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

activated sludge; chemical oxygen demand; effluent; Gaussian method; support vector machine

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Main Authors: Hossain S.S., Ayodele B.V., Ali S.S., Cheng C.K., Mustapa S.I.
Other Authors: 57226256715
Format: Article
Published: MDPI 2023
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spelling my.uniten.dspace-268532023-05-29T17:37:14Z Comparative Analysis of Support Vector Machine Regression and Gaussian Process Regression in Modeling Hydrogen Production from Waste Effluent Hossain S.S. Ayodele B.V. Ali S.S. Cheng C.K. Mustapa S.I. 57226256715 56862160400 36717300800 57204938666 36651549700 activated sludge; chemical oxygen demand; effluent; Gaussian method; support vector machine Organic-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. Final 2023-05-29T09:37:14Z 2023-05-29T09:37:14Z 2022 Article 10.3390/su14127245 2-s2.0-85132365407 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132365407&doi=10.3390%2fsu14127245&partnerID=40&md5=554a1d1762b69cf1a2e5e07ec997d91d https://irepository.uniten.edu.my/handle/123456789/26853 14 12 7245 All Open Access, Gold, Green MDPI Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description activated sludge; chemical oxygen demand; effluent; Gaussian method; support vector machine
author2 57226256715
author_facet 57226256715
Hossain S.S.
Ayodele B.V.
Ali S.S.
Cheng C.K.
Mustapa S.I.
format Article
author Hossain S.S.
Ayodele B.V.
Ali S.S.
Cheng C.K.
Mustapa S.I.
spellingShingle Hossain S.S.
Ayodele B.V.
Ali S.S.
Cheng C.K.
Mustapa S.I.
Comparative Analysis of Support Vector Machine Regression and Gaussian Process Regression in Modeling Hydrogen Production from Waste Effluent
author_sort Hossain S.S.
title Comparative Analysis of Support Vector Machine Regression and Gaussian Process Regression in Modeling Hydrogen Production from Waste Effluent
title_short Comparative Analysis of Support Vector Machine Regression and Gaussian Process Regression in Modeling Hydrogen Production from Waste Effluent
title_full Comparative Analysis of Support Vector Machine Regression and Gaussian Process Regression in Modeling Hydrogen Production from Waste Effluent
title_fullStr Comparative Analysis of Support Vector Machine Regression and Gaussian Process Regression in Modeling Hydrogen Production from Waste Effluent
title_full_unstemmed Comparative Analysis of Support Vector Machine Regression and Gaussian Process Regression in Modeling Hydrogen Production from Waste Effluent
title_sort comparative analysis of support vector machine regression and gaussian process regression in modeling hydrogen production from waste effluent
publisher MDPI
publishDate 2023
_version_ 1806425517999521792
score 13.211869

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