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Surrogate Assisted Evolutionary Multi-objective Optimisation applied toa Pressure Swing Adsorption system
  • Terence van Zyl ,
  • Liezl Stander ,
  • Matthew Woolway
Terence van Zyl
Unversity of Johannesburg, Unversity of Johannesburg

Corresponding Author:[email protected]

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Liezl Stander
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Matthew Woolway
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Abstract

Research has illustrated the benefits of using machine learning surrogate models as substitutes for computationally expensive models during optimisation. This paper extends recent research into optimising chemical plant design and operation. The study further explores Surrogate Assisted Genetic Algorithms (SA-GA) in more complex variants of the original plant design and optimisation problems, such as the inclusion of parallel and feedback components.