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Comparative Analysis of Support Vector Machine Regression and Gaussian Process Regression in Modeling Hydrogen Production from Waste Effluent

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dc.citedby3
dc.contributor.authorHossain S.S.en_US
dc.contributor.authorAyodele B.V.en_US
dc.contributor.authorAli S.S.en_US
dc.contributor.authorCheng C.K.en_US
dc.contributor.authorMustapa S.I.en_US
dc.contributor.authorid57226256715en_US
dc.contributor.authorid56862160400en_US
dc.contributor.authorid36717300800en_US
dc.contributor.authorid57204938666en_US
dc.contributor.authorid36651549700en_US
dc.date.accessioned2023-05-29T09:37:14Z
dc.date.available2023-05-29T09:37:14Z
dc.date.issued2022
dc.descriptionactivated sludge; chemical oxygen demand; effluent; Gaussian method; support vector machineen_US
dc.description.abstractOrganic-rich substrates from organic waste effluents are ideal sources for hydrogen production based on the circular economy concept. In this study, a data-driven approach was employed in modeling hydrogen production from palm oil mill effluents and activated sludge waste. Seven models built on support vector machine (SVM) and Gaussian process regression (GPR) were employed for the modeling of the hydrogen production from the waste sources. The SVM was incorporated with linear kernel function (LSVM), quadratic kernel function (QSVM), cubic kernel function (CSVM), and Gaussian fine kernel function (GFSVM). While the GPR was incorporated with the rotational quadratic kernel function (RQGPR), squared exponential kernel function (SEGPR), and exponential kernel function (EGPR). The model performance revealed that the SVM-based models did not show impressive performance in modeling the hydrogen production from the palm oil mill effluent, as indicated by the R2 of ?0.01, 0.150, and 0.143 for LSVM, QSVM, and CSVM, respectively. Similarly, the SVM-based models did not perform well in modeling the hydrogen production from activated sludge, as evidenced by R2 values of 0.040, 0.190, and 0.340 for LSVM, QSVM, and CSVM, respectively. On the contrary, the SEGPR, RQGPR, SEGPR, and EGPR models displayed outstanding performance in modeling the prediction of hydrogen production from both oil palm mill effluent and activated sludge, with over 90% of the datasets explaining the variation in the model output. With the R2 > 0.9, the predicted hydrogen production was consistent with the SEGPR, RQGPR, SEGPR, and EGPR with minimized prediction errors. The level of importance analysis revealed that all the input parameters are relevant in the production of hydrogen. How-ever, the influent chemical oxygen demand (COD) concentration and the medium temperature significantly influenced the hydrogen production from palm oil mill effluent, whereas the pH of the medium and the temperature significantly influenced the hydrogen production from the activated sludge. � 2022 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7245
dc.identifier.doi10.3390/su14127245
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85132365407
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85132365407&doi=10.3390%2fsu14127245&partnerID=40&md5=554a1d1762b69cf1a2e5e07ec997d91d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26853
dc.identifier.volume14
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleSustainability (Switzerland)
dc.titleComparative Analysis of Support Vector Machine Regression and Gaussian Process Regression in Modeling Hydrogen Production from Waste Effluenten_US
dc.typeArticleen_US
dspace.entity.typePublication
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