Prediction of blast furnace pressure difference based on a combined model of XGBoost and BP optimized by SSA
The pressure difference of blast furnace has an important impact on the normal operation and smelting efficiency of blast furnace.Excessive pressure difference can cause material suspension and disrupt the smooth operation of the blast furnace;a low pressure difference can cause air flow through the furnace or even pipeline.Therefore,it is the premise of ensuring the stability of the blast furnace on maintaining the blast furnace pressure difference within a certain range.It can help operators understand the working status,adjust the operating parameters in time,and ensure the stability of the blast furnace by predicting the blast furnace pressure difference in advance.In order to improve the prediction accuracy of blast furnace pressure difference prediction,based on the monitoring of blast furnace smelting data in a domestic iron and steel enterprise,a fusion prediction model of eXtreme Gradient Boosting(eXtreme Gradient Boosting,XGBoost)and Back Propagation Neural Network(Back Propagation Neural Network,BPNN)optimized by Sparrow Search Algorithm(Sparrow Search Algorithm,SSA)was established.The core of this model lies in the optimization of the individual models by the SSA algorithm,and reduced the prediction error by weighting the prediction value with error reciprocal method.The results show that the prediction effect of the XGBoost-BP fusion model based on SSA optimization is significantly higher than that of other models.The goodness of fit of the model reaches 0.842,which has high fitting ability.Compaed with SSA-BP and SSA-XGBoost models,the fusion model has smaller prediction error and faster convergence speed.Under the error range of±0.025×105 Pa,the prediction accuracy reaches 96.13％.Finally,based on the fusion model proposed,a blast furnace pressure difference prediction system was established,which not only plays a role in guiding blast furnace production,but also has a certain practical significance for the transformation and upgrading of ironmaking industry.
blast furnace pressure differenceSSAXGBoostBP neural networkprediction model
万方数据
维普
