An Intelligent Integrated Method for Quality Prediction in Lead-Zinc Sintering Process

Abstract

Based on some features in lead-zinc sintering process (LZSP), such as large time delay and strong non-linearity, an intelligent integrated method for quality prediction based on back-propagation neural network (BPNN) and improved grey system (IGS) is presented. First, the compositions of agglomerate are predicted by BPNN and IGS models. Then, a recursive entropy algorithm for the weighting coefficients is devised from the viewpoint of the information theory and an intelligent integrated prediction model (IIPM) is established. The compositions of sinter agglomerate are predicted by integrating the two prediction models. Application results show that the IIPM has higher prediction precision than that of single model and the proposed intelligent integrated method settles the modeling problem of the quality in the LZSP.