Intelligent Modeling and Predictive Control of Pre-Grinding System

Abstract:

Stably controlling the pre-grinding process is paramount important for improving the operational efficiency and significantly reducing production costs in cement plants. Recognizing the complexity in both structure and operation of the pre-grinding process, this paper proposed a fuzzy and model predictive control system to stabilize and optimize the pre-grinding process. Based on the available techniques and system analysis, it is divided into two different sub-systems. One is handled by Fuzzy Logic Control (FLC), and the other is implemented by Linear Matrix Inequality (LMI)-based Model Predictive Control (MPC) based on the model achieved by Least Square Support Vector Machine (LS-SVM) regression. With this approach, the control parameters can be obtained online by the use of aforementioned algorithms and applied to the pre-grinding system by using OLE for Process Control (OPC) and relevant software. The trail system has been deployed in the field and its operation clearly demonstrates the effectiveness and feasibility of this control system in practice.

• Periodical: Advanced Materials Research (Volumes 433-440)
• Pages: 2120-2127
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.433-440.2120
• Online since: January 2012
• Authors: Zhi Peng Liu, Qiang Yang, Wen Jun Yan
• Keywords: FLC, Least Squares Support Vector Machines (Ls-SVM), LMI, MPC, Pre-Grinding System

References

[1] W. Zou and J. Zou, Pre-grinding technology and analysis of its application key points, Science and Technology of Cement, no. 4, pp.17-21, (2008).

[2] C. Zhang, Pre-grinding equipment and its evaluation, China Building Material Equipment, no. 7, pp.22-24, (1996).

[3] X. Du, Q. Cheng and W. Lu, Hybrid fuzzy PID decoupling control using in ball mill, in Proc. International Conference on Sustainable Power Generation and Supply, vol. 1-4, pp.2494-2498, (2009).

DOI: 10.1109/supergen.2009.5347934

[4] V. Van Breusegen, L. Chen, V. Werbouck, G. Bastin, and V. Wertz, Multivariable linear quadratic control of a cement mill: An industrialapplication, Contr. Eng. Practica, vol. 2, no. 4, pp.605-611, (1994).

DOI: 10.1016/0967-0661(94)90004-3

[5] L. Magni, G. Bastin, and V. Wertz. Multivariable nonlinear predictive control of cement mills, IEEE Transactions on Control Systems Technology, vol. 7, no. 4, pp.502-508, (1999).

DOI: 10.1109/87.772166

[6] H. Xie, Z. Jiang, X. Liu, etc., Application of fuzzy control of laminar cooling for hot rolled strip, J. Materials Processing Technology, vol. 187, p.715–719, (2007).

DOI: 10.1016/j.jmatprotec.2006.11.166

[7] H. Wang and M. Jia, A fuzzy control method for ball mill system based on fill level soft sensor, in Proc. 21st Chinese Control and Decision Conference, vol. 6, pp.5888-5891, 2009.

DOI: 10.1109/ccdc.2009.5195254

[8] B. Liu, H. Su, W. Huang and J. Chu, Temperature predictive control based least squares support vector machines, Journal of Control Theory and Applications, vol. 2, no. 4, p.65–70, (2004).

DOI: 10.1007/s11768-004-0041-7

[9] J. Suykens and J. Vandewalle Least squares support vector machine classifiers, Neural Process Lett, vol. 3, no. 9, p.293–300, (1999).

[10] X. Li, G.Y. Cao and X.J. Zhu, Modeling and control of PEMFC based on least squares support vector machines, Energy Conversion and Management, vol. 47, no. 7-8, p.1032–1050, (2006).

DOI: 10.1016/j.enconman.2005.04.002

[11] C. De Souza, A. Trofino, An LMI approach to stablization of linear discrete-time periodic systems, International Journal of Control, vol. 73, no. 8, pp.696-703, (2000).

DOI: 10.1080/002071700403466

[12] M. Kothare, V. Balakrishnan, M. Morari, Robust constrained model predictive control using linear matrix inequalities, Automatica, vol. 32, no. 10, pp.1361-1379, (1996).

DOI: 10.1016/0005-1098(96)00063-5

[13] W. Kwon, D. Byun, Receding horzon tracking control as a predictive control and its stability properties, International Journal of Control, vol. 50, no. 5, pp.1807-1824, (1989).

DOI: 10.1080/00207178908953467

[14] K. Kim, J. Lee, W. Kwon, Intervalwise receding horizon H-infinity tracking control for discrete linear periodic systems, IEEE Transactions on Automatic Control, vol. 45, no. 4, pp.747-752, (2000).

DOI: 10.1109/9.847115

[15] G. Denicolao, Cyclomonotonicity, Riccati-Equations and periodic receding horizon control, Automatica, vol. 30, no. 9, pp.1375-1388, (1994).

DOI: 10.1016/0005-1098(94)90002-7

[16] P. Myung-June, R. Hyun-Ku, Robust control system design, Southeast University Press, pp.21-51, (1995).

[17] F. Wu, LMI-based robust model predictive control and its application to an industrial CSTR problem, Journal of Process Control, vol. 11, no. 6, pp.649-659, (2001).

DOI: 10.1016/s0959-1524(00)00052-4

[18] P. Falugi, S. Olaru and D. Dumur, Multi-model predictive control based on LMI: from the adaptation of the state-space model to the analytic description of the control law, International Journal of Control, vol. 83, no. 8, p.1548–1563, (2010).

DOI: 10.1080/00207171003736329