The Simulation and Design Applications of Grinding and Classification Process Control System

Abstract:

Based on the grinding and classification process dynamic model, the distributed simulation platform for semi-physical grinding process was analyzed. Based on the feedback correction and dynamic optimal control and optimization model calculated the optimal control law, the quality indicators to feedback regulation mechanism was introduced to eliminate the impact of process disturbances and other uncertainties. Intelligent control unit according to the deviation between the artificial test and expectations of quality indicators can feedback correction of the optimal control law. The field experiment results show that the program to stabilize the process of product quality, to achieve the process of saving energy. The grinding process of the optimal control of distributed simulation platform for the optimal control method and system design provide effective, convenient, reliable and intuitive engineering lab environment. Also it has important reference value to other metallurgical optimization of industrial process control engineering verification and simulation.

• Periodical: Applied Mechanics and Materials (Volume 203)
• Pages: 221-225
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.203.221
• Online since: October 2012
• Authors: De Yan Wang
• Keywords: Control System, Grinding, Programmable Logic Controller (PLC), Simulation Studies

References

[1] Tie M, Yue H, Chai T Y. A Hybrid Intelligent Soft-sensor Model for Dynamic Particle Size Estimation in Grinding Circuits [J]. Lecture Notes in Computer Science (S0302-9743), 2005, 3498(3): 871-876.

DOI: 10.1007/11427469_138

[2] Radhakrishnan V. Model Based Supervisor Control of a Ball Mill Grinding Circuit [J]. Journal of Process Control (S0959-1524), 1999, 9(3): 195-211.

DOI: 10.1016/s0959-1524(98)00048-1

[3] Ramasamy M, Narayanan S, Rao C. Control of Ball Mill Grinding Circuit Using Model Predictive Control Scheme [J]. Journal of Process Control (S0959-1524), 2005, 15(3): 273-283.

DOI: 10.1016/j.jprocont.2004.06.006

[4] Liu Y, Spencer S. Dynamic Simulation of Grinding Circuits [J]. Minerals Engineering (S0892-6875), 2004, 17(11): 1189-1198.

DOI: 10.1016/j.mineng.2004.05.018

[5] Tangsathitkulchai C. Effects of Slurry Concentration and Powder Filling on the Net Mill Power of a Laboratory Ball Mill [J]. Powder Technology: (S0032-5910), 2003, 137(3): 131-138.

DOI: 10.1016/j.powtec.2003.08.048

[6] Chen Xi-song. Constrained Model Predictive Control in Ball-mill Grinding Process [J]. Powder Technology, 2008, 18(6): 31-39.

DOI: 10.1016/j.powtec.2007.10.026