Efficient surrogates construction of chemical processes: Case studies on pressure swing adsorption and gas-to-liquids
Publication Date
2022Journal Title
AIChE Journal
ISSN
0001-1541
Publisher
Wiley
Language
en
Type
Article
This Version
AO
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Hao, Z., Zhang, C., & Lapkin, A. (2022). Efficient surrogates construction of chemical processes: Case studies on pressure swing adsorption and gas-to-liquids. AIChE Journal https://doi.org/10.1002/aic.17616
Description
Funder: Chinese Scholarship Council
Funder: Cambridge Trust; Id: http://dx.doi.org/10.13039/501100003343
Funder: National Research Foundation Singapore, CREATE: CARES, C4T Project
Abstract
Abstract: We propose a sequential sampling approach to training statistical digital twins. This approach is relevant for real‐world engineering problems with expensive data generation. Prerequisite for building surrogates is sufficient data; however, oversampling does not improve regression accuracy. The time for data generation may be reduced by: (a) applying a classifier to improve data quality and avoid evaluation of infeasible inputs, and (b) employing dynamic sampling linked to regression quality. In dynamic sampling, the initial sampling rate is large to generate enough data for surrogate regression in a few iterations; the sampling rate gradually slows down with the improvement of the iteratively refined surrogate. A dynamic process and a steady‐state process from the field of carbon capture and utilization are used as case studies: pressure swing adsorption (PSA) and gas‐to‐liquids (GTL). The computational costs for surrogates generation are reduced by 86% for PSA and 51% for GTL, compared with employing a static sampling rate.
Keywords
PROCESS SYSTEMS ENGINEERING, data quality, digital twins, dynamic sampling rate, gas‐to‐liquids, machine learning, pressure swing adsorption
Identifiers
aic17616
External DOI: https://doi.org/10.1002/aic.17616
This record's URL: https://www.repository.cam.ac.uk/handle/1810/333779
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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