Photo-Optical In-Situ Measurement of Drop Size Distributions: Applications in Research and Industry [original]

IFP Energies nouvelles International Con ference
Rencontres Scie nti ﬁ ques d'IFP Energies nouvelles
Dynamics of Evolving Fluid Interfaces – DEFI Gathering Physico-Chemical and Flow Properties
Dynamiques des écoulements à interfaces ﬂ uides – au croisement de la physico-chimie et de la mécanique de ﬂ uides
Photo-Optical
In-Situ
Measurement of Drop Size
Distributions: Applications in Research and Industry
Robert P. Panckow
1,4
* , Laura Reinecke
2
, Maria C. Cuellar
3
and Sebastian Maaß
4
1
Technische Universität Berlin, Fachgebiet Verfahrenstechnik, Ackerstraße 76, 13355 Berlin - Germany
2
Franken Filtertechnik KG, Max-Planck-Str. 7, 50354 Hürth - Germany
3
Department of Biotechnology, Delft University of Technology, Van der Maasweg 9, 2629 HZ Delft - The Netherlands
4
SOPAT GmbH, Boyenstr. 41, 10115 Berlin - Germany
e-mail: [email protected] - laura.reinecke@franken ﬁ lter.com - [email protected] - sebastian [email protected]
* Corresponding author
Abstract — The exact know ledge of Dr op Size Distributions (DSD) plays a major r ole in various ﬁ elds of
applications to contr ol and optimise pr ocesses as well as r educe waste. In the micr obial pr oduction of
advanced biofuels, oil dr oplets ar e pr oduced under turbulent conditions in an aqueous medium
containing many surface acti ve components, which might hinder the r ecovery of the pr oduct.
Knowledge of DSD is thus essential for process optimisation. This study demonstrates the capabi lity
of a photo-opti cal measur emen t method for DSD measur ement in ferment ation br oth and in plate
separators aimed at cost r eduction in the micr obial production of advanced biofuels. Measur ements
wer e made with model mi xtur es in a bior eactor , and at the inlet and outl et of a plate separator . In
the bior eactor , the method was effective in detecting a br oad range of dr oplet sizes and in
differ e ntiating other disperse components (e.g. micr obial cell s and gas bubbles). In the plate
separator , the method was effective in determi ning the in ﬂ uence of the varied parameters on the
separation ef ﬁ ciency .
Résumé — Mesure photo-opt ique in-situ de la distr ibution des taille des gouttes : app lications
dans la recherche et l ’ industrie — La connai ssance approfondie de la distrib ution du diamètre des
gouttelettes joue un rôle important dans des diverses appli cations des contrôles et d ’ optimisations des
processus, de plus elle permet de rédui re le gaspillage. Pendant la product ion microbienne de
biocarburants avancés, de s gouttelettes d ’ huiles sont produites sous les effets des turbulences du
milieu aqueux conten ant beaucoup de substances tensi o-actives, ce qui pourrait entra ver la
récupération du produit. La connaissance de la distribution du diam ètre des gouttelettes e st donc
essentielle pour l ’ opti misation des processus. Cette étude montre les possibil ités offertes par une
méthode de mesure ph oto-optique pour les mesures de la taille des gouttelet tes dans des bouillons de
fermentations et dans des séparateurs à plaque s, ceci vise à rédui re les coûts dans la product ion
microbienne d e biocarburants avancés. Les mesu res ont été effectuées avec des mélanges modèles
dans un bioréacteur ainsi qu ’ àl ’ entrée et à la sortie d ’ une plaque de séparation. Dans le bioréacteur ,
le procédé est ef ﬁ cace dans la détection d ’ une large gamme de tailles de gouttelettes e t dans la
différenciation des autres compo sants dispersés (par exemp le des cellules microbiennes et des bulles
de gaz). Dans le séparateur à plaque, le procédé est ef ﬁ cace, car il détermin e l ’ in ﬂ uence de différents
paramètres sur l ’ ef ﬁ cacité de la séparation .
Oil & Gas Science and T echnology – Rev . IFP Ener gies nouvelles (2017) 72 ,1 4
Ó R.P . Panckow et al., published by IFP Energies nouvelles , 2017
DOI: 10.2516/ogst/2017009
This is an Open Access article distributed under the terms of the Creati ve Commons Attribution License ( http://cr eati vecommons.or g/licenses/by/4.0 ),
which permits unrestricted use, distribut ion, and reproduct ion in an y medium, pro vided the original w ork is properl y cited.

LIST OF SYMBO LS AND/OR NOTATIONS
a
P
Disperse phase surface area per volume ( l m
 1
)
d
3,2
Sauter mean diame ter ( l m)
d
max
Maximum diam eter ( l m)
d
min
Minimum diam eter ( l m)
d
P
Particle diameter ( l m)
d
rX
Percentiles of quan tity r ( l m)
k Number of b ins (-)
N T otal number of particles (-)
Q
r
Cumulative distrib ution of quantity r (-)
q
r
Density dist ribution of quanti ty r (% l m
 1
)
_
V V olume ﬂ ow rate Lh
 1

