Modeling of hydrogen separation through Pd membrane with vacuum pressure using Taguchi and machine learning methods

Chen, WH; Wu, DR; Chang, MH; Rajendran, S; Ong, HC; Lin, KYA

Modeling of hydrogen separation through Pd membrane with vacuum pressure using Taguchi and machine learning methods

Chen, WH Wu, DR Chang, MH Rajendran, S Ong, HC Lin, KYA

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: International Journal of Hydrogen Energy, 2024
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Chen, WH
dc.contributor.author	Wu, DR
dc.contributor.author	Chang, MH
dc.contributor.author	Rajendran, S
dc.contributor.author	Ong, HC
dc.contributor.author	Lin, KYA
dc.date.accessioned	2024-11-11T06:34:42Z
dc.date.available	2024-11-11T06:34:42Z
dc.date.issued	2024-01-01
dc.identifier.citation	International Journal of Hydrogen Energy, 2024
dc.identifier.issn	0360-3199
dc.identifier.uri	http://hdl.handle.net/10453/181839
dc.description.abstract	The performance of hydrogen purification using palladium (Pd) membrane is analyzed by Taguchi and machine learning (ML) methods. Three system factors are considered: the feed gas mixture composition (i.e., H2, CO2, and H2O), retentate-side total pressure, and vacuum pressure. The Taguchi method designs the experimental cases based on the constructed orthogonal array. In addition, the hydrogen flux is investigated by the analyses of variance (ANOVA) and artificial neural networks (ANN) methods. The results show that the effects of the considered factors on the hydrogen permeation performance can be ranked as feed gas mixture composition > retentate-side total pressure > vacuum pressure. Both the retentate-side pressure and vacuum pressure present a positive effect on hydrogen flux. The average relative error of hydrogen flux between the experimental results and predictions by ANN is 2.1%, which proves that the ANN method is an effective technique for predicting the hydrogen flux through the Pd membrane. The ML classification test is then performed for hydrogen purity by three machine learning methods: decision tree (DT), support vector machine (SVM), and ensemble method. Among the various classification models, the Quadratic SVM model exhibits a relatively high average training accuracy but shows overfitting. However, the bagged model of the ensemble method can achieve an impressive training accuracy of 91.4% and a prediction accuracy of 85.7%.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	International Journal of Hydrogen Energy
dc.relation.isbasedon	10.1016/j.ijhydene.2024.08.204
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	03 Chemical Sciences, 09 Engineering
dc.subject.classification	Energy
dc.subject.classification	34 Chemical sciences
dc.subject.classification	40 Engineering
dc.title	Modeling of hydrogen separation through Pd membrane with vacuum pressure using Taguchi and machine learning methods
dc.type	Journal Article
utslib.for	03 Chemical Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-11-11T06:34:41Z
pubs.publication-status	Published

Abstract:

The performance of hydrogen purification using palladium (Pd) membrane is analyzed by Taguchi and machine learning (ML) methods. Three system factors are considered: the feed gas mixture composition (i.e., H2, CO2, and H2O), retentate-side total pressure, and vacuum pressure. The Taguchi method designs the experimental cases based on the constructed orthogonal array. In addition, the hydrogen flux is investigated by the analyses of variance (ANOVA) and artificial neural networks (ANN) methods. The results show that the effects of the considered factors on the hydrogen permeation performance can be ranked as feed gas mixture composition > retentate-side total pressure > vacuum pressure. Both the retentate-side pressure and vacuum pressure present a positive effect on hydrogen flux. The average relative error of hydrogen flux between the experimental results and predictions by ANN is 2.1%, which proves that the ANN method is an effective technique for predicting the hydrogen flux through the Pd membrane. The ML classification test is then performed for hydrogen purity by three machine learning methods: decision tree (DT), support vector machine (SVM), and ensemble method. Among the various classification models, the Quadratic SVM model exhibits a relatively high average training accuracy but shows overfitting. However, the bagged model of the ensemble method can achieve an impressive training accuracy of 91.4% and a prediction accuracy of 85.7%.

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http://hdl.handle.net/10453/181839