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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

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dc.citedby0
dc.contributor.authorDele-Afolabi T.T.en_US
dc.contributor.authorJung D.W.en_US
dc.contributor.authorAhmadipour M.en_US
dc.contributor.authorAzmah Hanim M.A.en_US
dc.contributor.authorAdeleke A.O.en_US
dc.contributor.authorKandasamy M.en_US
dc.contributor.authorGunnasegaran P.en_US
dc.contributor.authorid56225674500en_US
dc.contributor.authorid56223110700en_US
dc.contributor.authorid57203964708en_US
dc.contributor.authorid24723635600en_US
dc.contributor.authorid57194067040en_US
dc.contributor.authorid57052581200en_US
dc.contributor.authorid35778031300en_US
dc.date.accessioned2025-03-03T07:41:41Z
dc.date.available2025-03-03T07:41:41Z
dc.date.issued2024
dc.description.abstractChemical 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 Authorsen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.jmrt.2024.10.221
dc.identifier.epage5921
dc.identifier.scopus2-s2.0-85207814208
dc.identifier.spage5909
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85207814208&doi=10.1016%2fj.jmrt.2024.10.221&partnerID=40&md5=d795b626b1f04b0e6cfcbf862d3214ff
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36246
dc.identifier.volume33
dc.pagecount12
dc.publisherElsevier Editora Ltdaen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleJournal of Materials Research and Technology
dc.subjectAdaptive boosting
dc.subjectCeramic membranes
dc.subjectComposite membranes
dc.subjectEffluent treatment
dc.subjectFluid catalytic cracking
dc.subjectMembrane technology
dc.subjectAgro-waste PFA
dc.subjectAgro-wastes
dc.subjectCeramic systems
dc.subjectExtreme gradient boosting
dc.subjectGradient boosting
dc.subjectJaya algorithm
dc.subjectMass loss
dc.subjectMonolithics
dc.subjectPorous alumina
dc.subjectPorous ceramics
dc.subjectChemical attack
dc.titleJaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous aluminaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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