In the new, dry-process method of cement production, the temperature of cement rotary kiln sintering zone is a key factor in ensuring the quality of cement clinker. Based on the auto-regressive with extra inputs model, a finite control set model predictive control with soft constraint of the generalized triangular interval is proposed in this paper for the characteristics of a cement rotary kiln calcination system with multi-variable, multi-time delay, bounded disturbance, and slow dynamic process. Simulation experiments show that the steady-state error of the control algorithm proposed in this paper is smaller with better anti-disturbance performance than that of the traditional reference-trajectory-constrained, predictive control algorithm.

1. [1] V. Ramasamy, R. Kannan, G. Muralidharan, R. K. Sidharthan, G. Veerasamy, S. Venkatesh, and R. Amirtharajan, “A Comprehensive Review on Advanced Process Control of Cement Kiln Process with the Focus on MPC Tuning Strategies,” J. of Process Control, Vol.102, pp. 85-102, 2023. https://doi.org/10.1016/j.jprocont.2022.12.002
2. [2] Z. Tian, S. Li, and Y. Wang, “Coke Oven Flue Temperature Control Based on Improved Implicit Generalized Predictive Control,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.2, pp. 203-213, 2018. https://doi.org/10.20965/jaciii.2018.p0203
3. [3] J. Xu, C. Chen, and H. Yang, “Temperature Control System for Diode Laser Based on PID Control and Genetic-Algorithm,” J. of Shenyang University of Technology, Vol.39, pp. 449-453, 2017. http://doi.org/10.7688/j.issn.1000-1646.2017.04.17
4. [4] B. Zhao and H. Li, “Temperature Control of Semi-Conductor Laser with PID Parameter Tuning,” J. of Shenyang University of Technology, Vol.39, pp. 444-448, 2017.
5. [5] Z. T. Xue and Z. Li, “Application of Fuzzy Neural Network Controller for Cement Rotary Kiln Control System,” Advanced Materials Research. Trans Tech Publications Ltd., Vol.457, pp. 531-535, 2012.
6. [6] H. Zermane and H. Mouss, “Internet and Fuzzy Based Control System for Rotary Kiln in Cement Manufacturing Plant,” Int. J. of Computer Intelligence Systems, Vol.10, No.1, pp. 835-850, 2017. https://doi.org/10.2991/ijcis.2017.10.1.56
7. [7] V. Gomathi, S. Srinivasan, K. Ramkumar, and G. Muralidharan, “Structural Analysis Based Sensor Measurement Fault Diagnosis in Cement Industries,” Control Engineering Practice, Vol.64, pp. 148-159, 2017. https://doi.org/10.1016/j.conengprac.2017.02.012
8. [8] G. Veerasamy, R. Kannan, R. Siddharthan, G. Muralidharan, V. Sivanandam, and R. Amirtharajan, “Integration of Genetic Algorithm Tuned Adaptive Fading Memory Kalman Filter with Model Predictive Controller for Active Fault-Tolerant Control of Cement Kiln Under Sensor Faults with Inaccurate Noise Covariance,” Mathematics and Computers in Simulation, Vol.191, pp. 256-277, 2022. https://doi.org/10.1016/j.matcom.2021.07.023
9. [9] R. Zhou, Z. Huang, H. Li, J. Peng, and Y. Song, “An MPC Control System for Onboard Ultracapacitors with Maximum Current Constraint,” J. Adv. Comput. Intell. Intell. Inform., Vol.21, No.2, pp. 266-270, 2017. https://doi.org/10.20965/jaciii.2017.p0266
10. [10] D. Zhang, M. Wu, C. Lu, L. Chen, W. Cao, and J. Hu, “Intelligent Compensating Method for MPC-Based Deviation Correction with Stratum Uncertainty in Vertical Drilling Process,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.1, pp. 23-30, 2021. https://doi.org/10.20965/jaciii.2021.p0023
11. [11] C. Sun, Z. Zhou, X. Hao, M. Wang, B. Liu, and P. Zhao, “Model Predictive Control Algorithm Based on Interval Characteristics and Variable Soft Constraints,” Control and Decision, Vol.30, No.10, pp. 1879-1884, 2015. https://doi.org/10.1016/S0098-1354(99)00008-3
