Imperial College London
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Professor Omar K. Matar, FREng

Faculty of Engineering, Department of Chemical Engineering

Head of Department of Chemical Engineering
 
 
 
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Contact

 

+44 (0)20 7594 9618o.matar Website

 
 
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Assistant

 

Mr Avery Kitchens +44 (0)20 7594 6263

 
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Location

 

305 ACEACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Gonçalves:2020:10.1017/dce.2020.8,
author = {Gonçalves, GFN and Batchvarov, A and Liu, Y and Liu, Y and Mason, LR and Pan, I and Matar, OK},
doi = {10.1017/dce.2020.8},
journal = {Data-Centric Engineering},
title = {Data-driven surrogate modeling and benchmarking for process equipment},
url = {http://dx.doi.org/10.1017/dce.2020.8},
volume = {1},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Gonçalves,GFN
AU  - Batchvarov,A
AU  - Liu,Y
AU  - Liu,Y
AU  - Mason,LR
AU  - Pan,I
AU  - Matar,OK
DO  - 10.1017/dce.2020.8
PY  - 2020///
SN  - 2632-6736
TI  - Data-driven surrogate modeling and benchmarking for process equipment
T2  - Data-Centric Engineering
UR  - http://dx.doi.org/10.1017/dce.2020.8
UR  - https://www.cambridge.org/core/journals/data-centric-engineering/article/datadriven-surrogate-modeling-and-benchmarking-for-process-equipment/6B063F6486E7C6F7D7D897355A6CE084
UR  - http://hdl.handle.net/10044/1/94872
VL  - 1
ER  -
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