Supercritical CO2 cycles in combined-cycle power systems [original]

Supercritical CO2 cycles in combined-cycle power

systems: multicriteria evaluation and

exergoeconomic optimization

vorgelegt von

M.Sc.

Mohamed Bahaa Noaman

an der Fakultät III – Prozesswissenschaften

der Technischen Universität Berlin

zur Erlangung des akademischen Grades

Doktor der Ingenieurwissenschaften

- Dr.-Ing. -

genehmigte Dissertation

Promotionsausschuss:

Vorsitzender: Prof. Dr. Thomas William Brown

Gutachterin: Prof. Dr. Tetyana Morozyuk

Gutachter: Prof. Dr.-Ing. George Tsatsaronis

Gutachter: Prof. Dr. Sergio Mussati

Tag der wissenschaftlichen Aussprache: 15. Dezember 2021

Berlin 2022

Intentionally Blank Page

Acknowledgement

ACKNOWLEDGEMENT

I would like to express my deepest gratitude to my supervisors Prof. Tetyana Morozyuk and Prof.

George Tsatsaronis, for their guidance, continuous support, and knowledge. Thanks for supporting me

not only on an academic but also on a personal level. I really appreciate the opportunities they have

opened in front of me to be able to attend many valuable scientific conferences and publish my work in

leading academic journals. I am also grateful for becoming part of TU Berlin Campus El-Gouna as a

research associate at the Energy Engineering department after completing my master’s degree at the

same place back in 2014. The personal experience and knowledge I have gained both as a student and

research associate at El-Gouna and Berlin are invaluable.

I would also like to acknowledge the financial support I have received on my current position as part of

the Transnational Education (TNB) Project (ID 57128418) from the German Academic Exchange

Service (DAAD), funded by the Federal Ministry of Education and Research (BMBF). Many thanks to

Eng. Samih Sawiris and those who got involved from his company Orascom Development and from

TU Berlin for funding and initiating TU Campus El-Gouna.

My special thanks to my colleagues and friends in Germany and Egypt, who have always been a source

of mental support and happiness. In particular, my lifetime friend Louay who is also my current

colleague and a continuous source of assistance professionally and personally. Also, I would like to

acknowledge and thank my current and former colleagues, especially those whom I had the pleasure to

work with closely and contributed directly or indirectly to my academic progress: Sarah Hamdy, Sara

Al Ahmad, Christoph Banhardt, Timo Blumberg, Jing Luo, Jimena Incer, Johannes Wellmann, Jörg

Rüdiger, Nico Dabelstein, Saeed Sayadi, Eko Primabudi, Elisa Papadis, Renzo Castillo, Christina

Stahlbock, Ina Hohenhaus, Daniel Wolf, Prof. Szyszka, Olga Tcvetkova, and Detlef Riebow. I would

also like to thank our former master thesis students, whom I supervised, and who assisted in my

research: Omar Awad, Mohamad Al-Shurbaji, Meet Dholakia, Loretta Toma, and Mahmoud El-Sheikh.

I am also thankful to have the chance to attend the technology management class with Prof. Søren

Salomo and apply the knowledge acquired on “sCO2 cycle technology”. Also, many thanks to Tahar

Nabil and his company EDF for making our remote collaboration on the “application of machine

learning on sCO2 bottoming cycles” possible. Thanks as well to the “Ebsilon Professional” team for

their support whenever needed.

The completion of this dissertation could not have been possible without the strength and perseverance

that God provided me, the daily support of my wife Andrea, and the continuous encouragement from

my relatives and members of the Noaman, Al-Fransawy, and the Cabañero Spapens families, especially

the inner spiritual energy I constantly receive through my parents and brothers. In the end, I would like

to dedicate this work to my dad and all the beautiful souls that have left us during the current

pandemic—looking forward to reuniting again.

III

Intentionally Blank Page

Abstract

ABSTRACT

The use of supercritical carbon dioxide (sCO2) cycles in waste heat to power (WHP) applications,

specifically in combined-cycle power systems, can help industrial and power generation facilities save

money and increase efficiency. According to the literature, the main hindrance facing the

commercialization of these systems is their relative novelty compared to other well-established power

systems like the steam and organic Rankine cycles. This thesis aims to reveal and present detailed

economic and thermodynamic features and characteristics of these sCO2 power cycles to help increase

the market stakeholders' confidence in such a new technology. Hence, accelerate its promotion and

application in established facilities and new projects.

Therefore, five main components were implemented and reported in this work: (1) Review and

statistical analysis of a large pool of recently published academic articles and technical reports that have

focused on the economic and technical performance of sCO2 power cycles when applied to waste heat

recovery; (2) Implementing an economic analysis for a base-model and four selected case studies,

including details usually overlooked in the literature. Also, performing an extensive sensitivity analysis

for the base-model to give a holistic overview on the levelized cost of electricity (LCOE) and the

specific investment cost (SIC) range of sCO2 bottoming cycles; (3) Exergoeconomic analysis and

iterative optimization of selected sCO2 bottoming cycles to a fixed topping gas turbine (GT). Hence,

revealing the main thermodynamic inefficiencies and identifying all possible measures that could be

applied for cost reduction without any remarkable negative effects on the system performance; (4)

Utilisation of a machine learning (ML) algorithm to demonstrate the benefits of applying predictive-

analytical IT solutions in optimization and selection of sCO2 bottoming cycle layouts while varying the

topping gas turbine (three different GT sizes); (5) Also, as part of the economic analysis of sCO2 power

cycles, three different market scenarios were developed for the first time using a “technology foresight”

approach.

The main outcomes that were concluded out of these five main study components were the following:

(1) Out of 73 reviewed publications, 41 unique sCO2 bottoming cycle layouts were identified and

categorised into five groups depending on the configuration and complexity of each cycle. A wide range

of LCOE values (24 to 70 $/MWhe) was identified and extracted from this reviewed literature; (2) The

economic analysis helped estimate the investment cost of sCO2 bottoming cycles at a relatively low

uncertainty range around +/-30%. Using different economic calculation methods showed a 30%

variation in LCOE values of the base-model. Also, the sensitivity analysis showed the influence of

varying the system variables on LCOE and SIC results of the base-model. Then, two estimation charts

were developed and introduced to show the expected SIC, LCOE, and net power range from the four

selected sCO2 bottoming cycles in relation to the base-model; (3) The iterative exergoeconomic

optimization showed that most of the room available for improving the cost-effectiveness of the sCO2

bottoming cycle lies mainly in the design of its heaters and recuperators. Also, it revealed that the SIC

of the system could be reduced much easier than its LCOE, while the rate of decrease in LCOE is

slightly higher than the reduction in generated power; (4) The ML tool confirmed the relative superiority

of the sCO2 partial heating and triple heating cycle layouts, yet this outcome depends on the topping

GT size and the quality of the cycles generated by the ML algorithm. Also, implementing the fitness

function for the ML algorithm using exergoeconomics helped achieve the optimal economic solutions

of the sCO2 bottoming cycles at high accuracy; (5) Lastly, using the “technology foresight” approach,

three possible future market scenarios for the sCO2 power cycle technology were developed and

reported.

Loading more pages...