Prediction of higher heating value of fuel coal based on SVR and random forest models
Five models(LSVR,PSVR,RSVR,SSVR,and RF)were constructed by using two statistical learning algorithms(Support Vector Regression and Random Forest),and their performance in predicting the relationship between the high level heat generation of typical power coals and the industrial analysis data was evaluated.The results showed that the RSVR and RF models were able to accurately predict the high-level heat generation,especially within HHVd of 26.00～28.00 MJ/kg and Vd of 28～34%.The mean percentage errors(MAPE)of the RSVR and RF models were 0.97%and 0.96%,respectively.Different types of power coals were selected to verify the usability and application range of the models,and by comparing the absolute percentage errors with various types of coals,it can be found that the Random Forest model generally shows better adaptability and stability.
higher heating value predictionmachine learningbituminous coalsupport vector regression(SVR)random forest
万方数据
维普
