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| Received:October 11, 2011Revised:May 22, 2012 |
| 基金项目:This work was supported by the National Fundamental Research Program of China (No. 2009CB320601), the National Natural Science Foundation of China (Nos. 61020106003, 61134006, 61240012), the 111 Project(No. B08015), and the NKTSP Project (No. 2012BAF19G00). |
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| Intelligent optimal control system for ball mill grinding process |
| Dayong ZHAO,Tianyou CHAI |
| (State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University;State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University; Research Center of Automation, Northeastern University) |
| Abstract: |
| Operation aim of ball mill grinding process is to control grinding particle size and circulation load to ball mill into their objective limits respectively, while guaranteeing producing safely and stably. The grinding process is essentially a multi-input multi-output system (MIMO) with large inertia, strong coupling and uncertainty characteristics. Furthermore, being unable to monitor the particle size online in most of concentrator plants, it is difficult to realize the optimal control by adopting traditional control methods based on mathematical models. In this paper, an intelligent optimal control method with two-layer hierarchical construction is presented. Based on fuzzy and rule-based reasoning (RBR) algorithms, the intelligent optimal setting layer generates the loops setpoints of the basic control layer, and the latter can track their setpoints with decentralized PID algorithms. With the distributed control system (DCS) platform, the proposed control method has been built and implemented in a concentration plant in Gansu province, China. The industrial application indicates the validation and effectiveness of the proposed method. |
| Key words: Intelligent optimal control Fuzzy control Rule-based reasoning Grinding process Particle size |
