Expert system based adaptive dynamic matrix control for ball mill grinding circuit

Ball mill grinding circuit is a multiple-input multiple-output (MIMO) system characterized with couplings and nonlinearities. Stable control of grinding circuit is usually interrupted by great disturbances, such as ore hardness and feed particle size, etc. Conventional model predictive control usually cannot capture the nonlinearities caused by the disturbances in real practice. Multiple models based adaptive dynamic matrix control (ADMC) is proposed for the control of ball mill grinding circuit. The novelty of the strategy lies in that intelligent expert system is developed to identify the current ore hardness and then select a proper model for ADMC. Compared with the various nonlinear DMC strategies, the approach can synthesize and analyze as many variables and status as possible to adequately and reliably identify the process conditions, and it does not introduce additional computational complexity, which makes it readily available to the industrial practitioner. Simulation results and industrial applications demonstrate the effectiveness and practicality of this control strategy.

[1]  Guo H. Huang,et al.  Development of an intelligent decision support system for air pollution control at coal-fired power plants , 2004, Expert Syst. Appl..

[2]  Kumpati S. Narendra,et al.  Intelligent control using neural networks and multiple models , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[3]  Daniel Hodouin,et al.  A survey of grinding circuit control methods: from decentralized PID controllers to multivariable predictive controllers , 2000 .

[4]  D. Lainiotis Optimal adaptive estimation: Structure and parameter adaption , 1971 .

[5]  Mohamed A.H. El-Sayed Rule-based approach for real-time reactive power control in interconnected power systems , 1998 .

[6]  B W Bequette,et al.  Issues in the Design of a Multirate Model‐Based Controller for a Nonlinear Drug Infusion System , 1995, Biotechnology progress.

[7]  V. R. Radhakrishnan Model based supervisory control of a ball mill grinding circuit , 1999 .

[8]  Manuel A. Duarte,et al.  A comparative experimental study of five multivariable control strategies applied to a grinding plant , 1999 .

[9]  Jian Gu,et al.  Nonlinear dynamic matrix control based on multiple operating models , 2003 .

[10]  C. R. Cutler,et al.  Dynamic matrix control¿A computer control algorithm , 1979 .

[11]  Chris Aldrich,et al.  Neurocontrol of a ball mill grinding circuit using evolutionary reinforcement learning , 2001 .

[12]  Doug Cooper,et al.  A Practical Multiple Model Adaptive Strategy for Multivariable Model Predictive Control , 2003 .

[13]  Marappagounder Ramasamy,et al.  Control of ball mill grinding circuit using model predictive control scheme , 2005 .

[14]  Pavel Hrncirik,et al.  The BIOGENES system for knowledge-based bioprocess control , 2002, Expert Syst. Appl..

[15]  Jose A. Romagnoli,et al.  Robust control of a SAG mill , 2002 .

[16]  B. Wayne Bequette,et al.  Extension of dynamic matrix control to multiple models , 2003, Comput. Chem. Eng..

[17]  D. Lainiotis Optimal adaptive estimation: Structure and parameter adaptation , 1970 .

[18]  Nina F. Thornhill,et al.  Inferential measurement of SAG mill parameters III: inferential models , 2002 .