BOF steelmaking endpoint prediction based on FWA-TSVR

In view of the important parameters selection problem that affecting prediction precision and generalization ability in twin support vector regression (TSVR), a novel prediction model based on Fire Work algorithm (FWA) optimizing the parameters of TSVR is proposed. Firstly, ten factors influencing the basic oxygen furnace endpoint prediction accuracy are selected as input variables to establish end-point prediction model of the carbon content and temperature based on TSVR. And then FWA is applied to optimize the penalty coefficient and Gauss kernel width coefficient of the TSVR prediction model. Finally, simulations are implemented by using actual production data from basic oxygen furnace to test the performance of the FWA-TSVR model. The results show that the proposed model with higher prediction accuracy and generalization ability increases the endpoint hitting ratio.