A machine learning based regression methods to predicting syngas composition for plasma gasification system

•Support vector, decision tree, random forest, and Gaussian process regression used.•Models predict CO2, CO, N2, O2, H2, and CH4 levels in plasma gasification process.•Regression models tested using k-fold cross-validation for unbiased evaluation.•Models can be integrated into microcontroller systems for optimization and control. Plasma gasification is considered a promising technology that converts waste into energy through an environmentally friendly process. This research focuses on predicting the outputs of this process, utilizing ML regression techniques. Data from previous studies involving different solid fuels were collected, and four regression techniques Random Forest Regression, Gaussian Process Regression, Decision Tree Regression, and Support Vector Regression were employed to predict the levels of CO2, CO, N2, O2, H2, and CH4 in the plasma gasification process. The experimental dataset was gathered using a microwave gasifier with varying air flow rates (50–100 sL/min) and plasma power (3–6 kW). GPR a coefficient of determination (R2) values exceeding 0.983 for all outputs and low NRMSE and MSLE values (