still limited and pre vents the application of more sophisticated data-dri v en
modeling techniques. For this purpose, Matlab’ s Curve F itting toolbox and the poylfitn
4
toolbox
are used. In each case, test data or cross v alidation is applied to prev ent ov erfitting.
Phase V olume F raction Mo del and Concentration Soft-Senso r
The first part of the phase separation modeling aims for an empiric description of Kahlweit’ s
F ish and the corresponding fractions phase volumes. For mini-plant operation, two aspects are
of interest: gi ven a mixture concentration, in which temperature interv al does the three-phase
separation occur? Which fractions of phase volum es are present at specific temperatures? This
is systematically deri ved from the gathered e xperimental information of the full mapping of the
phase separation system in Sec. 3.4.6.
T emperatur e boundaries
The data is firstly classified into separation states (Fig. 3.8) to identify the operation re gion
(three-phase separation). Of importance therein is a suitable oil phase volume fraction >10 % to
alw ays ensure feasible product capture for mini-plant operation. T ransition states from the three
to the two-phasic separation are ho we ver e xcluded from this set, because the formation of dense
surfactant layers is observ ed here. These layers represent undesired local accumulations, which
impede mini-plant operation.
Subsequently , temperature limits for the operation re gion are obtained for sampled mixtures.
These are then mer ged into polynomial models for an upper bound
T h PhS
in Eq. (4.42) and lo wer
bound
T l PhS
in Eq. (4.43) respecti vely . The corresponding parameters are listed in T ab . C.13
Figure 4.20 sho ws the model surfaces in comparison to the e xperimental results. Here, especially
the steep descent of the
T
-
γ
model plane and the rather small width of Kahlweit’ s F ish are in
good accordance with identified dependencies in Fig. 3.11.
T h PhS = P T h , PhS
ps = 1 · 1 + P T h , PhS
ps = 2 · α + P T h , PhS
ps = 3 · γ + P T h , PhS
ps = 4 · Y + P T h , PhS
ps = 5 · α · γ + P T h , PhS
ps = 6 · γ · Y
+ P T h , PhS
ps = 7 · α · γ · Y + P T h , PhS
ps = 8 · α 2 + P T h , PhS
ps = 9 · γ 2 + P T h , PhS
ps = 10 · Y 2 (4.42)
T l PhS = P T l , PhS
ps = 1 · 1 + P T l , PhS
ps = 2 · α + P T l , PhS
ps = 3 · γ + P T l , PhS
ps = 4 · Y + P T l , PhS
ps = 5 · α · γ + P T l , PhS
ps = 6 · γ · Y
+ P T l , PhS
ps = 7 · α · γ · Y + P T l , PhS
ps = 8 · α 2 + P T l , PhS
ps = 9 · γ 2 + P T l , PhS
ps = 10 · Y 2 (4.43)
wi t h α = α s = 8 , γ = γ s = 8 , Y = Y s = 8 , T = ( T Set t l er − 273 . 15 )
4 https://www .mathworks.com/matlabcentral/filee xchange/34765-polyfitn [Access: 2019-03-14]
99

4 De velopment of Strate gies for Process Design & Operation
γ in g/g
Y in g/g
0.6
0.4
60
T
low,high
=
f

( α

=

0.5, γ, Y )
70
0.05
80
0.06
90
0.2
0.07
100
0.08 0.09 0
0.1
T hi g h Mo del
T lo w Mo del
T hi g h Exp.
T lo w Exp.
T in °C

Fig. 4.20:
Model surface plots of the lo wer and upper boundary for temperature of the three-phase
region depending on yield and surf actant concentration at constant oil to water ratio of 50 %.
Evolution of fractions of phase volumes
Next, the correlation of the initial mixture concentration and temperature with the fractions of
phase v olumes (
α , γ = f ( Φ Oil , Φ M ix , Φ W at er , T , Y )
) is modeled. This is a core functionality of the
model, since it enables the crucial soft-sensing of concentrations from phase le vel data observ ed
in the mini-plant’ s settler . T o describe the modeling approach, Fig. 4.21 depicts experimental
72 74 76 78 80 82
T in °C
0
10
20
30
40
50
60
Φ Oil in %
Φ Oil = f