x Size variable ( l m)
GREEK SYMBOLS
D Difference operator
g
d
Dynamic viscosit y of dispe rse phase (mPa s)
g
ﬂ
Dynamic visco sity of ﬂ uid (mPa s)
q Density (kg m
 3
)
r
ﬂ
Interfacial tension between ﬂ uids (N m
 1
)
u
d
Phase fraction of disperse phase (vol%)
u
ﬂ
Phase fract ion of ﬂ uid (vol%)
SUBSCRIPTS
0 Related to quantity number
3 Related to quantity volume
i Index number
n Related to quantity number
r T ype of quantity
v Related to quantity volume
X Percentage
ABBREVIATIONS
CDF Cumulative Distr ibution Function
CFD Computational Fluid Dyna mics
DSD Drop Size Distribution
FOV Field Of V iew
GDP Gradient-Direct ion-Pattern
Img Image
NCC Normalised Cross- Correlation procedure
SMD Sauter Mean Diameter
l-l Liquid-Liquid
INTRODUCTION
One of the recent advances in the ﬁ eld of biofuels is the
application of synthetic biology to d evelop microorganisms
that produce long chain hydroca rbons, which are also know n
as advanced biofue ls ( Cuellar and van der W iele n, 2015 ).
These fuels have been demon strated in airplanes durin g
the UN Earth Summ it in Rio de Janei ro 2012 and public
transport buses in São Paulo, Br azil ( www .amyr is.com ).
These fuels are produce d by microorganisms converting
the substrate (glucose, glycer ol) to the biofuel product,
which is secret ed into the fermentation broth, resulting in a
dispersion of product droplets in an aqueous phase from
which the product then has to be recover ed. The turbulent
conditions in the bioreactor and the presence of surface
active components origi nating from the feedst ock or the
microbial process lead to product stabilisation in the form
of emulsions ( Heeres et al. , 2014, 2015 ). As a consequence,
product recover y strategies often involve intensiv e centrifu-
gation, shifts in pH and temperature, and/or the use of de-
emulsi ﬁ ers. Howev er , for econ omically feasible product ion
of the biofuel, the recover y process has to be cheap, so a
low cost process technology should be used. T echnol ogies
such as gas enhanced oil recover y ( Heeres et al. , 2016 ),
magnetic nanoparticles ( Furtado et al. , 2015 ) and catas -
trophic phase inversion ( Glonke et al. , 2016 ) have been
proposed as alternative de-em ulsi ﬁ cation methods. Further-
more, gravity separators such as plate droplet separators
offer opportunities for either concentrati ng the disperse
phase prior to one of the de-em ulsi ﬁ cation met hods above,
or for the compl ete separation of the disperse phase when
the droplet stabilisation can be mitigated durin g the conver-
sion process. The latter is, however, still an object of
research. The ac tual Drop Size Di stribution (DSD) ob tained
after the microbial convers ion process, on the other hand, is
in ﬂ uenced by broth compo sition, reactor parameter s, oil
fraction, type of microorganism and fermentat ion age,
among others. For this type of system off-line meas urement
methods are often not suitable due to non-hom ogeneous
sampling, changes in DSD durin g sample procedure and
ageing of the sample. In-situ meas urement methods over-
come these issues, but should be a ble to differentiate other
disperse phases presen t in the broth such as gas bubble s
and the microorganisms, and must not compr omise the
sterility of the system.
The separation of Liquid -Liquid (l-l) dispersions by
means of gravity separa tors is a standard operat ion in
process engineering . They are typically used when immisci-
ble two-phase mixtures from o rganic and aqueous phases
have to be separa ted. This is the case in a varie ty of upstream
and downstream proces ses in chemical industry , petroleum
industry or pharm aceutical industry . Internal compo -
nents such as plat es have a signi ﬁ cant impact on the phase
Page 2 of 17 Oil & Gas Science and T echnology – Rev . IFP Energies nouvelles (2017) 72 ,1 4

separation ( Mungma et al. , 2014 ). The dimensioning of
gravity or plate droplet separators is currently often an
empirical issu e. The design is based on informat ion about
settling behavi our and residence time and also coales cence
characteristics ( Schlieper et al. , 2004 ). This can result in
overdimensioni ng of these apparat uses to guarantee suf ﬁ -
cient separation ef ﬁ ciency . Besides relevant variables such
as viscosity of the involved ﬂ uids g
ﬂ
, interfacial tensi on r
ﬂ
between these ﬂ uids or phase fract ion u
ﬂ
, the DSD q
0
based
on the particle number and the DSD q
3
based on the particle
volume, play a cruci al role in the selec tion of design param-
eters. The inline meas urement of the DSD at the inlet and
outlet of a gravity or plat e droplet separator is a possible
option to study the in ﬂ uence of the parameters mentioned
above on the separation. Based on these measurements an
improved desig n method for gravity separators using multi-
phase ﬂ ow Computational Fluid Dynamics (CFD) shoul d be
established in the future.
In this contribut ion, we investigated the a pplicability
of in-situ droplet size meas urement by a photo-optical
method both in a ferm entation broth and in plate droplet
separators, aimed at the development of ef ﬁ cient and low
cost separation met hods for the production of advanced
biofuels.
1
IN-SITU
DROPLET SIZE MEASU REMENT
BY PHOTO-OPTIC AL ENDOSCOPE TECHNIQ UE
1.1 Drop Size Measurement s
The aim of this study is to evaluate the applicabil ity of an
in-situ measurement techni que for particle sizes as a tool
in developing new process and reactor concepts containing
l-l dispersions. Knowledge of the DSD is essential for the
development, evalua tion and implementation ( e.g. in CFD
code) of model approac hes which will help to overcome
the gap between industrial process pract ice and detailed pro-
cess understandi ng. Therefore, vario us experiments have
been carried out, analysing the drop size as a function of
reactor desig n parameters, operating conditions and physical
characteristics of the l-l dispersion.
A broad overview of exist ing measurement techni ques can
be found in ( Abidin et al. , 2013 ). They describe the different
methods of measurem ent which can be classi ﬁ ed into in-situ
and external measurement. T wo main groups of meas ure-
ment technique s are reviewed by Abi din et al . (2013).
Several issues regard ing the applications of the techniques
and possible ways to overcome the problems are discussed.
They conclude that laser-based systems provi de fast in-situ
measurements whi ch are useful for onli ne monitoring and
detecting process changes but are unable to deliver reliable
drop size and distrib ution values. In-situ ima ge analysis
techniques give accurate meas urement of drop size and, with
development of automated ima ge analysis, they can be used
for real-time monitoring an d process control.
In light of the studies and literatu re reports already
mentioned, the measurem ent of the DSD in highly concen-
trated emulsions was carried out with the reliable endoscope
technique in co mbination with image analysis ( Ritter and
Kraume, 2000 ), see Figure 1 for different example images
– for more details see ( www .sopat.eu ). They suggested a
minimum numbe r of 200 particles to form one sample for
one DSD as a reliable number for statistical demands ( Ritter
and Kraume, 1999 ). This numbe r was always exceeded by a
minimum facto r of ca. 4 for all investigated cases.
Figure 1 shows clearly the broad applicabil ity of the endo-
scope measurem ent technique for different partic ulate
systems. Different e ndoscopes with different lense s which
create different magni ﬁ cations were used to take these differ-
ent example images. The lens with the highest magni ﬁ cation
was used for the n -butyl chloride/ water system. The smal lest
measurable drop diameter with this lens is around 10 l m.
The largest screen size was achiev ed with the lens which
was used for the presented air/water system ( Fig. 1 ).
The diameter of the image was 8000 l m.
Particularly in fast coalescing systems, the quanti tative
size measurem ent of ﬂ uid disperse phases is a maj or chal-
lenge. In con trast to sampling, which is time-con suming
and involves the danger of adult eration, an in-situ working
analysis method wi th high spatial and temporal resoluti on
is selected to measure the DSD ( Schlüter , 201 1 ; Panckow
et al. , 2015 ).
1.2 Demands on
In-Situ
Devices
The photo-optical SOP A T measuring techni que for particle
sizing is capable of acquiring raw data (two-di mensional
images) of the disperse phase (in this study: dropl ets) during
the process and measure the sizes by means of automated
image analysis, see Figure 2 . The softwar e analysis proces s-
ing the various raw data obtainable utilizing the broad
variety of chemical apparatus is descri bed in detail in
Section 1.3 .
For precise image analys is, high quality images are neces-
sary . Therefor e the selected system consists of HD lenses,
either endoscopic o r microscopic ones. The o ptical lenses
are surrounded by a protection tube (material: stai nless steel
1.4571 or Hastelloy
Ò
C-22) and they represent the front part
of the probe. The atta ched housing protects a 6 megapixels
camera from water and dust ful ﬁ lling IP65 requirements.
As is known from microscopy , different magni ﬁ cations for
analysing different particle size ranges can be achieved by
switching the described probe manually . The highest magni-
ﬁ cation allows particle measurements starting at 900 nm
in terms of minim um measured diameter . By chang ing the
Oil & Gas Science and T echnology – Rev . IFP Ener gies nouvelles (2017) 72 ,1 4 Page 3 of 17

