Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/161990
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dc.contributor.authorTanudjaja, Henry Jonathanen_US
dc.contributor.authorChew, Jia Weien_US
dc.date.accessioned2022-09-28T05:10:25Z-
dc.date.available2022-09-28T05:10:25Z-
dc.date.issued2022-
dc.identifier.citationTanudjaja, H. J. & Chew, J. W. (2022). Application of machine learning-based models to understand and predict critical flux of oil-in-water emulsion in crossflow microfiltration. Industrial and Engineering Chemistry Research, 61(24), 8470-8477. https://dx.doi.org/10.1021/acs.iecr.1c04662en_US
dc.identifier.issn0888-5885en_US
dc.identifier.urihttps://hdl.handle.net/10356/161990-
dc.description.abstractRandom Forest (RF) and Neural Network (NN), respectively, were employed to understand and predict the critical flux (Jcrit) of oil-in-water emulsions in crossflow microfiltration. A total of 223 data sets from various studies were compiled, with nine operational parameters and one target variable of critical flux. RF indicated crossflow velocity (CFV) as the most dominant parameter in determining critical flux, outweighing surfactant and oil variations. Exceptions were found in specific cases when casein concentration was the most dominant, since the smaller sizes of casein significantly decreased Jcrit. The NN model predicted the best when all nine input parameters were integrated and the worst when CFV was the sole parameter used for model development, even though CFV was identified as the most dominant. The results here demonstrate the usefulness of machine learning tools to enhance the understanding on and prediction of critical flux without any governing equations.en_US
dc.description.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.language.isoenen_US
dc.relationA20B3a0070en_US
dc.relationA2083c0049en_US
dc.relation2019-T1-002-065en_US
dc.relationRG100/19en_US
dc.relationMOE-MOET2EP10120-0001en_US
dc.relation.ispartofIndustrial and Engineering Chemistry Researchen_US
dc.rights© 2022 American Chemical Society. All rights reserved.en_US
dc.subjectEngineering::Chemical engineeringen_US
dc.titleApplication of machine learning-based models to understand and predict critical flux of oil-in-water emulsion in crossflow microfiltrationen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Chemical and Biomedical Engineeringen_US
dc.contributor.researchNanyang Environment and Water Research Instituteen_US
dc.contributor.researchSingapore Membrane Technology Centreen_US
dc.identifier.doi10.1021/acs.iecr.1c04662-
dc.identifier.scopus2-s2.0-85125227564-
dc.identifier.issue24en_US
dc.identifier.volume61en_US
dc.identifier.spage8470en_US
dc.identifier.epage8477en_US
dc.subject.keywordsCrossflow Microfiltrationen_US
dc.subject.keywordsMachine-Learningen_US
dc.description.acknowledgementWe acknowledge funding from the A*STAR (Singapore) Advanced Manufacturing and Engineering (AME) under its Pharma Innovation Programme Singapore (PIPS) program (A20B3a0070), A*STAR (Singapore) Advanced Manufacturing and Engineering (AME) under its Individual Research Grant (IRG) program (A2083c0049), the Singapore Ministry of Education Academic Research Fund Tier 1 Grant (2019- T1-002-065; RG100/19), and the Singapore Ministry of Education Academic Research Fund Tier 2 Grant (MOEMOET2EP10120-0001).en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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