Prediction of FeO content in sinter based on KPCA and logistics-SSA-BP
Sinter is one of the important raw materials in the blast furnace ironmaking process.The control of FeO content has an important impact on ironmaking process,iron quality and energy consumption.Due to the problems of feature selection deviating from reality and poor generalization ability of prediction model in the current research process,the prediction model of FeO content of sinter based on kernel principal component analysis(KPCA)and Logistics-SSA-BP was proposed.The feature parameters were screened and dimensionality reduced by Pearson and KPCA.Combined with Logistic-SSA-BP optimization algorithm,the collected data are trained,learned and verified.The experimental results show that the absolute error is stable within the range of[0,0.21],and the hit rate reaches 98.75％within+0.2 between the predicted value and the actual value.The performance of the prediction model is better,and the evaluation indexes of MSE,MAE,and RMSE reach 0.013、0.101,0.115.The prediction model could accurately predict the FeO content of sinter,which could provide direction to blast furnace operators when establishing batching programs and executing process operations.
NETL
NSTL
万方数据
