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dc.contributor.authorHao, Z
dc.contributor.authorZhang, C
dc.contributor.authorLapkin, Alexei
dc.date.accessioned2022-02-09T13:21:46Z
dc.date.available2022-02-09T13:21:46Z
dc.date.issued2022-06
dc.date.submitted2021-08-09
dc.identifier.issn0001-1541
dc.identifier.otheraic17616
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/333779
dc.descriptionFunder: Chinese Scholarship Council
dc.descriptionFunder: Cambridge Trust; Id: http://dx.doi.org/10.13039/501100003343
dc.descriptionFunder: National Research Foundation Singapore, CREATE: CARES, C4T Project
dc.description.abstractAbstract: 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.
dc.languageen
dc.publisherWiley
dc.subjectPROCESS SYSTEMS ENGINEERING
dc.subjectdata quality
dc.subjectdigital twins
dc.subjectdynamic sampling rate
dc.subjectgas‐to‐liquids
dc.subjectmachine learning
dc.subjectpressure swing adsorption
dc.titleEfficient surrogates construction of chemical processes: Case studies on pressure swing adsorption and gas-to-liquids
dc.typeArticle
dc.date.updated2022-02-09T13:21:45Z
prism.publicationNameAIChE Journal
dc.identifier.doi10.17863/CAM.81196
dcterms.dateAccepted2022-01-15
rioxxterms.versionofrecord10.1002/aic.17616
rioxxterms.versionAO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidLapkin, Alexei [0000-0001-7621-0889]
dc.identifier.eissn1547-5905
cam.issuedOnline2022-02-08


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