probe, a larger Field Of V iew (FOV) can be achieved. The
largest available FOV from the discussed in-situ probes is
24 mm. This allows particle analys es up to 9 mm in particle
diameter .
The camera is controlling a xenon ﬂ ash. The latter ensures
high illumination intensities in a very short time. The ﬂ ash
time varies, depending on the intensity , from 2 l s for
the lowest intensit y up to 8 l s for the highes t intensity .
Figure 1
Example image gallery: representative samples taken with the endoscope technique using different lenses according to the expected particle size
for different systems.
Figure 2
Photo-optical measuring method for particle size distributions.
Page 4 of 17 Oil & Gas Science and T echnology – Rev . IFP Energies nouvelles (2017) 72 ,1 4

This ensures sharp ima ge capture even from fast movi ng
particles. The ﬂ ash is inside the central box ( Fig. 10, right
in Sect. 3.1 ). The light from the stroboscope is transport ed
by a ﬁ bre-optic. This ensure s highly homogeneous illumina-
tion across the FOV . The ﬁ bre-optic is ﬂ exibl e but is
surrounded by a robust and IP65 certi ﬁ ed protective hose.
Using a diverse variety of ﬂ anges, the system can easily
be installed in pre-existing apparat uses. The probes tolerate
a process press ure up to 150 bar, however for speci al
applications even higher pressure c an be achieved. The
image analysis is carri ed out on a work station wi th four
Intel
Ò
Core
TM
i7 processors. The detai ls of the image
analysis softwar e are given in Secti on 1.3 .
1.3 Image Anal ysis Working Principle
V arious commerci al software packages are now available
that analyse images automaticall y and measure the size of
the droplets. The qualiti es of the results from these packages
differ and depend on a number of variables, includ ing the
quality of the initial images. Custom soft ware can be written
to incorporat e previously validated image proces sing
algorithms. The re is also software available allowing a
manual marking of droplets on images whi ch are then
measured by the computer . This process is extremely time
consuming, but generally consi dered essential for producing
accurate results as well as checking the perfor mance of
automatic software ( Brown et al. , 2004 ).
T o overcome the drawback of time consuming manual
quanti ﬁ cation using photo- optical methods with ima ge
analysis, a full y automated method based on MA TLAB
Ò
was implemented and put into practice. The softwar e
employs a Normal ised Cross-Correl ation (NCC) procedu re
algorithm, whi ch is explained in detail along with the pre-
ﬁ ltering which was employed by ( Maaß et al. , 2012 ).
Additionally , it avoids human generated bias by different
observers, also shown in ( Maaß et al. , 2012 ).
In order to ensure robust and accurat e drop detection, a
series of images is ﬁ rst pre- ﬁ ltered to remo ve irrelevant
and misleading image infor mation. This is done with image
subtraction using the integrate d sequence as difference
image (compare Fig. 3a and 3b ). The noise in the pictures
a) b)
c) d)
Figure 3
Image processing steps to remove redundant information and increase the ability to detect possible drop circles, ( Maaß et al. , 2012 ).
Oil & Gas Science and T echnology – Rev . IFP Ener gies nouvelles (2017) 72 ,1 4 Page 5 of 17

is reduced by the self-quotient image method ( Gopalan and
Jacobs, 2010 ). This operation norm s the intensity of every
local pixel based on the local environment. It is carried out
by division of the proces sed image ( Fig. 3b ) by a smoothed
version of itself, see Figure 3c after convol ution with a
Gaussian matrix.
Figure 3d show s the self-quotient ima ge which
emphasises the changes of the intensities from the original
image. This resul ts in an indepe ndence from any illumina-
tion or proces s variation.
Then the drop recognition follows. It consi sts of three
steps: pattern recogni tion by correlation of pre- ﬁ ltered
gradients with search samples, the pre-s election of plausible
circle coordinates , and the classi ﬁ cation of each of those
circles by an exact edge examinati on. The software employs
a NCC algorithm ( Lewis, 1995 ) to evalua te possible drop
matches, see Maaß et al. (2012) for more technical details
and results.
The approaches d escribed here are comparable to the
work of Rojas-Domíng uez et al. (2015) . They propose a
transformation of the denominated Gradient -Direction-
Pattern (GDP). The GDP is the describ ed pattern matching
algorithm which is currently only implem ented to ﬁ nd
circular patt erns. Therefore the existing ima ge analysis tech-
nology by SOP A T was further developed to take also irreg-
ular shaped particles into account ( Panckow et al. , 2015 ).
2 APPLICATION: FERMENT ATION BROTH
2.1 Chemical Systems and Operating Condition s
The model fermentation broth was obtained by fed-batch
cultivation un der similar condition s to those reported in
Cuellar et al. (2009) . The fermentat ion broth contained ca .
50 g L
 1
of E. coli K12 cell s on a dry wei ght basis, to which
ca. 10 vol% hexadecan e had been added. For the meas ure-
ment purposes of this paper a volume of 1-2 L of this mixture
was transferr ed to a 7 L Applikon bioreactor operating at a
stirrer speed of 300 rpm. When indicated, air was sparged
at a ﬂ ow rate of
_
V = 1.5 L min
 1
.
2.2 Speci ﬁ cations of the Installed Photo-Optic al Probe
The probe “ SOP A T MM ” used in the fermentation broth
system contains a high magni ﬁ cation microscopic lens
system projectin g an image from the inside of the bioreactor
with a cross section diameter (the FOV) of 385 l m. It is
transported onto the camera sensor , being 15.989 mm in
diagonal, with a resolution of 2752 9 2200 pixels, creating
a circular projectio n of the observed measurem ent volume,
see Figure 6 . T o convert the discr etised object s in pixels,
acquired at the camer a sensor , back to the unit of the real
particles, a convers ion factor of 0.175 l m/pixel has to be
used. The probe is able to d etect particles in the range of
ca .1 l mt o1 7 0 l m. It has a wet ted diameter of 24.5 mm
and a maximum wetted length of 320 mm with a ﬂ ow gap
located on its tip, see Figure 4 .
This ﬂ ow gap has an adjus table size with a maximum of
2 mm ranging down to 200 l m. The meas ured volume is
illuminated from the opposite side at the very end of the
tip of the probe by collim ated light, see Figure 5a .
This illu mination method by an opposing back light leads
to a projection of the photographed parti cles onto the sensor
as dark objects with stro ng contrast in front of a bright back-
ground. The control softwar e of the probe allows binning for
the camera sensor to be enabled, see the schematic drawing
in Figure 5b . By doing this, the values of a speci ﬁ ed numbe r
of pixels are added and their numbe r is set to one single
pixel. This results in a b righter image wi th a lower resolu-
tion. For example in Figure 5b , the binning 2 9 2 results
in a combination of 2 vertical and 2 horiz ontal pixels to
one single pixel that is four times as bright as (the average
of) the 4 native pixel s and the segment there by is reduced
in resolution by the same factor of 4.
Figure 4
Probe “ SOP A T MM ” with adjustable ﬂ ow gap used for in-situ
droplet size measurement in the fermentation broth.
a) b)
Figure 5
Probe speci ﬁ cation: a) illumination of the particles by transmis-
sion of collimated light, b) reducible resolution of the camera
sensor by binning.
Page 6 of 17 Oil & Gas Science and T echnology – Rev . IFP Energies nouvelles (2017) 72 ,1 4

