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, 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.