In order to achieve accurate prediction of phosphorus content in molten steel at the end point of converter,kernel principal component analysis(KPCA)was used to reduce dimensionality of data,and then an improved dung beetle optimization(IDBO)algorithm was proposed to optimize least square support vector machine(LSSVM).Finally,KPCA-IDBO-LSSVM prediction model is estab-lished to predict the phosphorus content of steel at the end of converter.The prediction results of termi-nal phosphorus content of KPCA-IDBO-LSSVM were compared with those of LSSVM,IDBO-LSS-VM and other models.The results showed that the addition of KPCA and IDBO significantly improved the prediction effect.The prediction error of terminal phosphorus content of KPCA-IDBO-LSSVM reaches 90%within±0.003%,which brings practical help for smelting.
converter steelmakingphosphorus content predictiondung beetle algorithmnuclear principal component analysis
NETL
NSTL
万方数据
