Data-driven evolutionary optimisation for the design parameters of a chemical process: a case study
No Thumbnail Available
Date
2021
Authors
Stander, Liezl
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
The optimisation of Chemical plant design and operation has proven to be challeng ing due to the complexity of real-world systems. The complexity translates into high com putational costs from the mathematical representations and simulation models for these
systems. Research has illustrated the benefits of using surrogate models as substitutes for
computationally expensive models. This research investigates two main concepts. The
first one being the resource cost reduction when implementing surrogate assisted genetic
algorithms for the optimisation of computationally expensive simulation models repre senting chemical systems. The second component focuses on determining the robustness
of these algorithms towards stochastic and multi-objective systems. Two main algorithms
were developed to optimise four different chemical plant systems. The Chemical Plant
System - Basic (CPS-B) is a stochastic chemical process including buffer tanks, process ing units, and a tank with multiple feed streams. The Chemical Plant System - Parallel
(CPS-P) and Chemical Plant System - Feedback (CPS-F) are more complex variants of the
CPS-B introducing additional complexities in the form of parallel and feedback loop sys tems respectively. The Surrogate Assisted Genetic Algorithm (SA-GA) was used to opti mise these three systems. The SA-GA algorithm was adapted for multi-objective optimi sation. The new adapted algorithm called the Surrogate Assisted NSGA-II (SA-NSGA) algo rithm was tested on a popular literature case, the Pressure Swing Adsorption (PSA) system.
The optimisation results for all the chemical systems illustrated that using Genetic Algo rithms as an optimisation framework for complex stochastic, single and multi-objective
chemical plant systems results in significant computational benefits. Introducing Ma chine Learning Surrogate models as substitutes for computationally expensive simulation
models into a Genetic Algorithm framework yielded significant computational efficiency
improvements. The optimisation of CPS-B, CPS-P, CPS-F, and PSA achieved 1.8, 1.74, 1.95,
and 5 times speedup of the total elapsed run time, despite the increased complexity in
the systems. It is worth noting that the SA-GA and SA-NSGA algorithms implemented in
this research yielded results confirming both their flexibility and robustness towards more
complex stochastic, single and multi-objective systems.
Description
A research report submitted in partial fulfilment for the degree of Master of Science in Computer Science to the Faculty of Science, School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, 2021
Keywords
Citation
Stander, Liezl (2021) Data-driven evolutionary optimization for the design parameters of a chemical process:a case study, University of the Witwatersrand, Johannesburg, <http://hdl.handle.net/10539/32439>