12. [12] R. Teja, P. Sridhar, and M. Guruprasath, “Control and Optimisation of a Triple String Rotary Cement Kiln Using Model Predictive Control,” IFAC-PapersOnLine, Vol.49, No.1, pp. 748-753, 2016. https://doi.org/10.1016/j.ifacol.2016.03.146
13. [13] Z. Yang, X. Wang, and H. Yu, “Study on Generalized Predictive Control of Cement Rotary Kiln Calcining Zone Temperature,” 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conf., pp. 1653-1658, 2016. https://doi.org/10.1109/IMCEC.2016.7867498
14. [14] J. S. Hernandez, R. Rivas-Perez, and J. J. Moriano, “Design of a Generalized Predictive Controller for Temperature Control in a Cement Rotary Kiln,” IEEE Latin America Trans., Vol.16, No.4, pp. 1015-1021, 2018. https://doi.org/10.1109/TLA.2018.8362131
15. [15] V. Ramasamy, R. K. Sidharthan, R. Kannan, and G. Muralidharan, “Optimal Tuning of Model Predictive Controller Weights Using Genetic Algorithm with Interactive Decision Tree for Industrial Cement Kiln Process,” Processes, Vol.7, No.12, Article No.938, 2019. https://doi.org/10.3390/pr7120938
16. [16] A. Wurzinger, H. Leibinger, S. Jakubek, and M. Kozek, “Data Driven Modeling and Nonlinear Model Predictive Control Design for a Rotary Cement Kiln,” IFAC-PapersOnLine, Vol.52, No.16, pp. 759-764, 2019. https://doi.org/10.1016/j.ifacol.2019.12.054
17. [17] E. Skouras and V. Burganos, “Three-Dimensional Simulation of the Effects of Demolition Waste Recycling into Rotary Cement Kilns,” Industrial & Engineering Chemistry Research, Vol.56, No.1, pp. 301-310, 2017. https://doi.org/10.1021/acs.iecr.6b03759
18. [18] L. Minchala, Y. Zhang, and L. Garza-Castañón, “Predictive Control of a Closed Grinding Circuit System in Cement Industry,” IEEE Trans. on Industrial Electronics, Vol.65, No.5, pp. 4070-4079, 2017. https://doi.org/10.1109/TIE.2017.2762635
19. [19] G. Ahmadi and M. Teshnelab, “Identification of Multiple Input-Multiple Output Non-Linear System Cement Rotary Kiln Using Stochastic Gradient-Based Rough-Neural Network,” J. of AI and Data Mining, Vol.8, No.3, pp. 417-425, 2020. https://doi.org/10.22044/jadm.2020.8865.2021
20. [20] L. Iheukwumere-Esotu and A. Yunusa-Kaltungo, “Knowledge Criticality Assessment and Codification Framework for Major Maintenance Activities: A Case Study of Cement Rotary Kiln Plant,” Sustainability, Vol.13, No.9, Article No.4619, 2021. https://doi.org/10.3390/su13094619
21. [21] A. Naregalkar and S. Durairaj, “A Novel LSSVM-L Hammerstein Model Structure for System Identification and Nonlinear Model Predictive Control of CSTR Servo and Regulatory Control,” Chemical Product and Process Modeling, Vol.17, No.6, pp. 619-635, 2022. https://doi.org/10.1515/cppm-2021-0020
22. [22] A. Faudzi, N. Lazim, and K. Suzumori, “Modeling and Force Control of Thin Soft McKibben Actuator,” Int. J. Automation Technol., Vol.10, No.4, pp. 487-493, 2016. https://doi.org/10.20965/ijat.2016.p0487
23. [23] T. Fujita, T. Xi, R. Ikeda, S. Kehne, M. Fey, and C. Brecher, “Identification of a Practical Digital Twin for Simulation of Machine Tools,” Int. J. Automation Technol., Vol.16, No.3, pp. 261-268, 2022. https://doi.org/10.20965/ijat.2022.p0261
24. [24] S. Guan, X. Wu, and Z. Wu, “Model Predictive Zone Control with Soft Constrained Appending Margin,” Asian J. of Control, Vol.23, No.6, pp. 2776-2785, 2021. https://doi.org/10.1002/asjc.2413
25. [25] X. Liu, L. Qiu, Y. Fang, K. Wang, Y. Li, and J. Rodriguez, “A Fuzzy Approximation for FCS-MPC in Power Converters,” IEEE Trans. on Power Electronics, Vol.37, No.8, pp. 9153-9163, 2022. https://doi.org/10.1109/TPEL.2022.3157847
26. [26] A. Gonzalez-Prieto, C. Martin, I. González-Prieto, M. J. Duran, J. Carrillo-Ríos, and J. J. Aciego, “Hybrid multivector FCS–MPC for six-phase electric drives,” IEEE Trans. on Power Electronics, Vol.37, No.8, pp. 8988-8999, 2022. https://doi.org/10.1109/TPEL.2022.3154470