Data-driven surrogate modeling and benchmarking for process equipment
File(s)
Author(s)
Type
Journal Article
Abstract
In chemical process engineering, surrogate models of complex systems are often necessary for tasks of domain exploration, sensitivity analysis of the design parameters, and optimization. A suite of computational fluid dynamics (CFD) simulations geared toward chemical process equipment modeling has been developed and validated with experimental results from the literature. Various regression-based active learning strategies are explored with these CFD simulators in-the-loop under the constraints of a limited function evaluation budget. Specifically, five different sampling strategies and five regression techniques are compared, considering a set of four test cases of industrial significance and varying complexity. Gaussian process regression was observed to have a consistently good performance for these applications. The present quantitative study outlines the pros and cons of the different available techniques and highlights the best practices for their adoption. The test cases and tools are available with an open-source license to ensure reproducibility and engage the wider research community in contributing to both the CFD models and developing and benchmarking new improved algorithms tailored to this field.
Date Acceptance
2020-07-25
Citation
Data-Centric Engineering, 1
ISSN
2632-6736
Publisher
Cambridge University Press
Journal / Book Title
Data-Centric Engineering
Volume
1
Copyright Statement
© The Author(s), 2020. Published by Cambridge University Press in association with Data-Centric Engineering
License URL
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Identifier
https://www.cambridge.org/core/journals/data-centric-engineering/article/datadriven-surrogate-modeling-and-benchmarking-for-process-equipment/6B063F6486E7C6F7D7D897355A6CE084
Grant Number
EP/T000414/1
Subjects
cs.CE
cs.CE
cs.LG
physics.flu-dyn
stat.ML
Publication Status
Published
Article Number
e7
Date Publish Online
2020-09-04