To solve the problem of low prediction accuracy of sinter strand gas consumption in iron and steel industry,autoregressive integrated moving average model(ARIMA),long short-term memory network model(LSTM)and extreme gradient boosting model(XGBoost)are established to predict the blast furnace gas consumption.The performance of the prediction models is verified by comparing the actual data of iron and steel industry.The results show that the prediction accuracy of XGBoost model is higher than ARIMA model and LSTM model.The mean absolute percentage error of XGBoost model is 3.45％,root mean square error is 703.53 m3/min,R2 is 99.91％,and the robustness and general-ization ability of XGBoost model are strong.In addition,in order to strengthen the connection between the prediction models and different operating states of sinter strand,the gas consumption of different operating states is predicted.LSTM model shows the best prediction effect in normal production state of sinter strand,while XGBoost model shows the best prediction effect in production reduction and pro-duction increase state.
iron and steel industrysinter strandgas consumption predictiondata modeloperat-ing state
维普
万方数据
