Comparative assessment of simulation-based and surrogate-based approaches to flowsheet optimization using dimensionality reduction
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
This work proposes a framework for simulation-based and surrogate-based reduced space Bayesian optimization of process flowsheets. The framework uses global sensitivity analysis for dimensionality reduction via the identification of critical process variables that contribute significantly to the variability of the objective function (e.g. productivity and operating costs). Both simulation- and surrogate-based algorithms are applied to a biopharmaceutical and a chemical process simulator for the production of plasmid DNA and dimethyl ether (DME), respectively. Their capabilities are assessed in terms of the trade-off between computational effectiveness and solution accuracy. Results indicate that simulation-based Bayesian optimization achieves better objective function values, while surrogate-based Bayesian optimization is more computationally effective.
Date Issued
2024-10
Date Acceptance
2024-07-10
Citation
Computers and Chemical Engineering, 2024, 189
ISSN
0098-1354
Publisher
Elsevier
Journal / Book Title
Computers and Chemical Engineering
Volume
189
Copyright Statement
© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
http://dx.doi.org/10.1016/j.compchemeng.2024.108807
Publication Status
Published
Article Number
108807
Date Publish Online
2024-07-20