Abstract
In this article, super learner approaches such as hybrid Bayesian Optimization Algorithm-Support Vector Regression (BOA-SVR), Bayesian Optimization Algorithm-Boosted Regression Tree (BOA-BRT), along with a statistical method (response surface methodology, RSM) were utilized as potential tools for predicting biodiesel synthesis using waste date seed oil as feedstock. Novelties of this investigation comprise (a) hybridization of BOA with each artificial intelligence (AI) approach resulting in the formation of BOA-SVR and BOA-BRT super learner models, (b) the model performance was compared using several performance indicators including coefficient of determination (R-2), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), etc., (c) validation of the model was confirmed using extra simulated data, (d) the crow search algorithm (CSA) was integrated with the BOA-SVR resulting advanced super learner model (BOA-SVR-CSA) for finding the global optimal point. BOA-BRT model provided relatively low R-2 (0.81) and high errors (MAE of 8.5159, RSME of 12.4674, MAPE of 106.0391). RSM model was statistically significant (P-value <.05) with relatively high R-2 (0.95) and moderate errors (MAE of 4.8886, RSME of 5.5964, MAPE of 22.1574). The BOA-SVR model provided low errors (MAE of 3.8342, RSME of 3.8884, MAPE of 18.91) with a high R-2 of 0.98. The overall results suggested that the BOA-SVR model performs better with increased accuracy than other models. The extra simulated data further confirmed the prediction capability of the developed super learner model (BOA-SVR). The maximum biodiesel yield of 91.35% was achieved with a KOH dose of 0.6 wt%, M:O of 7:1 at a reaction time of 2 hours using the advanced super learner model (BOA-SVR-CSA). Overall, this novel platform could be of considerable promise in other process modeling and multiobjective optimization applications.