Application of machine learning algorithms to predict the performance of coal gasification process

The coal gasification process is one of the most convenient and clean coal technologies that convert coal into electricity, syngas, and other energy products. Thus, it is essential to estimate the outcomes of this process to obtain the optimum amount of product. Therefore, the main effort of this study is to evaluate the capability of various machine learning (ML) methods to predict gasification process output variables such as the product gas generation and product gas heating value. For this purpose, various regression models were created by using different ML algorithms such as Sequential Minimal Optimization Regression, Gaussian Process Regression, Lazy K-Star, Lazy IBk, Alternating Model Tree, Random Forest, and M5Rules. Coal properties such as fixed carbon, volatile matter, and mineral matter content and gasification process parameters, such as air feed per kg of coal, steam feed per kg of coal, and bed temperature were used as input parameters. The performances of the models were evaluated using various well-known statistical measures such as coefficient of determination (R2), the mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE in %), and root relative squared error (RRSE in %). In the test dataset, the Random Forest model achieved the best results for both outputs with R2 = 0.9730, MAE = 0.0338, RMSE = 0.0451, RAE = 15.7148%, and RRSE = 19.1181% values for the prediction of the heating value of the product gas and R2 = 0.9928, MAE = 0.0214, RMSE = 0.0258, RAE = 8.8001%, and RRSE = 9.1592% values for prediction of the product gas generation.