The lead-zinc sintering process (LZSP) is an important step in imperial smelting. This paper presents an intelligent integrated optimization and control system (IIOCS) for the LZSP. The optimization and control scheme has a hierarchical configuration. First, the requirements of process control and the configuration of the IIOCS are described. Then, models for predicting quantity and quality (Q&Q) are established using correlation and mechanism analysis, and are implemented by improved back-propagation neural networks. Based on the models, an integrated algorithm combining c-means clustering, genetic, and chaos approaches is employed to optimize the operating parameters of the process. Finally, the control of the process state is carried out by a distributed control system to control the Q&Q of the product.
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Applied Mathematics
- Computer Science Applications