2.3 Analytical Met hodology
The experiments perfor med and their corres ponding start
time (relativ e to the start of experiment e01) are shown in
T able 1 . V ariations were made on the gap and whether the
mixture was aerated or not.
For experiment e06 a reduced binning was used in order
to measure with a higher resoluti on than in the other
experiments. The resulting images, which are four times
darker than those with binning 2 9 2, were still bright
enough to be usable, since a very thin gap of 200 l m was
applied. By setting a gain, a furt her adjustment of the bright-
ness of the resulting output image is possible: by increasing
the gain, i.e. amplifyi ng the signal of the impinging photons
on the camera sensor, the background noise is increased as
well. However , in order to get a suf ﬁ cient brightness of the
resulting pictures, for the larger gap sizes in e01-e03, e05
a higher gain was applied. In the last two columns of T able 1
the absolute numbers of images, having been acquir ed and
used for the analysis of each experi ment, as well as the
counts of particles per singl e image (Detect ions Img
 1
) are
reported. The in ﬂ uence of the gap size as well as the
presence of another disperse phase besides the disperse
phase of interest (here: droplets) is presen ted in Figure 6 .
The images shown here represent a contr ast expanded
version of the acquired raw data, stretchi ng the brightest grey
value to whi te and the darkest grey value to b lack. Hereby ,
the images become visible for the human eye which is not
able to distinguish betwee n small differences in grey values.
Especially the image in Figure 6a would appear as pure grey
without contrast expansion. The se distorted images are only
intended for a clearer visualis ation and are not used by the
software, which is fed with the original ima ges as raw data.
In the images acquir ed with the big gap of 2 mm in
experiment e01, there is a low contrast resulting from the
refraction of light in the thick layer of fermentation broth
T ABLE 1
Experiments in the model fermentation broth system.
ID T ime (min) Gap (mm) Binning Gain Aeration Images Detections Img
 1
e01 0 2 2 9 2 14 Non-aerated 250 3.2
e02 18 1 2 9 2 14 Non-aerated 250 7.9
e03 22 1 2 9 2 14 Aerated 250 8.3
e04 32 0.5 2 9 2 3-5 Non-aerated 4 9 250 13.3
e05 60 1 2 9 2 12 Non-aerated 3 9 250 13.6
e06 87 0.2 1 9 1 0-9 Non-aerated 5 9 250 23.9
a) b) c)
Figure 6
Comparison of different gap sizes: a) e01 with 2 mm, b) e03 with 1 mm, c) e06 with 200 l m; all experiments have microbial cells and oil droplets
as disperse phases, b) e03 additionally had air bubbles.
Oil & Gas Science and T echnology – Rev . IFP Ener gies nouvelles (2017) 72 ,1 4 Page 7 of 17

leading to a high overal l diffuseness, see Figure 6a . There-
fore, after the contrast expansi on of the narrow -ranged grey
values, these images appear very noisy . The appearance of
small droplets is stro ngly weakened and the number of
droplets detected is low , which is why the setup in experi-
ment e01 is not suitable to detect a reliable DSD of the
disperse oil, although the absol ute number of detections
( N = 788) ful ﬁ ls the criterion of a minimum numbe r of
200 particles ( Ritt er and Kraume, 1999 ), see Secti on 1.1 .
W ith a smaller gap size, see Figures 6b and 6c , the contrast
increases and smaller dropl ets become visible. For the small-
est gap of 200 l m, the stru ctures of the cell agglom erates and
other componen ts ( e.g. dirt particles) becom e visible in the
emulsion. The y emerge as back ground structure s in the
dense (as compared to the other disperse phases) cell
emulsion, see Figure 6c .
These background structures as well as the bubbles in
e03, see Figure 6b , are not analysed in this study . Therefor e
the software was parametrised to ﬁ nd the dropl ets by
creating a pattern contai ning information describing the
appearance (grey values) of droplets in the SOP A T software,
see Section 1.3 for details about the soft ware algorithms for
particle recogniti on. Combined or separated size distrib u-
tions of different particle types can be produced by
parametrising the softwar e to detect additionally/only the
speci ﬁ c particle type ( i.e. droplets, bubbles and/or ce ll
agglomerates), see Figure 7 .
This mechanism for the detection of different disperse
phases is illustrated by the subtraction of two size distrib u-
tions: Figure 7a “ droplets + bubbles ” -distribution and
Figure 7b “ bubbles only ” -distribution. Eac h of the green
circles in the ﬁ gure mark s a particle detect ed by the auto-
mated softwar e with its corres ponding location and size on
the photograph. In the ﬁ rst step, the soft ware is parametrised
to detect the dropl et size distribution using a pattern repre-
senting the droplet appearance (grey values). Since the
bubbles cause a strong refraction of the light and result in a
high contrast projection on the camera sensor – resultin g in
a high quality pattern match – they are likely to be detected
by this ﬁ rst droplet patt ern as well, see Figure 7a . The result-
ing particle size distribution contains both the liquid droplets
and the gaseous bubble phase. By creating a second pattern
that represents the bubbles only a second distrib ution can
be obtained, see Figure 7b , containing no droplets at all since
their match quality (in compariso n to the bubble patt ern) is
too low . The subtr action of these two particle detections ,
“ droplets + bubble s ” -distribution and “ bubb les only ” -distri-
bution, resul ts in the de sired “ droplets only ” -distribution,
see Figure 7c . These tw o patterns for droplets and bubble s
do not lead to errone ous detections by misint erpretation of
the small cell stru ctures in the background, but only classify
the droplet and bubble phase, which are clear ly distinguish-
able from the cell s. This procedure of distingu ishing between
different disperse phases is possible as long as their optical
appearance (grey values, object borders, sizes ) are suf ﬁ -
ciently different. A basic prerequisite is the possibility of a
clear identi ﬁ cation and distinction with the human eye in
order to achieve a consistent training of the software and thus
a reliable size analysis. The detect ion of the cell size distribu-
tion is not part of this study . In order to measure it in the
investigated fermentat ion broth, very small gaps ( Fig. 6c
200 l m or smaller) have to be applie d to achieve an appropri-
ate visualisation – and thus ident i ﬁ cation by the human eye –
of the single cell stru ctures.
However , besides the advantages mentioned above when
choosing a small gap, the exclus ion of big droplets and an
a) b) c)
Figure 7
W orking principle to distinguish between different disperse phases, particle detections marked as green circles: a) detection of all objects corre-
sponding to the taught pattern, b) only detecting the high contrast bubbles c) subtraction of the detections a) and b) to get the size distribution of
only the droplets.
Page 8 of 17 Oil & Gas Science and T echnology – Rev . IFP Energies nouvelles (2017) 72 ,1 4

