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dc.contributor.authorHu, Jiahui
dc.contributor.authorKim, Changsu
dc.contributor.authorHalasz, Peter
dc.contributor.authorKim, Jeong F.
dc.contributor.authorKim, Jiyong
dc.contributor.authorSzekely, Gyorgy
dc.date.accessioned2020-08-09T05:58:59Z
dc.date.available2020-08-09T05:58:59Z
dc.date.issued2020-08-04
dc.identifier.citationHu, J., Kim, C., Halasz, P., Kim, J. F., Kim, J., & Szekely, G. (2020). Artificial intelligence for performance prediction of organic solvent nanofiltration membranes. Journal of Membrane Science, 118513. doi:10.1016/j.memsci.2020.118513
dc.identifier.issn0376-7388
dc.identifier.doi10.1016/j.memsci.2020.118513
dc.identifier.urihttp://hdl.handle.net/10754/664508
dc.description.abstractThere is an urgent need to develop predictive methodologies that will fast-track the industrial implementation of organic solvent nanofiltration (OSN). However, the performance prediction of OSN membranes has been a daunting and challenging task, due to the high number of possible solvents and the complex relationship between solvent-membrane, solute-solvent, and solute-membrane interactions. Therefore, instead of developing fundamental mathematical equations, we have broken away from conventions by compiling a large dataset and building artificial intelligence (AI) based predictive models for both rejection and permeance, based on a collected dataset containing 38,430 datapoints with more than 18 dimensions (parameters). To elucidate the important parameters that affect membrane performance, we have carried out a thorough principal component analysis (PCA), which revealed that the factors affecting both permeance and rejection are surprisingly similar. We have trained three different AI models (artificial neural network, support vector machine, random forest) that predicted the membrane performance with unprecedented accuracy, as high as 98% (permeance) and 91% (rejection). Our findings pave the way towards appropriate data standardization, not only for performance prediction, but also for better membrane design and development.
dc.description.sponsorshipThe authors thank Murielle Rabiller-Baudry from Université Rennes; Anja Drews from HTW Berlin, Yvonne Thiermeyer from Merck KGaA and TU Dortmund; Stefanie Blumenschein from Merck KGaA and TU Dortmund; Matthias Wessling from RWTH Aachen; Dominic Ormerod from VITO; and Gregory S. Smith from University of Cape Town for the provision of data related to their published articles. The PhD scholarship from King Abdullah University of Science and Technology (KAUST) is gratefully acknowledged (JH). The research reported in this publication was supported by funding from KAUST. JFK thanks the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019M3E6A1064799, 2019R1G1A109477811, and 2020R1C1C1007876). CSK and JYK thanks the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2019R1F1A106365312).
dc.language.isoen
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0376738820310905
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Membrane Science. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Membrane Science, [, , (2020-08-04)] DOI: 10.1016/j.memsci.2020.118513 . © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleArtificial intelligence for performance prediction of organic solvent nanofiltration membranes
dc.typeArticle
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentChemical Engineering Program
dc.contributor.departmentAdvanced Membranes and Porous Materials Research Center
dc.identifier.journalJournal of Membrane Science
dc.rights.embargodate2022-08-04
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Chemical Engineering and Analytical Science, The University of Manchester, The Mill, Sackville Street, Manchester, M1 3BB, United Kingdom
dc.contributor.institutionDepartment of Energy and Chemical Engineering, Incheon National University (INU), Incheon, Republic of Korea
dc.contributor.institutionDepartment of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
dc.identifier.pages118513
pubs.publication-statusAccepted
kaust.personHu, Jiahui
kaust.personSzekely, Gyorgy
refterms.dateFOA2020-08-09T05:59:00Z


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