( α

=

0.5, γ

=

0.08, Y

=

0 )
Mo del
Po ly
Mo del
T r ig
Exp erimen t

Fig. 4.21:
Comparison modeling strategy for the phase v olume fraction e volution: polynomial (Poly)
and sigmoid augmented model (T rig).
100

4.3 De velopment of the Dynamic Process Model
data on the oil phase fraction for the standard mixture
α = 50
% -
γ = 8
% -
Y = 0
%. Y et again
the main focus lies on an adequate representation of the three-phase separation for which high
oil le vels > 30 % are present in contrast to the border regions. This behavior is hard to capture
with standard polynomial model structures and leads to larger de viations in the region of interest
(Model Poly ).
Therefore, an augmented model structure is implemented using switching functions (Model
T rig
).
Using the model functions
T l PhS
and
T h PhS
for the critical lo wer and upper boundary of temper -
ature for the three-phase body , these can be augmented with sigmoidal functions to activ ate the
formation of rele v ant phase fractions only within the three-phase region ( [ T l PhS , T h PhS ] ):
Φ Phase =( P PhS , Phase
ps = 1 · 1 + P PhS , Phase
ps = 2 · T + P PhS , Phase
ps = 3 · α + P PhS , Phase
ps = 4 · γ + P PhS , Phase
ps = 5 · Y
+ P PhS , Phase
ps = 6 · T · α + P PhS , Phase
ps = 7 · α · γ + P PhS , Phase
ps = 8 · γ · Y
+ P PhS , Phase
ps = 9 · T · γ + P PhS , Phase
ps = 10 · α · Y + P PhS , Phase
ps = 11 · T 2 + P PhS , Phase
ps = 12 · α 2
+ P PhS , Phase
ps = 13 · γ 2 + P PhS , Phase
ps = 14 · Y 2 ) · T RI G PhS , T h · T RI G PhS , T l (4.44)
Phase = { Oil , W a t er } , α = α s = 8 , γ = γ s = 8 , Y = Y s = 8 , T = ( T Set t l er − 273 . 15 )
T RI G T h , PhS = 1
1 + exp ( 500 · ( T − T h PhS )) (4.45)
T RI G T l , PhS = 1
1 + exp ( − 500 · ( T − T l PhS )) (4.46)
Φ Oil
and
ϕ W at er
are then used to describe the beha vior of respectiv e fractions of the phase
v olumes depending on
α
,
γ
,
T
, and
Y
. For parameter fitting, solely data classified as three-
phase region data and in accordance with
T l PhS
and
T h PhS
are used. Again resulting parameters
are listed in the appendix (T ab . C.14). Additionally , Fig. 4.22 provides a visualization of the
models of
ϕ Oil
and
ϕ W at er
for v aried temperature and surfactant concentration. The experimental
observ ation therein is widely captured by the model. The present deviations mainly result from
partly inconsistent data in the border area of the three-phase region. This could be rectified
by extended e xperimental studies including retries. Ho wev er , phenomena identified in general
for
MES
are well reproduced and in good agreement with theory presented in Sec. 2.1.3. For
completeness it is noted, that the v olume fraction of the emulsion phase
ϕ M ix
is obtained from
the subtraction of the excess phase v olumes from the total v olume of the separated system.
V L , t o t
Set t l er = ( ϕ Oil + ϕ M ix + ϕ W a t er ) · V L , t o t
Set t l er (4.47)
101

4 De velopment of Strate gies for Process Design & Operation
0
10
0.04
20
30
40
0.06
50
60
γ in g/g
0.08
0.1
60
65
70
75
80
85
90
95
Φ Oil = f

