The calorific value of coal is a key index to measure coal quality,which reflects the energy re-leased when coal is fully burned.The calorific value of coal can be obtained by experimental measurement and calculation,in which the experimental method is accurate but complex,expensive and time-consuming.In practical application,the calorific value data is estimated by multiple linear regression,but the accuracy of this method is low.In view of this,a calculation method of coal calorific value based on gradient boosting regression tree(GBRT)is proposed,which is a machine learning regression analysis method and can effective-ly overcome the limitations of multiple linear regression models in dealing with nonlinear data.The proposed model is verified and compared on the internationally recognized COALQUAL coal quality database.The re-sults show that the prediction errors(MAE,MSE,RMSE)of GBRT model are less than those of multiple lin-ear regression model,and the fitting goodness(R2=0.989)is greater than that of multiple linear regression model(R2=0.970),demonstrating that GBRT is an efficient and accurate prediction model of coal calorific val-ue and has certain practical significance for coal quality evaluation.
关键词
煤炭发热量/梯度提升回归树/回归分析/预测
Key words
Calorific value of coal/gradient boosting regression tree/regression analysis/prediction
维普
万方数据