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Interaction effect of process parameters and Pd-electrocatalyst in formic acid electro-oxidation for fuel cell applications: Implementing supervised machine learning algorithms

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dc.citedby6
dc.contributor.authorHossain S.K.S.en_US
dc.contributor.authorAli S.S.en_US
dc.contributor.authorRushd S.en_US
dc.contributor.authorAyodele B.V.en_US
dc.contributor.authorCheng C.K.en_US
dc.contributor.authorid57226256715en_US
dc.contributor.authorid36717300800en_US
dc.contributor.authorid57073870900en_US
dc.contributor.authorid56862160400en_US
dc.contributor.authorid57204938666en_US
dc.date.accessioned2023-05-29T09:36:06Z
dc.date.available2023-05-29T09:36:06Z
dc.date.issued2022
dc.descriptionCarbon nanotubes; Electrocatalysts; Electrooxidation; Forestry; Formic acid; Gaussian distribution; Learning algorithms; Palladium; Parameter estimation; Regression analysis; Support vector machines; Formic acid electrooxidation; Fuel cell application; Gaussian kernel functions; Gaussian process regression; Interaction effect; Machine learning algorithms; Performance; Process parameters; Regression trees; Support vector machine regressions; Sensitivity analysisen_US
dc.description.abstractThe increasing interest in renewable and sustainable energy production as a means of attaining net-zero carbon emissions in the near future has spurred research attention in the development of fuel cells that convert chemical energy to electrical energy. In this study machine learning algorithms namely Support Vector Machine (SVM) regression, Regression Trees, and Gaussian Process Regression (GPR) were configured for modeling the effect of palladium supported on carbon nanotube used for formic acid electro-oxidation. The effect of process parameters such as the amount of palladium, the amount of sodium tetrahydridoborate (NaBH4), amount of water, and the electro-oxidation reaction time on the formic acid electro-oxidation to generate current density was evaluated by the various models. The trained SVM regressions models incorporated with linear, quadratic, cubic, and fine Gaussian kernel functions, as well as the Boosted and the Bagged regression Trees, did not show impressive performance as indicated by a low coefficient of determination (R2) < 0.5 and high prediction errors. However, the SVM regression modeled with Median Gaussian kernel function, the GPR incorporated with rotational quadratic and squared exponential kernel functions displayed higher performance with R2 > 0.6 but less than 0.7. The optimization of the SVM, Ensemble Tree, and GPR models resulted in significant performance with R2 of 0.82, 0.83, and 0.85, respectively. The sensitivity analysis using modified Garson algorithm to determine how each of these parameters influences the predicted current density from the direct formic acid fuel cells showed that the level of importance of the input parameters on the predicted current density can be ranked as Pd composition > electro-oxidation time > amount of water > NaBH4 proportion. � 2022 John Wiley & Sons Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1002/er.7602
dc.identifier.epage21597
dc.identifier.issue15
dc.identifier.scopus2-s2.0-85122132352
dc.identifier.spage21583
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85122132352&doi=10.1002%2fer.7602&partnerID=40&md5=c7ec810672fc706806ddbade676270e8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26664
dc.identifier.volume46
dc.publisherJohn Wiley and Sons Ltden_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleInternational Journal of Energy Research
dc.titleInteraction effect of process parameters and Pd-electrocatalyst in formic acid electro-oxidation for fuel cell applications: Implementing supervised machine learning algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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