Endpoint prediction of BOF steelmaking based on twin support vector regression
GAO Chuang1，SHEN Minggang1，WANG Huanqing2
(1. School of Materials and Metallurgy, University of Science and Technology Liaoning, Anshan 114051,
Liaoning, China;2. School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, Liaoning, China)
Abstract：In order to improve the endpoint hit rate of basic oxygen furnace (BOF) steelmaking, a new prediction model of BOF endpoint is established to achieve the accurate prediction of the carbon content and temperature at the end of the converter. Knearest neighbor weighted based twin support vector regression (KNNWTSVR) is adopted for the model. A KNN weighted matrix is introduced to the objective functions, and the whale optimization algorithm is used to solve the objective functions to improve the performance of the algorithm. Then, based on the datasets of a 260 t BOF, the prediction model for converter steelmaking endpoint is established. The experimental results show that the predicted hit rates of the endpoint carbon content (error ±0.005 %) and temperature (error ±15 ℃) are 94% and 88%, respectively, and the double hit rate achieves 84%. Compared with the other two existing modeling methods, the proposed model obtains the best prediction effect. Therefore, it meets the requirements of the real production of converter steelmaking, and it is also suitable for mathematical modeling in other fields of metallurgy.
2019, Vol. 29 