inhibited ﬂ ow throu gh the gap could occur, especially for
higher viscosities of the analysed emulsion.
For further discussion of the meas ured drop sizes in
Section 2.4 as well as Sections 3.2 and 3.3 the foll owing
formulas have been adapted. As the mai n representations
of the DSD, the de nsity distributions of numbe r q
0
and of
volume q
3
as well as the cumulative distrib utions of number
Q
0
and of volume Q
3
are used in this paper . The ﬁ rst
represent the likelih ood of the occurrence of a speci ﬁ c event
x (here: particle diameter d
P
) based on the type of quantity r
( i.e. r = 0 for number , r = 3 for volume). Their general
mathematical relationship is stated in Equation (1) .
Q r x i
ðÞ ¼
Z x i
x min
q r x ðÞ dx ð 1 Þ
In particle characteri sation the speci ﬁ c event x indi cates a
certain particle feature and could also represen t, for example,
circularity , colour , inner structure , membrane thic kness and
many others. In this paper, only the distributions of the
feature “ particle diameter d
P
” are addressed. As a commonly
used characteristic value in particle sizing, the Sauter Mean
Diameter (SMD ), see Equatio n (2) , relates the available
source/sink (volu me) to the means of transportation (area)
and therefore provi des an essential parameter when charac-
terising heat and mass transfer in ch emical processes.
d 3 ; 2 ¼ 6
a P
¼ X
N
i ¼ 1
d 3
P ; i . X
N
i ¼ 1
d 2
P ; i ð 2 Þ
Herein a
P
is the characteristic surfa ce area per unit
volume of the dispe rse phase and N is the tota l number of
particles. Using Equati on (2) to calculate the SMD , spherical
particles are assum ed. Besides averaged diameter s like the
SMD, percent iles d
rX
are used to charact erise distributions.
They indicate that the cumulative distribution exceeds a
certain percentage X of the total quan tity r at the given diam-
eter d . For examp le, a charact eristic diameter d
n 10
=1 5 l m
indicates that 10% of all particle diameter s are less than
15 l m and 90% are greater than or equal to 15 l m. Instead
of numbers, the type of quantity r is usually expressed in
letters ( n – number, v – volume) when using percentiles.
Often d
n 1
, d
n 99
or d
n 5
, d
n 95
or d
v 1
, d
v 99
are used instead of
the absolute minimum and maxi mum diameter of the distri-
bution due to stat istical outliers. When transforma tions from
the dimension lengt h to volume (or vice versa ) are made, the
droplets are always assumed to be spheric al.
2.4 Results and Discus sion
Since this study is focuse d on the size distribution of droplets
and there are only 15 bu bbles present on the 250 images of
e03 ( T ab. 1 ), it is not inten ded to present and discu ss the
“ bubbles only ” -distri bution, see Figure 7 in Section 2.3 .
The measured average sizes represented as SMD d
3,2
as well
as selected percentil es are shown in T able 2 .
In all of the experiments (except e06) a binning of 2 9 2
was used, see T able 1 . In spite of the signi ﬁ cant opacity of
the system under consideration, by using a binning it was
still possible to visualise the dispe rse phases, even for the
large gap size. W itho ut the effect of binning, the images
for experiments e01, e02 , e03 and e05 (gap  1 mm) would
have been too dark to analyse. The brighter and richer in
contrast the objects appear on the camer a sensor , the more
reliably the automati c image analys is algorithms can
operate: the probabi lity of pre-selection of a plausible circle
coordinate and class i ﬁ cation by exact edge examination
( Sect. 1.3 ) incre ases, and the probabi lity of false detections
decreases. As ment ioned in Section 2.3 , the setup of exper-
iment e01 has been deemed unsuitable for the system under
consideration. As can be seen by the values in T able 2 , e01
results in the narrowest distribution, having the maximum
values for d
n 1
, d
v 1
and the minimum values for d
n 95
, d
v 90
,
d
max
. The calculated values in T able 2 show that the very
small droplets could not be detected because of the high
diffuseness. This is due to the in ﬂ uence of the gap on the
T ABLE 2
Characteristic values measured in the model fermentation broth system.
ID d
3,2
( l m) d
n 1
( l m) d
n 95
( l m) d
v1
( l m) d
v90
( l m) d
max
( l m)
e01 64.87 21.42 81.20 29.40 109.39 151.96
e02 86.82 18.43 107.95 27.53 221.20 234.96
e03 77.23 18.38 98.59 26.13 149.57 212.51
e04 96.63 17.50 1 1 1.77 28.93 241.27 368.67
e05 106.10 18.43 120.17 33.60 243.13 325.73
e06 82.54 9.55 81.32 20.10 208.42 269.73
Oil & Gas Science and T echnology – Rev . IFP Ener gies nouvelles (2017) 72 ,1 4 Page 9 of 17

detection: a large gap wi ll result in bett er (more reali stic,
unhindered) ﬂ ow condit ions at the measurement point ,
but will produce more diffuse images, whereas a small
gap will result in clearer images that are richer in contrast,
but large droplets could be exclud ed by the constraine d
ﬂ ow through the gap. The gap in ﬂ uence is illustrated in
Figure 6 .
In T able 2 the two values d
v 90
and d
max
, both representing
the upper diam eters of the DSD, are compa red. The clearest
indication that d
max
is strongly in ﬂ uenced by outliers can be
seen in experiment e04. Only 18 particles have a diameter
above d
v 90
= 241.27 l m, one of them with d
P
=
d
max
= 368.67 l m. This in ﬂ uence arises from the fact that
a few large droplets have a signi ﬁ cant effect on the vol-
ume-based distrib ution, in this case 18 droplets cover 10%
of the volume of a ll detected droplets. The relevance of these
few large droplets becom es even more obvious when consid-
ering the number distrib ution: for the same experiment (e04)
with d
n 95
= 1 1 1.77 l m, the vast maj ority of droplets lie
below this value. This means only 5% (  667 particles) of
the droplets detect ed are bigger than 1 11.77 l m, with
one of these having the maximum diam eter d
max
 d
n 95
.
In Section 3.3 , a direc t comparison of Q
0
and Q
3
shed light
on the relationship between the number- and volum e-based
distributions.
These values indicate a longer right tail of the droplet size
distribution, see also Figure s 8a and 9a . The small numbe r of
the total particles N = 13,345 detected in e04 which strongly
in ﬂ uence the maximum diam eter demonstrates the relevance
of statistical interpretation considering outliers , and should
warn of misint erpretation of these values located at the very
end of the DSD. The appearance of these very large droplets
above the reliable detection limit of the probe type MM
(170 l m, Sect. 2.2 ) could indi cate a very broad distrib ution
and the presence of a higher number of big droplets which
are cut off in the measured distribution. This assum ption
has to be investigated in future studies by using a probe
for which the measurement range e nds signi ﬁ cantly above
400 l m.
a)
b)
Figure 8
Comparison of oil DSD in fermentation broth at different times:
e02 (18 min) and e05 (60 min) as a) density distribution q
0
and
b) cumulative distributio n Q
0
.
a)
b)
Figure 9
Ability to detect droplets below 20 l m by applying a thinner
gap: e06 (200 l m gap) compared to e05 (1 mm gap), plotted
as a) density distribution q
0
of number and b) cumulative distri-
bution Q
0
of number .
Page 10 of 17 Oil & Gas Science and T echnology – Rev . IFP Energies nouvelles (2017) 72 ,1 4

