Application of Intelligent Integrated Optimization System for Raw Material Proportioning in Lead–Zinc Sintering Blending Process

Abstract

The blending process as the first working procedure in lead–zinc sintering processes (LZSP) is a key step to guarantee the quality of products in the lead–zinc sintering production. This paper presents an intelligent integrated optimization system (IIOS) with a hierarchical configuration for raw material proportioning in the lead–zinc sintering blending process (LZSBP). First, considering the relationships between the mixture ratio and the production indices, back-propagation neural network (BPNN) models are established to predict the agglomerate compositions. Then, a raw material proportioning optimization strategy (RMPOS) is proposed to determine an optimal mixture ratio and actualize the optimization of the blending process. The proportioning optimization is implemented through qualitative and quantitative synthetic optimization for the primary proportioning, the zone optimization for the secondary proportioning, and the intelligent coordination between them. The practical running results demonstrate the validity of the proposed optimization strategies.