Surrogate Assisted Evolutionary Multi-objective Optimisation applied to a Pressure Swing Adsorption system
preprintposted on 2022-04-08, 03:56 authored by Terence van ZylTerence van Zyl, Liezl Stander, Matthew Woolway
The complexity of Chemical Plant Systems (CPS) makes optimising their design and operation challenging tasks. This complexity also results in analytical and numerical simulation models of these systems having high computational costs. Research demonstrates the benefits of using machine learning models as surrogates or substitutes for these computationally expensive simulation models during CPS optimisation. This paper presents the results of our study, extending recent research into optimising chemical plant design and operation. The study explored the original Surrogate Assisted Genetic Algorithms (SA-GA) in more complex variants of the plant design and operation optimisation problem. The more complex plant design variants include additional parallel and feedback components. The study also proposes a novel multivariate extension, Surrogate Assisted NSGA (SA-NSGA), to the original univariate SA-GA algorithm. The study evaluated the SA-NSGA extension on the popular Pressure Swing Adsorption (PSA) system. This paper outlines our extensive experimentation, comparing various meta-heuristic optimisation techniques and numerous machine learning models as surrogates. The results in both more complex plant design variants and the PSA case show the suitability of Genetic Algorithms combined with surrogate models as an optimisation framework for CPS design and operation in single and multi-objective scenarios. The analysis further confirms that combining a Genetic Algorithm framework with Machine Learning Surrogate models as a substitute for long-running simulation models yields significant computational efficiency improvements, 1.7 - 1.84 times speedup for the increased complexity examples and a 2.7 times speedup for the Pressure Swing Adsorption system. The discussion successfully concludes that surrogate assisted Evolutionary Algorithms can be scaled to increasingly complex CPS with parallel and feedback components.