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Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina

Chemical attack is one of the most significant issues affecting porous ceramic systems employed as membranes for separation technologies, which necessitate frequent system reliability testing. In this work, the non-linear predictive power of a hybridized machine learning prediction model, specifical...

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Main Authors: Dele-Afolabi T.T., Jung D.W., Ahmadipour M., Azmah Hanim M.A., Adeleke A.O., Kandasamy M., Gunnasegaran P.
Other Authors: 56225674500
Format: Article
Published: Elsevier Editora Ltda 2025
Subjects:
Adaptive boosting
Ceramic membranes
Composite membranes
Effluent treatment
Fluid catalytic cracking
Membrane technology
Agro-waste PFA
Agro-wastes
Ceramic systems
Extreme gradient boosting
Gradient boosting
Jaya algorithm
Mass loss
Monolithics
Porous alumina
Porous ceramics
Chemical attack
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spelling my.uniten.dspace-362462025-03-03T15:41:41Z Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina Dele-Afolabi T.T. Jung D.W. Ahmadipour M. Azmah Hanim M.A. Adeleke A.O. Kandasamy M. Gunnasegaran P. 56225674500 56223110700 57203964708 24723635600 57194067040 57052581200 35778031300 Adaptive boosting Ceramic membranes Composite membranes Effluent treatment Fluid catalytic cracking Membrane technology Agro-waste PFA Agro-wastes Ceramic systems Extreme gradient boosting Gradient boosting Jaya algorithm Mass loss Monolithics Porous alumina Porous ceramics Chemical attack Chemical attack is one of the most significant issues affecting porous ceramic systems employed as membranes for separation technologies, which necessitate frequent system reliability testing. In this work, the non-linear predictive power of a hybridized machine learning prediction model, specifically Jaya-XGBoost to predict the corrosion-induced mass loss of monolithic and nickel-reinforced porous alumina ceramics has been examined. This study demonstrates the mass loss of monolithic and Ni-reinforced porous alumina developed using rice husk and sugarcane bagasse in acidic and alkaline corrosive media. Based on empirical findings, the formation of a very stable Ni3Al2SiO8 spinelloid phase in the RH-graded composites increased their chemical stability in the corrosive environments compared to their monolithic and corresponding SCB-graded counterparts. Corrosion testing data of these specimens were collected and fitted into both XGBoost and Jaya-XGBoost machine learning algorithms. The results showed that the Jaya-XGBoost model performed better in predicting the corrosion-induced mass loss of both the monolithic and the nickel-reinforced porous alumina than the regular XGBoost model in terms of statistical accuracy measures. The Jaya-XGBoost model developed in this study effectively predicted the mass loss in NaOH (R2 = 0.9984; MAE = 0.0168) and mass loss in H2SO4 (R2 = 0.9824; MAE = 0.0217) of the monolithic and nickel-reinforced porous alumina. The precision that can be obtained by modifying hyper-parameters with the Jaya method, combined with the well-known accuracy of XGBoost, renders the proposed model novel. ? 2024 The Authors Final 2025-03-03T07:41:41Z 2025-03-03T07:41:41Z 2024 Article 10.1016/j.jmrt.2024.10.221 2-s2.0-85207814208 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207814208&doi=10.1016%2fj.jmrt.2024.10.221&partnerID=40&md5=d795b626b1f04b0e6cfcbf862d3214ff https://irepository.uniten.edu.my/handle/123456789/36246 33 5909 5921 All Open Access; Gold Open Access Elsevier Editora Ltda 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/
topic Adaptive boosting
Ceramic membranes
Composite membranes
Effluent treatment
Fluid catalytic cracking
Membrane technology
Agro-waste PFA
Agro-wastes
Ceramic systems
Extreme gradient boosting
Gradient boosting
Jaya algorithm
Mass loss
Monolithics
Porous alumina
Porous ceramics
Chemical attack
spellingShingle Adaptive boosting
Ceramic membranes
Composite membranes
Effluent treatment
Fluid catalytic cracking
Membrane technology
Agro-waste PFA
Agro-wastes
Ceramic systems
Extreme gradient boosting
Gradient boosting
Jaya algorithm
Mass loss
Monolithics
Porous alumina
Porous ceramics
Chemical attack
Dele-Afolabi T.T.
Jung D.W.
Ahmadipour M.
Azmah Hanim M.A.
Adeleke A.O.
Kandasamy M.
Gunnasegaran P.
Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina
description Chemical attack is one of the most significant issues affecting porous ceramic systems employed as membranes for separation technologies, which necessitate frequent system reliability testing. In this work, the non-linear predictive power of a hybridized machine learning prediction model, specifically Jaya-XGBoost to predict the corrosion-induced mass loss of monolithic and nickel-reinforced porous alumina ceramics has been examined. This study demonstrates the mass loss of monolithic and Ni-reinforced porous alumina developed using rice husk and sugarcane bagasse in acidic and alkaline corrosive media. Based on empirical findings, the formation of a very stable Ni3Al2SiO8 spinelloid phase in the RH-graded composites increased their chemical stability in the corrosive environments compared to their monolithic and corresponding SCB-graded counterparts. Corrosion testing data of these specimens were collected and fitted into both XGBoost and Jaya-XGBoost machine learning algorithms. The results showed that the Jaya-XGBoost model performed better in predicting the corrosion-induced mass loss of both the monolithic and the nickel-reinforced porous alumina than the regular XGBoost model in terms of statistical accuracy measures. The Jaya-XGBoost model developed in this study effectively predicted the mass loss in NaOH (R2 = 0.9984; MAE = 0.0168) and mass loss in H2SO4 (R2 = 0.9824; MAE = 0.0217) of the monolithic and nickel-reinforced porous alumina. The precision that can be obtained by modifying hyper-parameters with the Jaya method, combined with the well-known accuracy of XGBoost, renders the proposed model novel. ? 2024 The Authors
author2 56225674500
author_facet 56225674500
Dele-Afolabi T.T.
Jung D.W.
Ahmadipour M.
Azmah Hanim M.A.
Adeleke A.O.
Kandasamy M.
Gunnasegaran P.
format Article
author Dele-Afolabi T.T.
Jung D.W.
Ahmadipour M.
Azmah Hanim M.A.
Adeleke A.O.
Kandasamy M.
Gunnasegaran P.
author_sort Dele-Afolabi T.T.
title Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina
title_short Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina
title_full Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina
title_fullStr Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina
title_full_unstemmed Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina
title_sort jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and ni-reinforced porous alumina
publisher Elsevier Editora Ltda
publishDate 2025
_version_ 1825816169661071360
score 13.244413

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