The in ﬂ uence of aeration on the drop sizes can be seen
from the calculated diam eters in Ta b l e 2 . Compar ing e02
with e03 after having increased the air ﬂ ow from
_
V = 0 L min
 1
to
_
V = 1.5 L min
 1
, all the cha racteristic
values are lower, resulting in a slight shift to smal ler diame-
ters. Furthermor e an increased SMD can be observed over
time by comparing experiment s e02 and e05, see T able 2 .
The repetition of experiment e02 as e05 with the same probe
con ﬁ guration show ed reasonable results considering the
range of drop sizes in the distrib ution, see Figure 8 .
It should b e kept in mind that the system was deli berately
disturbed by aerat ion (e03) in between experi ments e02 and
e05. A signi ﬁ cantly higher number of droplets were d etected
at the later time in e05, see T able 1 . The shift to bigger
droplets, resultin g in a higher SMD, may be the result of
advanced biol ogical conversion ove r time in combinati on
with the forced increase of transport proces ses by aeration
in e03.
In Figures 8 and 9 , the number of bins k is chosen to be
40. Following Sturges ’ Rule k = (1 + log
2
N ), the calculated
number of bins should be chosen such that k = 16 for the
maximum occurr ing particle count, viz . N = 29,861 in e06
( Sturges, 1926 ). Since Sturges ’ Rule is known to lead to
an over-smoothed histogram, especially for large samples,
and only considers normal, not skewed distributions ( Legg
et al. , 2013 ), a higher number was chosen. This also results
in a more representative Cumulative Distribution Functi on
(CDF) and leads to preser vation of the shape of both density
distributions in Figure 9 alth ough they span different inter-
vals. The width of the bins grows accordingly with base
10 resulting in equally broad app earing bars over a logarith-
mic scale.
The capacity of the system to detect very small droplets
by applying a thin gap can be seen when compa ring e05
and e06, see Figure 9 . In this ﬁ gure, the percent iles d
n 1
and d
n 95
from T able 2 are also plotted. By compa ring the
percentiles d
n 1
of both experiment s, the lowered min imum
of the detection range for the thin gap (200 l m, e06) can
clearly be seen. On the other hand, the big droplets seem
distorted when applyi ng a thin gap; compare percent iles
d
n 95
, Figure 9 as well as d
v 90
and d
max
in T able 2 .
The probe was able to measure the oil droplets in a broad
size range and, with the usage of a very thin gap, even
observe microbia l cell structures (see experi ment e06 in
Fig. 6c and T ab. 1 ) and to differentiate among gas bubbles,
see Figures 6b and 7 .
3 APPLICATION: PLATE DRO PLET SEPARATORS
3.1 Chemical Systems and Operating Condition s
For the investigation of the parameters ment ioned in the
introduction ( e.g. g
ﬂ
, u
ﬂ
) which in ﬂ uence the phase separa -
tion in gravity or plate dropl et separators, a labor atory plant
DN100 was bu ilt, see Figure 10 . It is operat ed in circulation.
The main part of the laboratory plant is a separator of
100 mm diameter and a length of 400 mm . It operates both
with and without plate internals . Frequency convert ers are
used to control the pump ﬂ ow rate of the main and dispersed
phases to ensure a constant volume ﬂ ow as well as a certain
phase fraction of the dispersed phase. Furthermore, the plant
consists of two more ﬁ bre bed phase separa tors to prevent
enrichment of non-separated droplets in eac h phase.
The DSD at the inlet of the phase separa tor is produced
mechanically by a special dispersin g unit and can be repro-
ducibly adjusted in the range 1 to 1000 microns. After a
steady state is reached in the separa tor (usually after
20 min) the in-situ meas urement of the DSD at the inlet
and outlet is recorded. Fo r these experiments, a photo-
optical probe ( “ SOP A T Sc ” ) with a measuring range of
12 to 1500 microns is used with an attached re ﬂ ection adap-
ter generating a ﬂ ow gap of 5 mm. A descri ption of how to
obtain an adequate adjustment of the latter is given in
Figure 10
Experimental setup for investigating the plate droplet separator .
Oil & Gas Science and T echnology – Rev . IFP Ener gies nouvelles (2017) 72 ,1 4 Page 1 1 of 17

