Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed co-gasification of biomass wastes from oil palm

The quest to attain net-zero emissions has increased the drive for more renewable energy potential from the co-gasification of biomass. The co-gasification of coconut shell and oil palm wastes is a potential technological route to produce hydrogen-rich syngas. However, the complexity of the gaseous-phase reaction often results in process uncertainties which could lead to energy and material wastage. Taking advantage of the data generated from the process, this study explores the performance of twelve machine learning algorithms built on the support vector machine (SVM), the Gaussian process regression (GPR), and the non-linear response quadratic model (NLRQM) using Sequential quadratic programming, and the Levenberg-Marquardt algorithms. The co-gasification of coconut shell and oil palm wastes blend catalyzed by Portland cement, dolomite, and limestone resulted in the maximum syngas production of 42 mol.%, 38 mol.%, 45 mol.%, respectively. The co-gasification process was modeled using SVM regression incorporated with linear, quadratic, and cubic kernel functions, GPR incorporated with rotational, squared, Matern 5/2, and exponential kernel functions, and non-linear response quadratic model (NLRQM) using sequential quadratic programming (SQP), and Levenberg-Marquardt (LM). The performance analysis of the models revealed that the SVM incorporated with linear kernel had the least performance with R 2 in the range of 0.3–0.7. Whereas the best performance in terms of prediction of the syngas composition was obtained using the NLRQM algorithm with an inbuilt SQP and LM algorithms. The observed syngas composition was consistent with predicted values with R 2 > 0.97 for the three catalyzed co-gasification processes. The low RMSE (<1) and MAE (<1) obtained from the models are indications of the robustness of the accurate prediction of the NLRQM-LM and NLRQM-SQP algorithms. The sensitivity analysis revealed that the co-gasification temperature, catalysts loading, and the blending amount play a significant role in the predicted syngas composition. However, the co-gasification temperature had the highest influence as indicated by the level of importance values. • Hydrogen-rich syngas production from co-gasification of biomass blend was investigated. • Twelve machine learning algorithms were employed to model the co-gasification process. • Support vector machine and Gaussian process regression performed moderately. • Non-linear response quadratic model embedded with inbuilt algorithms perform best. • Gasification temperature significantly influenced the predicted syngas composition.