( T , α

=

0.5, Y

=

0)
Φ Oil in %
T in °C

0
0.04
10
20
30
0.06
40
0.08
0.1
60
65
70
75
80
85
90
95
Φ Water = f

( T , α

=

0.5, Y

=

0)
Φ Water in %
T in °C
γ in g/g

Fig. 4.22:
Surface plots of the model of e xcess phase volume fractions for oily (left) and aqueous phase
(right) at constant oil to water ratio and yield. Markers highlight experimental data.
Mo del of the Excess Phase Comp osition
The second part of the phase separation model no w aims for the determination of component
concentrations within the excess phases. Y et again, this is not an easy task, since all av ailable
analytical de vices fail in determining surfactant and w ater concentrations. Hence, characteristic
phenomena of
MES
are exploited. The idea and modeling procedure is sketched in Fig. 4.23.
Firstly , a reduced set of ke y components is deri v ed from the applied microemulsion system.
The influence of dissolved g asses is ne glected and water is assumed to be the main compound
of the aqueous phase. Thus catalyst and ligand are basically not considered. Due to the very
high reaction selecti vities, 1-dodecene is assumed to be the main nonpolar oily compound
subsidiary also for iso-dodecene and dodecane. T ridecanal is then considered as the main polar
oily compound. The follo wing modeling idea then is based on two main assumptions:
■
As already presented in Sec. 2.1.3, the surfactant concentration in oily and aqueous e xcess
phase are at the le vel of the
cmc
. For mixtures of nonionic surf actants, water , and long-
chained oily substrates the
cmc
s are around
10 − 5
to
10 − 4 mol L − 1
(Rosen et al., 1982;
Huibers et al., 1996).
■
At these very small surf actant concentrations, the composition of excess phases is then
mainly determined by the two-phasic oil-w ater miscibility gap below the three-phase body
and compositions can be estimated by calculating the L iquid- L iquid- E quilibrium (LLE).
This means, that with the formulation of a
cmc
model and an appropriate
LLE
description both
excess phases can be fully determined re garding their composition. This model formulation is
then connected to the mini-plant model through a back calculation of the reduced set of ke y
components into the full set of components i .
102

4.3 De velopment of the Dynamic Process Model
A ppl i ed
Mi cr o em u l sion
Sy stem
i=1…12
K ey
Com po nen t s
Exce ss Ph ases
j=1…4
Ful ly
det erm i ne d
Sy stem
LLE -UNIFA C
W at er – Oi l
Exce ss Ph ases
➔ j =1, 2,3
▪ 12 m ain
c om pone nts
▪ I nte gration of
m od e ls
▪ Bac kcal cu lat e on
c om p on e nts i
cm c v i a
Sur f ace
Tens i o n
➔ j =4
▪ Em piric al
m od el
▪ Experi me ntal
val id at ion
▪ Do de ce ne
▪ Tride can al
▪ Wate r
▪ Surfac tant

Fig. 4.23: Modeling strategy for the oil and w ater excess phase composition.
cmc model for surfactant concentrations
As already sho wn in Sec. 2.1.1, the
cmc
is accessible via surface tension measurements. Thus,
extended e xperimental studies for the system at hand were conducted with representati ve pur e
oil and water phases:
■ pure 1-dodecene as oily excess phase
■
60 wt.-% 1-dodecene – 40 wt.-% tridecanal as oily excess phase under reaction conditions
■ water with 2.17 wt.-% sodium sulfate
It needs to be mentioned that the aqueous excess phase actually contains lar ger quantities of
rhodium catalyst and ligand. In preliminary studies the influence of catalyst, ligand, and catalyst
acti v ation was tested against of e xemplary systems w ater-sodium sulfate or w ater only . Similar
v alues for the surface tension were obtained for catalyst containing aqueous solutions and water -
sodium sulfate. Hence, the effect of sodium sulf ate on thecmc is assumed to be predominant.
For each phase composition, successi ve dilutions for the surf actant concentration were tested
at dif ferent temperatures. The obtained data are listed in the appendix in T ab . C.20 - C.21.
cmc
data for dif ferent temperatures are then computed and used to parameterize a model fit
function for all in v estigated phase systems (bottom right in Fig. 4.24). The obtained results
are in very good agreement with theory as an increase of
cmc
with rising temperatures can be
observed for 1-dodecene, while the in verse case is present for water . This behavior is back ed
by the observ ations of Kahlweit et al. (1990). For the 1-dodecene/tridecanal system also a
decrease of the
cmc
with temperature is observed, while v alues are still in the same order of
magnitude as for pure 1-dodecene. This is rather une xpected and assumed to be due to the higher
polarity of tridecanal. For the aqueous e xcess phase, the obtained
cmc
at 25
◦ C
(4.01
· 10 − 5
g/g)
is furthermore in good agreement with data presented by Rosen et al. (1982) (5.59
· 10 − 5
g/g for
pure substance equi v alent of Marlipal ® , C 12 E 8 ).
103