Section 3.2 . T o verif y these measurements, the separation
ef ﬁ ciency was also determined volumetr ically . The ﬁ rst
dispersion tested consisted of water and sil icon oil, the other
system was a dispe rsion of water and paraf ﬁ n oil. T able 3
shows the physical properties of the utilized oils for the
disperse phases as wel l as their types and suppliers. For
the aqueous phases tap water was used. All experiments
were carri ed out at room temperat ure.
This investiga tion was initially focuse d on experiment s
with small amounts of dispersed phase to study only
sedimentatio n of droplet swarms with no major impact by
coalescence (which would be present at high volume
fraction).
3.2 Determinatio n of an Adequate Probe Con ﬁ guration
for One Targ et Operating Point
As for the fermentation broth system in Section 2 , the exper-
iments in the plate droplet separator were carried out using a
ﬂ ow gap at the point of measurem ent. The investigated
droplet sizes are larger than those in the fermentat ion broth
experiments, whi ch is why the SOP A T Sc probe, which
features a larger FOV , is employed. In general – depending
on the ﬁ neness of the dispersio n – the ﬂ ow gaps are also
larger in these experi ments than in the fermentat ion broth.
In Figure 1 1 , the illuminati on possibilities by attaching a
re ﬂ ection adapte r are illustrated .
W ith no re ﬂ ecti on adapter being atta ched, see Figure 1 1a ,
only the re ﬂ ected light at the interface of the particles is sent
back to the optical lens system. This method only enhances
each particle interface in the close vicinit y of the probe tip.
By applying a re ﬂ ection adapter, see Figure 1 1b , the light
that passed through the measurement volum e is directed
backwards to illuminate the scene from the other side. This
trans ﬂ ection metho d results in photog raphs with higher
contrast and accentua tes the smaller droplets which are
usually projected onto the camer a sensor with lower inten-
sity . In contr ast to the trans mission method, descri bed in
Section 2.2 , see Fig. 5a , the trans ﬂ ection method does not
result in bright ﬁ eld photography but is charact erised by a
dark background.
As indicated in earli er studies, wor king without a re ﬂ ec-
tion adapter could result in failure to identify very small
droplets, ( Maaß et al. , 2015 ). W ithout know ledge of the
measured volume ’ s size, a reliable statement about the
DSD is dif ﬁ cult. W ith higher diffuseness of the light in
the photographed volume, the inte nsity of the projections
of the particle interfaces is diminished. W ith a lowered
intensity (almost in the magnitud e of the background noise)
the software is set via threshold parameter s not to analyse
the faint objects in order to avoid erroneous detections .
Consequently , an investig ation of this in ﬂ uence on the mea-
sured DSD was undertaken in advance of those experiments
investigating the separa tion ef ﬁ ciency , see Figure 12 .
Herein the SOP A T Sc probe, mentioned above, is used at
the operating point of
_
V = 100 L h
 1
, u
d
= 1 vol% with
silicon oil as the disperse phase and water as the continuous
phase for the separator con ﬁ gurat ion with plate internals.
The dispersion unit for the representat ive operating point
chosen in this section is adjusted to produce a very ﬁ ne dis-
persion, since the effects on the resulting detections and
measured DSD are more obvious. The basic calculations
used to compute the DSD are the same a s those described
in Section 2.3 .
The units in Figure 12 describe the size of the pho-
tographed dropl ets in the syst em. In order to calculate back
to the originally projected object sizes on the camera sensor,
given in the camer a sensor relat ed unit of length (bein g
pixels), the convers ion factor 1.96 l m/p ixel has to be used.
The conversion from pixels to l m is dependent on the spe-
ci ﬁ cs of the lens system (optical magni ﬁ cation). In both
graphs the quanti ty of interest of all detected droplets, being
(a) volume and (b) number , is normalised to the respective
amount per ima ge (Img
 1
). In Figure 12a , a limit ation of
T ABLE 3
Speci ﬁ cations of the oils used as disperse phases.
q (kg m
 3
)a t2 5 ° C g
d
(mPa s) at 20 ° C T ype Supplier
Silicon oil 920 5.09 Element 14 PMDS Obermeier
Paraf ﬁ n oil 760 1.46 Pionier 1 137 Hansen & Rosenthal
a) b)
Figure 1 1
Illumination methods: a) re ﬂ ection and b) direct trans ﬂ ection
by use of a re ﬂ ection adapter .
Page 12 of 17 Oil & Gas Science and T echnology – Rev . IFP Energies nouvelles (2017) 72 ,1 4

the ordinate was chosen to yield a more detailed representa-
tion of the interesting range.
As can be seen in Figure 12a , an expect ed distribution of
droplets for the outlet related to its inle t DSD is observable.
Following the red outl et curve of a certain probe con ﬁ gura-
tion ( i.e .r e ﬂ ection adapte r gap size) it coinci des exactl y with
its blue inlet curve until ca . 60-80 l m. From a certa in point,
which corres ponds to the separation of the ﬁ rst small dro-
plets, the curves diverge up to the cut-o ff drop size at which
the red outlet curve ends. Meanin g, all droplets abov e are
entirely separa ted. In contrast to this fami liar trend, the
diagram in Figure 12b , representi ng the absolu te particle
number detected at the inlet and outl et, reveals an unex-
pected trend . For the trial wi th a ﬂ ow gap o f 2.5 mm
( Fig. 12b, solid lines ), the number of particles counte d at
the outlet is greater than the count at the inlet. According
to the explanation given in the beginn ing of this section, this
is a result of setting the gap of the re ﬂ ection adapter too large,
resulting in failure to ident ify the small particles at the inlet.
Therefore, at this operating point with dispersion unit pro-
ducing a very ﬁ ne dispersion, the gap should be set below
1.5 mm when the inte rest lies in the absolute number rather
than the vo lumetric relationship between inlet and outl et.
Having determin ed a distance of 1.5 mm for the ﬂ ow gap,
Figure 13 shows an accu rate correspondence between the
absolute volume density distributions of the inlet and outlet
for the inseparabl e droplets. Henc e, the fraction of droplets
in the range below d
P
 60 l m present at the inlet almost
completely reaches the outl et of the separator . No fraction
of droplets in the de tectable range of the probe seem s to
be lost in the meas ured volume, neithe r at the inlet nor the
outlet. The biggest droplets detected at this operating point
(approximatel y 1000 l m, see Fig. 12b and 13 ) are below
the maximum detect able particle size of the used probe,
viz . d
P
= 1500 l m, see Section 3.1 .
As a resul t of this study (preliminary to the investigation
of the separation ef ﬁ ciency), the obtai ned DSD are deeme d
reliable in the detectable range when using a certain gap size
(in the discussed case 1.5 mm) dependent on the operating
conditions.
3.3 Results and Discus sion
The volumetric separa tion ef ﬁ ciency a nd cut-off drop size
can be determined from the measurements of the inlet and
outlet DSD for each experiment. In this study the cut-o ff
drop size is the largest drop measured at the separator outlet,
which hence could not be separated. Based on these values
and the DSD, two examp les for different operating states
are compared. For each experi ment, 15,000-25,000 drops
were evaluated at 60-90 fram es in order to min imise possible
statistical inaccu racies. The basic calculations used to com-
pute the DSD are the same as those described in Section 2.3 .
Figure 13
Measured DSD for a gap size of 1.5 mm at the inlet and outlet
for
_
V = 100 L h
 1
, u
d
= 1 vol%.
a)
b)
Figure 12
Measured DSD for different gap sizes at the inlet and outlet for
_
V = 100 L h
 1
, u
d
= 1 vol%, plotted as a) absolute volume of
particles and b) absolute number of particles per image
(Img
 1
).
Oil & Gas Science and T echnology – Rev . IFP Ener gies nouvelles (2017) 72 ,1 4 Page 13 of 17

Representati ve results of the detection process are given
in Figure 14 .I n Figures 14a and 14b the p araf ﬁ n oil/water
system is shown, and Figures 14c and 14d show the silicon
oil/water system. For both systems, the outl et droplets are
clearly smaller in size and fewer in numbe r . The very shiny
objects in Figure s 14a and 14b are droplets, which stick to
the lens. These are not taken into account by the software,
since they do not move.
a) b)
c) d)
Figure 14
Representative images of the detection for
_
V = 200 L h
 1
, u
d
= 1 vol%, in the paraf ﬁ n oil/water system at the a) inlet and b) outlet and the silicon
oil/water system at the c) inlet and d) outlet.
b) a)
Figure 15
Comparison of the two systems for
_
V = 200 L h
 1
, u
d
= 1 vol% a) cumulative DSD Q
0
of number , b) cumulative DSD Q
3
of volume.
Page 14 of 17 Oil & Gas Science and T echnology – Rev . IFP Energies nouvelles (2017) 72 ,1 4

