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Selective acid gas separation from diatomic nonmetal gas via ZIF-8 membrane: Taguchi analysis and neural network modeling

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dc.citedby2
dc.contributor.authorSuhaimi N.H.en_US
dc.contributor.authorYeong Y.F.en_US
dc.contributor.authorJusoh N.en_US
dc.contributor.authorWaqas S.en_US
dc.contributor.authorArshad U.en_US
dc.contributor.authorYap B.K.en_US
dc.contributor.authorid57214777218en_US
dc.contributor.authorid25823579000en_US
dc.contributor.authorid54991089100en_US
dc.contributor.authorid57210701927en_US
dc.contributor.authorid57221461706en_US
dc.contributor.authorid26649255900en_US
dc.date.accessioned2025-03-03T07:41:30Z
dc.date.available2025-03-03T07:41:30Z
dc.date.issued2024
dc.description.abstractZIF-8 membranes are an option for separating acid gas from diatomic nonmetal gas, proposing an alternative technology for combating increasing greenhouse gas emissions and reducing climate change's harms. Synthesis and operational parameters are the critical factors that contribute to the upward trend in membrane performance in gas separation applications. In this study, the L8 (23) orthogonal array of the Taguchi method was adopted to identify the optimum conditions for separating acid gas from diatomic nonmetal gas. Three key parameters - seeding duration, growth time, and operating pressure were investigated at two levels each. From Taguchi analysis, the SN ratio and means are influenced by growth time, with a delta of 2.266, for CO2 flux. Meanwhile, the SN ratio and means for CO2/N2 ideal gas selectivity are impacted by seeding duration, with a delta of 4.190. Additionally, a feedforward artificial neural network (ANN) with three inputs, one hidden layer, and two outputs is employed to develop a predictive model. The findings indicated that the ANN successfully projected the CO2 flux and CO2/N2 ideal gas selectivity, with an R-value of 1 for training, validation, testing, and overall, respectively suggesting the validity of the model. Overall, customizing synthesis and operating parameters using the Taguchi method improves membrane performance and reduces variation, while the ANN model provides insight into forecasting acid gas separation from diatomic nonmetals application. ? 2024 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo103102
dc.identifier.doi10.1016/j.rineng.2024.103102
dc.identifier.scopus2-s2.0-85206258302
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85206258302&doi=10.1016%2fj.rineng.2024.103102&partnerID=40&md5=5dcbeed54bcc9c7ce44a79d6816cbc82
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36173
dc.identifier.volume24
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleResults in Engineering
dc.subjectGas permeable membranes
dc.subjectGreenhouse gas emissions
dc.subjectKyoto Protocol
dc.subjectTaguchi methods
dc.subjectAcid gas
dc.subjectCO2/N2 separation
dc.subjectDiatomics
dc.subjectGas separations
dc.subjectMembrane performance
dc.subjectNeural-networks
dc.subjectSynthesis parameters
dc.subjectTaguchi analysis
dc.subjectZIF-8
dc.subjectZIF-8 membranes
dc.subjectNafion membranes
dc.titleSelective acid gas separation from diatomic nonmetal gas via ZIF-8 membrane: Taguchi analysis and neural network modelingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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