4 De velopment of Strate gies for Process Design & Operation
− 10 − 8 − 6 − 4 − 20 2
10 . 0
20 . 0
30 . 0
40 . 0
50 . 0
60 . 0
70 . 0
80 . 0
log( w Sur f act an t ) in wt.-%
σ in mN/m
cmc f or W ater Phase
60 . 4 ◦ C
69 . 5 ◦ C
80 . 0 ◦ C
25 . 1 ◦ C
− 4 − 20 2
21 . 5
22 . 0
22 . 5
23 . 0
23 . 5
24 . 0
24 . 5
log( w Sur f act an t ) in wt.-%
σ in mN/m
cmc f or Oil Phase w/o tridecanal
60 . 1 ◦ C
70 . 2 ◦ C
80 . 1 ◦ C
− 4 − 20 2
20 . 5
21 . 0
21 . 5
22 . 0
22 . 5
23 . 0
23 . 5
24 . 0
24 . 5
log( w Sur f act an t ) in wt.-%
σ in mN/m
cmc f or Oil Phase w tridecanal
59 . 8 ◦ C
69 . 6 ◦ C
80 . 0 ◦ C
w PhS , Sur f ac t an t
s = P cmc , Phase
ps = 1 · exp ( P cmc , Phase
ps = 2 · T )
Phase P cmc , Phase
ps = 1 P cmc , Phase
ps = 2
Oil w/o TDC 2 . 71 · 10 − 16 0.0823
Oil w TDC 150.4 -0,183
W ater 1309 -0.0580

Fig. 4.24:
Experimental determination of
cmc
. The
cmc
is obtained at the intersection of descending
and constant part of the surface tension. A fit function for the temperature dependent
cmc
w PhS , Sur f ac t an t
s is provided together with fitted parameters for each tested phase composition.
LLE surr ogate model for e xcess phase composition
The next step then handles the determination of the oil and w ater amount in the excess phases.
According to Fig. 2.3 and Fig. 2.4 the composition of this excess phases is dominated by the
binary miscibility gap of oil and water . W ith respect to the very small surf actant concentrations
(
cmc
) and the rather steep binodal expected for the w ater-1-dodecene/tridecanal miscibility gap,
it is assumed that the composition of the excess phases can be approximated solely with the
pseudo-binary
LLE
. This system is then gi ven by w ater (
j = 1
) and a pseudo oily compound
consisting of 1-dodecene ( j = 2) and tridecanal ( j = 3).
For this, the molar Gibbs enthalp y of the mixture M ix is defined:
g M ix =
3
∑
j = 1
x j · g j + RT (
3
∑
j = 1
x j · ln ( x j )) + RT (
3
∑
j = 1
x j · ln ( γ j )) (4.48)
104

4.3 De velopment of the Dynamic Process Model
γ j
are the acti vity coefficients of the respecti v e compounds, which account for the nonideal
beha vior of the mixture. As a first estimate,
UNIF A C
is used to calculate these acti vity coeffi-
cients, since necessary group contribution and interaction parameters are a v ailable from literature.
Based on Eq. (4.48), separation and respecti v e phase compositions at giv en temperature and
yield are calculated using UNIF A C coefficients listed in the appendix in Sec. C.2.
The calculated equilibrium data is then used to set up a surrogate polynomial model of the
form:
x Oil / W a t er
j = P Conc , Oil / W at er
ps = 1 · T Se t t l er + P C onc , Oil / W at er
ps = 2 · Y mol
s = 8 + P Conc , Oil / W at er
ps = 3 · T Se t t l er · Y mol
s = 8
+ P Conc , Oil / W at er
ps = 4 · T 2
Set t l er + P Conc , Oil / W at er
ps = 5 · T Se t t l er · Y mol
s = 8
2
+ P Conc , Oil / W at er
ps = 6 · T 2
Set t l er · Y mol
s = 8 + P Conc , Oil / W at er
ps = 7 (4.49)
Fitted parameters are listed in the appendix in T ab . C.15 and the results of
LLE
calculations
and fitting of the surrogate model are visualized in Fig. 4.25. For both surface plots, the mole
fraction of water
x W at er
in the water and oily e xcess phase is shown depending on temperature
T
and tridecanal content
Y
. In both cases, the correlation of
x W at er
with the inputs is physically
consistent as a higher mutual solubility of water and oil is present for increased temperature and
higher yields (meaning a higher oil phase polarity). The model has been further v alidated with
a comparison to GC measurements of the oil phase collected from separated microemulsions.
Considering the measurement accuracy and the v ery small concentrations to be measured, a
qualitati vely good agreement of model and e xperiment can be ensured.
0
0.6
0.02
0.04
400
0.06
0.4
Oi lP hase
0.08
360
0.2 320
0 280