Figure 15 shows the in ﬂ uence of the density difference
on the separation behavi our in the gravity separator .
For this purpose the DSD of the tw o systems, paraf ﬁ n
oil/water and silicon oil/w ater ( Fig. 14 ), are compa red
under the same plant operating conditions. The dispe rsion
unit is adjusted to produce a rather coarse dispersio n.
Analogous to the procedure in Section 3.2 ,a ﬂ ow gap of
5 mm was deter mined for this larger dropl et regime and
applied in the separation ef ﬁ ciency experiment s described
in this section.
The photo-optical measurem ents clearly indicate that
a lower density difference (and thus lower droplet velocities)
results in slightly larger droplets at the outlet. This result
is more obvious when compa ring the volume distributions
Q
3
. In fairly broad distributions, as in these experiment s,
the larger droplets have a major impact on the volumetric
separation. For bett er observation, the distrib utions are there-
fore plotted on a linear scale. Since the DSD at the separator
inlet in ﬂ uences the volumetr ic separation, it has been
adjusted to be as similar as possible.
T ABLE 4
Results of the separation quality for the two investigated systems.
Dispersed/main phase D q (kg m
 3
) Separation ef ﬁ ciency
(vol%) SOP A T
Separation ef ﬁ ciency
(vol%) volumetrically
Cut-off drop size (vol%)
SOP A T
Silicon oil/water 70 32 33 290
Paraf ﬁ n oil/water 233 53 54 256
a) b)
c) d)
Figure 16
Representative images of the detection for the water/sili con oil system,
_
V =5 0Lh
 1
, u
d
= 5 vol% without plate internals at the a) inlet and b)
outlet and with plate internals at the c) inlet and d) outlet.
Oil & Gas Science and T echnology – Rev . IFP Ener gies nouvelles (2017) 72 ,1 4 Page 15 of 17

T able 4 shows that for these experi ments with a low
disperse phase fraction, a very good corres pondence
between the volumetr ic degrees of separa tion could be
achieved. As expect ed, the separation ef ﬁ ciency decreases
with a reduction in the density difference, while the cut-off
drop size increases.
As previously descri bed, internals have a positive in ﬂ u-
ence on the separation ef ﬁ ciency . They reduce the settli ng
distance, equali ze the ﬂ ow distributions and provide a
surface for coales cence. Consequently , results in one of the
systems (water/silicon oil with water being the dispersed
phase) for two con ﬁ gurat ions, with and without plat e
internals, are compa red. The operating conditions were kept
the same as before. The plat es have a distance of 7 mm and
the length of the plate package in the ﬂ ow direction is
210 mm. As shown in Section 3.2 for smal l droplet fractions,
the measuring gap between the probe tip and the re ﬂ ecting
surface has to be reduced for experiment s with a higher
concentration of the disperse phase to minimise the like li-
hood of failure to identify small dropl ets. The adjusted gap
of 5 mm ful ﬁ lls this requirement for the coarse dispersion
and u
d
= 5 vol%.
Figure 16 shows represen tative images of the detection
for the water/silicon oil system at the operating po int
_
V =5 0Lh
 1
, u
d
= 5 vol% . When comparing the inlet dis-
tribution, see Figure s 16a and 16b , with the silicon oil/water
system at the operating point
_
V = 200 L h
 1
, u
d
= 1 vol%,
see Figure 14c , a coarser distrib ution shifted to larger droplet
diameters c an be seen in the experi ments with water as the
disperse phase. Furthe rmore, the fact that the plate internals
result in a higher separa tion ef ﬁ ciency is clear ly observable
by comparing the outl ets, Figures 16b and 16d .
Figure 17 shows the compa rison of inlet and outlet DSD.
Similarly for these experi ments, the DSD at the inlet
was adjusted to be as similar as possible. It is clearly visible,
particularly in the volume distribution Q
3
, that the size of the
droplets at the separa tor outlet can be reduced signi ﬁ cantly
by the inclusio n of plate inte rnals. This mainly affects the
volumetric separation ef ﬁ ciency .
Under the operating condit ions above, the photo-opti cally
obtained separa tion ef ﬁ ciencies are in accordance with those
volumetrically measured, see T able 5 .
CONCLUSION
The experiments carried out in the separa tion applicati on
clearly showed a big in ﬂ uence of the densi ty difference as
well as the presence of plate inte rnals on the separation ef ﬁ -
ciency . The effect of both parameters on the change of the
DSD from inle t to outlet was observed by a photo-optical
method. Exa mining its reliabilit y with volum etric measure-
ments show ed very good agreem ent.
The photo-opti cal measuring method shows potential for
DSD measurement during microbial fermentat ions, since it
detects a broad range of droplet sizes and allows differenti-
ation from other disperse compo nents such as microbia l cells
and bubbles. In the model mixture tested, the SMD d
3,2
ranged between 70 and 110 l m, while in actual fermenta-
tions for the product ion of biofuels expect ed mean droplet
a) b)
Figure 17
Comparison for the water/silicon oil system,
_
V =5 0Lh
 1
, u
d
= 5 vol% with and without plate internals, a) cumulative DSD Q
0
of number ,
b) cumulative DSD Q
3
of volume.
T ABLE 5
Results of the separation quality for the water/silicon oil system for two
con ﬁ gurations, with and without plate internals.
Separation
ef ﬁ ciency
(vol%) SOP A T
Separation
ef ﬁ ciency
(vol%)
volumetrically
Cut-off drop
size (vol%)
SOP A T
W ithout plates 68 69 490
W ith plates 90 95 300
Page 16 of 17 Oil & Gas Science and T echnology – Rev . IFP Energies nouvelles (2017) 72 ,1 4

sizes are ca. 10 l m. Ongoing research will focus on strate-
gies for prom oting droplet coales cence and avoidi ng droplet
stabilisation during ferm entation.
It could be shown clearly that the demon strated particle
measurement syst em precisely determined the in ﬂ uence of
the varied parameters on the separation ef ﬁ ciency . It is able
to perform well even in very opaque media like ferm entation
broth. Addi tionally , it could not only detect the required
DSD, but was also able to distinguish between different dis-
perse phases, such as disturbing bubbles, yeast agglomerates
and dirt particles . As the results are available inline and in
real-time, a closed contr ol loop can be established in the
future.
ACKNOWLEDGME NTS
W e would like to thank Susana Pedraza de la Cuesta and
Caroline van Houten from Delft Uni versity of T echnology
for preparing the fermen tation broth and for their assistance
during the droplet size meas urements.
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Manuscript submitted in October 2016
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Oil & Gas Science and T echnology – Rev . IFP Ener gies nouvelles (2017) 72 ,1 4 Page 17 of 17

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