Duanmu, F; Chia, DN; Sorensen, E; (2023) A novel stochastic optimization software for the optimal design of chemical processes modeled in commercial simulation software. In: Proceedings of The Foundations of Computer-Aided Process Operations and Chemical Process Control (FOCAPO / CPC 2023). Foundations of Computer Aided Process Operations / Chemical Process Control: San Antonio, TX, USA.

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Abstract

The design and optimization of a chemical process is often done using commercial software. Current commercial simulation software either only have Non-linear Programming (NLP) optimization functionalities, or the built-in Mixed Integer Non-linear Programming (MINLP) optimizer cannot efficiently handle complex designs, even though the optimization of a chemical process is typically a highly non-convex MINLP problem. Therefore, in this work, a novel stochastic optimization software – StOp – is presented. StOp has a simple user interface and can communicate with commercial simulation software in solving the optimization using stochastic methods coded within StOP (currently Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, and Fast and Elitist Nondominated Sorting Genetic Algorithm), with the process model expressed in the commercial software. StOp also has key functional features such as parallel computing, dynamic bounds, a timeout function, and saving good solutions, which are customized for the optimization of chemical processes. The software is illustrated by considering the optimization of a three distillation column superstructure.

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