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Biochar production from valorization of agricultural Wastes: Data-Driven modelling using Machine learning algorithms

Biochar is an important nanomaterial that can be used in wastewater treatment. The production of biochar is often done by heating biomass in the absence of oxygen, a process known as pyrolysis. The valorization process involves a complex chemical reaction which is often not easily demystified. The d...

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Main Authors: Kanthasamy, R., Almatrafi, E., Ali, I., Hussain Sait, H., Zwawi, M., Abnisa, F., Choe Peng, L., Victor Ayodele, B.
Format: Article
Published: Elsevier Ltd 2023
Online Access:http://scholars.utp.edu.my/id/eprint/37306/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162013310&doi=10.1016%2fj.fuel.2023.128948&partnerID=40&md5=0807448ff206948b85808a59ca53b00d
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spelling oai:scholars.utp.edu.my:373062023-10-04T08:37:57Z http://scholars.utp.edu.my/id/eprint/37306/ Biochar production from valorization of agricultural Wastes: Data-Driven modelling using Machine learning algorithms Kanthasamy, R. Almatrafi, E. Ali, I. Hussain Sait, H. Zwawi, M. Abnisa, F. Choe Peng, L. Victor Ayodele, B. Biochar is an important nanomaterial that can be used in wastewater treatment. The production of biochar is often done by heating biomass in the absence of oxygen, a process known as pyrolysis. The valorization process involves a complex chemical reaction which is often not easily demystified. The data obtained from the valorization of the biomass can be employed to model the process for the purpose of understanding the relationship between the input and targeted parameters thereby optimizing the process. This study employs a data-driven approach to model biochar production from agricultural wastes. The parametric analysis shows that the biochar yields obtained from the different agriculture wastes were significantly influenced by temperature, heating rate, residence time, and Nitrogen flow rate. The support vector machine (SVM) models with different kernel functions displayed predictive potentials of the biochar production with R2 in the range of 0.5�0.8. The Gaussian process regression models with different kernel functions offer better prediction potentials compared to the SVM models as indicated by a higher R2 > 0.7. The artificial neural network-based algorithms outperformed the SVM and GPR as indicated by the R2 > 0.9 and low predictive errors. The relative importance analysis shows that the residence time during the valorization reaction has the most significant influence on the predicted biochar produced from agricultural wastes. © 2023 Elsevier Ltd Elsevier Ltd 2023 Article NonPeerReviewed Kanthasamy, R. and Almatrafi, E. and Ali, I. and Hussain Sait, H. and Zwawi, M. and Abnisa, F. and Choe Peng, L. and Victor Ayodele, B. (2023) Biochar production from valorization of agricultural Wastes: Data-Driven modelling using Machine learning algorithms. Fuel, 351. ISSN 00162361 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162013310&doi=10.1016%2fj.fuel.2023.128948&partnerID=40&md5=0807448ff206948b85808a59ca53b00d 10.1016/j.fuel.2023.128948 10.1016/j.fuel.2023.128948 10.1016/j.fuel.2023.128948
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Biochar is an important nanomaterial that can be used in wastewater treatment. The production of biochar is often done by heating biomass in the absence of oxygen, a process known as pyrolysis. The valorization process involves a complex chemical reaction which is often not easily demystified. The data obtained from the valorization of the biomass can be employed to model the process for the purpose of understanding the relationship between the input and targeted parameters thereby optimizing the process. This study employs a data-driven approach to model biochar production from agricultural wastes. The parametric analysis shows that the biochar yields obtained from the different agriculture wastes were significantly influenced by temperature, heating rate, residence time, and Nitrogen flow rate. The support vector machine (SVM) models with different kernel functions displayed predictive potentials of the biochar production with R2 in the range of 0.5�0.8. The Gaussian process regression models with different kernel functions offer better prediction potentials compared to the SVM models as indicated by a higher R2 > 0.7. The artificial neural network-based algorithms outperformed the SVM and GPR as indicated by the R2 > 0.9 and low predictive errors. The relative importance analysis shows that the residence time during the valorization reaction has the most significant influence on the predicted biochar produced from agricultural wastes. © 2023 Elsevier Ltd
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author Kanthasamy, R.
Almatrafi, E.
Ali, I.
Hussain Sait, H.
Zwawi, M.
Abnisa, F.
Choe Peng, L.
Victor Ayodele, B.
spellingShingle Kanthasamy, R.
Almatrafi, E.
Ali, I.
Hussain Sait, H.
Zwawi, M.
Abnisa, F.
Choe Peng, L.
Victor Ayodele, B.
Biochar production from valorization of agricultural Wastes: Data-Driven modelling using Machine learning algorithms
author_facet Kanthasamy, R.
Almatrafi, E.
Ali, I.
Hussain Sait, H.
Zwawi, M.
Abnisa, F.
Choe Peng, L.
Victor Ayodele, B.
author_sort Kanthasamy, R.
title Biochar production from valorization of agricultural Wastes: Data-Driven modelling using Machine learning algorithms
title_short Biochar production from valorization of agricultural Wastes: Data-Driven modelling using Machine learning algorithms
title_full Biochar production from valorization of agricultural Wastes: Data-Driven modelling using Machine learning algorithms
title_fullStr Biochar production from valorization of agricultural Wastes: Data-Driven modelling using Machine learning algorithms
title_full_unstemmed Biochar production from valorization of agricultural Wastes: Data-Driven modelling using Machine learning algorithms
title_sort biochar production from valorization of agricultural wastes: data-driven modelling using machine learning algorithms
publisher Elsevier Ltd
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/37306/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162013310&doi=10.1016%2fj.fuel.2023.128948&partnerID=40&md5=0807448ff206948b85808a59ca53b00d
_version_ 1779441363073368064
score 13.211869

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