x Oil in mol/mol

Y in g/g T in K

0.999996
0
0.999997
0.999998
280
x

Water

in mol/mol
0.999999
Wa te rP hase
0.2
1
Y in g/g
320
T in K
0.4 360
0.6 400

0.999996
0
0.999997
0.999998
280
x Water in mol/mol
0.999999
W ate r Ph ase
0.2
1
Y i n g/ g
320
T in K
0.4 360
0.6 400

Fig. 4.25:
Surface plots of polynomial surrogate model for content of w ater in the excess phases deri ved
from UNIF A C (marker points).
105

4 De velopment of Strate gies for Process Design & Operation
Infer ence on full set of model concentr ations
As a final step, the concentration information of the sub models for the excess phases is mer ged
and subsequently transformed into the domain of the mini-plant model. Hence, the reduced set
of component concentrations of the excess phases is back calculated to obtain the full set of all
twelve components concentrations. Detailed outlines on that are w ai ved at this point and the
reader is referred to the gi ven detailed procedure in Fig. C.10 and subsubsection C.2.
4.3.7 Settler Mo del fo r Dynamic Three-Phase Sepa ration
The phase separation model is no w implemented into a model of the settler . It is assumed, that
the separation beha vior is solely defined by the composition of the
ME
at the settler’ s inlet
and its temperature. Thus, de veloped phase fractions are transported to the respecti ve phase
hold-ups already present in the settler . Additionally , dif fusion between the phase hold-ups, as
well as backmixing are neglected. This assumption is supported by rather large residence times
in the settler applied for plant operation, the fast separation of the microemulsion within the
three-phase region, and the lack of rigorous descriptions of mass transfer and fluid properties.
Figure 4.26 sho ws the general idea: a splitter unit prior to the separation zone in the settler is
applied. According to its composition and its temperature feed stream
F s = 8
is separated into
the indi vidual phases of the microemulsion. These ”phase streams“ are then fed into respecti v e
independent phase hold-ups of the actual settler unit. Three respecti ve phase drains are then
provided. Though, these are not connected to a certain phase and the phase exiting via a certain
drain can v ary .
Settler
11
13
F 8 10
F 9
F 11
F 10 12
F 14
F 13
F 12

Fig. 4.26: Model scheme for the settler .
Splitter Mo dule
The splitter unit
u = 10
is represented by a component mass balance, sho wing the split of the
reactor outlet into three phase streams:
s = 9
oil phase,
s = 10
mix/emulsion phase,
s = 11
water
106

