Prediction of end-point LF refining furnace based on wavelet transform based weighted optimized twin support vector machine algorithm

Abstract

During the LF refining process, the end-point temperature and carbon content changes at the end of refining are relatively lagging. And most of the traditional prediction models suffer from weak operational generalization ability, long computation time, and the existence of multiple polarization points, which greatly affect the prediction accuracy of the models. In this paper, a wavelet transform based weighted algorithm (WTW) optimized twin support vector machine algorithm (WTWTSVR) prediction model for refining end-point temperature and carbon content is proposed. WTW is introduced into the objective function on the basis of TSVR, and the objective function is converted into an unconstrained optimization solution problem, and then a mathematical model of LF refiner end-point temperature and carbon content is established to complete the prediction of these parameters. The production practice shows that the forecast accuracy of the model for 400 furnace times is 91.5%, 90.2%; 95.6%, 95.5% for refining end-point temperature error and carbon content error within ±5% and ±10%, respectively. The double hit rate within different error ranges (within 10 °C for the temperature model and within 0.005% for the carbon content model) reached 86.5%. The results indicate that the method can provide theoretical guidance for the LF refining production process.