论文信息 - Hybrid Intelligent System for Supervisory Control of Mineral Grinding Process

Hybrid Intelligent System for Supervisory Control of Mineral Grinding Process

The particle size is the important technical performance index of the grinding process, which closely related to the overall performance of the mineral processing. In this paper, we mainly concern on the determination of the particle size for the supervisory control of the grinding process by the technical performance index decision system. The overall structure of the system and introduce of every part are given briefly. The experiment results and its compare with the neural network method show its validity and efficiency

Tianyou Chai | Jinliang Ding | Changxin Liu | Ping Zhou

[1] Chai Tianyou. Intelligent Optimizing Control System of Grinding Process for Particle Size Index , 2006 .

[2] Daniel Hodouin,et al. Constrained real-time optimization of a grinding circuit using steady-state linear programming supervisory control , 2002 .

[3] Agnar Aamodt,et al. Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[4] Raj K. Rajamani,et al. Optimal control of a ball mill grinding circuit—II. Feedback and optimal control , 1991 .

[5] J. A. Herbst,et al. Financial implications of comminution system optimization , 1996 .

[6] K. Najim,et al. Adaptive control—practical aspects and application to a grinding circuit , 1997 .

[7] Janet L. Kolodner,et al. An introduction to case-based reasoning , 1992, Artificial Intelligence Review.

[8] Raj K. Rajamani,et al. Optimal control of a ball mill grinding circuit—I. Grinding circuit modeling and dynamic simulation , 1991 .