4.3 De velopment of the Dynamic Process Model
phase. The actual phase split is calculated using the model of the fractions of phase volumes
presented abov e. Thus, Φ Oil and Φ W at er are applied on the reactor outlet v olume flow:
F s = 8 , i = F Oil
s = 9 , i + F M ix
s = 10 , i + F W at er
s = 11 , i (4.50)
N i
∑
i = 1
F Oil
s = 9 , i
ρ Set t l er , i
= Φ Oil ·
N i
∑
i = 1
F s = 8 , i
ρ Set t l er , i
(4.51)
N i
∑
i = 1
F W at er
s = 11 , i
ρ Set t l er , i
= Φ W at er ·
N i
∑
i = 1
F s = 8 , i
ρ Set t l er , i
(4.52)
Component mass streams
F Oil
s = 9 , i
and
F W at er
s = 11 , i
are then calculated according to the model of the
composition of the excess phases, using the concentration measures
w Oil , t ot , PhS
s
,
w PhS , W at er , t ot
s
, and
w PhS , Sur f act an t
s
(see also Fig. C.10) as total oil, water , or surfactant content in
F Oil
s = 9 , i
and
F W at er
s = 11 , i
:
F Phase
s , i · (
5
∑
i = 1
F s = 8 , i ) = w Oil , t ot , PhS
s · F s = 8 , i · F Phase
s f or i = { 1 . . . 5 } (4.53)
F Phase
s , i · F s = 8 = F s = 8 , i · F Phase
s f or i = { 6 . . . 8 } (4.54)
F Phase
s , i = 9 = w PhS , W at er , t ot
s · w W at er
ca t · F Phase
s (4.55)
F Phase
s , i = 10 = w PhS , W at er , t ot
s · w Rh
ca t · F Phase
s (4.56)
F Phase
s , i = 11 = w PhS , W at er , t ot
s · w Lig
ca t · F Phase
s (4.57)
F Phase
s , i = 12 = w PhS , Sur f act an t
s · F Phase
s (4.58)
Indexing − Oil Phase : s = 9 , Phase = Oil W ater Phase : s = 11 , Phase = W at er
Exemplarily the amount of each oily reactant in water or oily excess phase is specified using
Eq. (4.53). It is assumed, that the mass ratio of an oily compound
i = 1 . . . 5
compared to the
sum of all oily compounds is the same for the splitter feed and splitted streams. This general
idea is then also adapted for dissolv ed gasses and the aqueous catalyst solutions. For the latter , a
constant composition is assumed, set by
w W at er
ca t
,
w Rh
ca t
, and
w Lig
ca t
. At this point it is highlighted that
this also sets the catalyst loss to wards the oil phase only being dependent on its water content.
This set of equations pro vides a full determination of all mass fractions for both e xcess phases.
The composition of the emulsion split stream is then a consequence of the mass balance.
First Principles Mo del of the Settler
The settler unit is modeled with three indi vidual hold-ups. Thus, three mass balances are
107

