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...

Full description

Bibliographic Details
Published in:JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
Main Authors: Dele-Afolabi, T. T.; Jung, D. W.; Ahmadipour, Masoud; Hanim, M. A. Azmah; Adeleke, A. O.; Kandasamy, M.; Gunnasegaran, Prem
Format: Article
Language:English
Published: ELSEVIER 2024
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001350259500001
author Dele-Afolabi
T. T.; Jung
D. W.; Ahmadipour
Masoud; Hanim
M. A. Azmah; Adeleke
A. O.; Kandasamy
M.; Gunnasegaran
Prem
spellingShingle Dele-Afolabi
T. T.; Jung
D. W.; Ahmadipour
Masoud; Hanim
M. A. Azmah; Adeleke
A. O.; Kandasamy
M.; Gunnasegaran
Prem
Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina
Materials Science; Metallurgy & Metallurgical Engineering
author_facet Dele-Afolabi
T. T.; Jung
D. W.; Ahmadipour
Masoud; Hanim
M. A. Azmah; Adeleke
A. O.; Kandasamy
M.; Gunnasegaran
Prem
author_sort Dele-Afolabi
spelling Dele-Afolabi, T. T.; Jung, D. W.; Ahmadipour, Masoud; Hanim, M. A. Azmah; Adeleke, A. O.; Kandasamy, M.; Gunnasegaran, Prem
Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
English
Article
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.
ELSEVIER
2238-7854
2214-0697
2024
33

10.1016/j.jmrt.2024.10.221
Materials Science; Metallurgy & Metallurgical Engineering
gold
WOS:001350259500001
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001350259500001
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
container_title JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
language English
format Article
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.
publisher ELSEVIER
issn 2238-7854
2214-0697
publishDate 2024
container_volume 33
container_issue
doi_str_mv 10.1016/j.jmrt.2024.10.221
topic Materials Science; Metallurgy & Metallurgical Engineering
topic_facet Materials Science; Metallurgy & Metallurgical Engineering
accesstype gold
id WOS:001350259500001
url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001350259500001
record_format wos
collection Web of Science (WoS)
_version_ 1818940501096660992