Just-in-time-learning based prediction model of BOF endpoint carbon content and temperature via vMF mixture model and weighted extreme learning machine

Abstract

Basic oxygen furnace (BOF) steelmaking is a complicated physical chemical process, in which the endpoint carbon content and temperature are two important indicators. In BOF steelmaking, the quality of raw materials varies greatly between different batches, which would lead to the inaccurate predictions for these two indicators. Additionally, there are imbalance problems in production process data of BOF steelmaking. For the time-varying problem, a novel similarity criterion based on von-Mises Fisher mixture model (VMM) is proposed in this paper and applied for sample selection of just-in-time-learning (JITL)-based endpoint carbon content and temperature prediction model. The V-shaped transfer function is utilized to develop weighted extreme learning machine (WELM) as local regression model to address the imbalance problems. The performance of the proposed methods is compared with other methods under JITL framework. The experimental results show that the proposed online model can provide a more accurate prediction.