4 De velopment of Strate gies for Process Design & Operation
implemented. Each phase hold-up is solely fed with the respecti v e stream from the splitter unit
(
F Oil
s = 9 , i
,
F M ix
s = 10 , i
,
F W at er
s = 11 , i
). Ho wev er , the v olume fraction of microemulsion phases might vary due
to the current separation state, while the outlet positions of the settler unit are mechanically fixed.
Thus, it is a priori not clear which phase is drawn from which settler drain. Regarding this, a
continuous switching of the composition of settler outlet streams is implemented:
d H U Oil , L
u = 11 , i
d t = F Oil
s = 9 , i − ( F s = 12 · T RI G Out , Oil
u = 11 + F s = 13 · T RI G Out , M ix
u = 11 + F s = 14 · T RI G Out , W at er
u = 11 ) · w Oil
u = 11 , i
(4.59)
d H U M ix , L
u = 12 , i
d t = F M ix
s = 10 , i − ( F s = 13 · ( 1 − T RI G Out , Mix
u = 11 ) · ( 1 − T RI G Out , M ix
u = 13 )
+ F s = 14 · ( 1 − T RI G Out , W at er
u = 13 ) · ( 1 − T RI G Out , W at er
u = 11 )
+ F s = 12 · ( 1 − T RI G Out , Oil
u = 11 ) · ( 1 − T RI G Out , Oil
u = 13 )) · w M ix
u = 12 , i (4.60)
d H U W at er , L
u = 13 , i
d t = F W at er
s = 11 , i − ( F s = 14 · T RI G Out , W at er
u = 13 + F s = 13 · T RI G Out , M ix
u = 13 + F s = 12 · T RI G Out , Oil
u = 13 ) · w W at er
u = 13 , i
(4.61)
T o visualize this implementation, Fig. 4.27 represents a scheme of the settler unit with three
fixed outl ets 1-3. Respecti ve phase hold-ups are represented by the phase le vel
L Oil
,
L E m
, and
L W at er
, v arying between 0 and the maximum le vel of the settler
L t o t
Set t l er
. T ogether with the lev el
setpoints for the outlet drains
L SP , Ou t l e t
Oil
,
L SP , Ou t l e t
M ix
, and
L SP , Ou t l e t
W at er
the formulation of switching
functions is possible like sho wn in the schematic dra wing. Exemplarily
T RI G Out , M ix
u = 13
is used to
check, whether an enlar ged water phase is present, which is superseding the emulsion phase at
the middle drain. Consequently , this leads to a total of six switching functions. For the middle
emulsion phase the logical statement applies that it is present at a certain outlet, if none of the
other phases is present (no trigger acti ve).
𝐿 𝐸𝑚 .
𝐿 𝑂𝑖𝑙
𝐿 𝑊𝑎𝑡𝑒𝑟
3 trigger to activate
water phase at outlets
𝑂𝑢𝑡𝑙𝑒𝑡 1 𝑂𝑖𝑙
𝑂𝑢𝑡𝑙𝑒𝑡 3 𝑊𝑎𝑡𝑒𝑟
𝑂𝑢𝑡𝑙𝑒𝑡 2 𝑀𝑖𝑥
𝐿 𝑂𝑖𝑙
𝑆𝑃 , 𝑂𝑢𝑡𝑙𝑒𝑡
𝐿 𝑀𝑖𝑥
𝑆𝑃 , 𝑂𝑢𝑡𝑙𝑒𝑡
𝐿 𝑊𝑎𝑡𝑒𝑟
𝑆𝑃 , 𝑂𝑢𝑡𝑙𝑒𝑡
3 trigger to activate oil
phase at outlets
Emulsion activate at outlet
if oil and water are absent
Level of
phases
Trigger Oil
𝑇𝑅𝐼𝐺 𝑢= 11
𝑂𝑢𝑡 ,𝑊𝑎𝑡𝑒𝑟 = 𝑓( 𝐿 𝑡𝑜𝑡 − 𝐿 𝑂𝑖𝑙 − 𝐿 𝑊𝑎𝑡𝑒𝑟
𝑆𝑃 , 𝑂𝑢𝑡𝑙𝑒𝑡 )
𝑇𝑅𝐼𝐺 𝑢= 11
𝑂𝑢𝑡 ,𝑀𝑖𝑥 = 𝑓 (𝐿 𝑡𝑜𝑡 − 𝐿 𝑂𝑖𝑙 − 𝐿 𝑀𝑖𝑥
𝑆𝑃 , 𝑂𝑢𝑡𝑙𝑒𝑡 )
𝑇𝑅𝐼𝐺 𝑢= 11
𝑂𝑢𝑡 ,𝑂𝑖𝑙 = 𝑓 (𝐿 𝑡𝑜𝑡 − 𝐿 𝑂𝑖𝑙 − 𝐿 𝑂𝑖𝑙
𝑆𝑃 , 𝑂𝑢𝑡𝑙𝑒𝑡 )
𝑢 = 13
𝑢 = 12
𝑢 = 1 1
Trigger Water
𝑇𝑅𝐼𝐺 𝑢= 13
𝑂𝑢𝑡 ,𝑊𝑎𝑡𝑒𝑟 = 𝑓 (𝐿 𝑊𝑎𝑡𝑒𝑟 − 𝐿 𝑊𝑎𝑡𝑒𝑟
𝑆𝑃 , 𝑂𝑢𝑡𝑙𝑒𝑡 )
𝑇𝑅𝐼𝐺 𝑢= 13
𝑂𝑢𝑡 ,𝑀𝑖𝑥 = 𝑓 (𝐿 𝑊𝑎𝑡𝑒𝑟 − 𝐿 𝑀𝑖𝑥
𝑆𝑃 , 𝑂𝑢𝑡𝑙𝑒𝑡 )
𝑇 𝑅𝐼𝐺 𝑢= 13
𝑂𝑢𝑡 ,𝑂𝑖𝑙 = 𝑓 (𝐿 𝑊𝑎𝑡𝑒𝑟 − 𝐿 𝑂𝑖𝑙
𝑆𝑃 , 𝑂𝑢𝑡𝑙𝑒𝑡 )

Fig. 4.27: Scheme for the dynamic acti vation of the drain of indi vidual phases at the settler outlets.
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