Dual-rate Interval Control of Pump Pool Level and Feeding Pressure During Regrinding
-
摘要: 赤铁矿再磨过程是以矿浆泵频率为输入、以给矿压力为内环输出、以泵池液位为外环输出的强非线性串级工业过程.当赤铁矿粒度分布大范围变化,导致一段磨矿与磁选矿浆流量和再磨排矿流量频繁波动,使泵池液位频繁波动,造成内外环频繁波动.本文将上述动态特性变化用未建模动态来描述,通过设计消除前一时刻未建模动态补偿信号叠加于基于线性模型设计的反馈控制器,采用一步最优前馈控制律和提升技术,提出了泵池液位和给矿压力双速率区间控制算法.给出了所提控制算法的稳定性和收敛性分析,通过半实物仿真实验表明所提出的控制算法可以将处于频繁随机波动的泵池液位和给矿压力变化率控制在目标值范围内.Abstract: The regrinding process of hematite is a strong nonlinear cascade process with frequency of slurry pump as the input, feeding pressure as the inner loop output and sump level as the outer loop output. During its operation, frequent fluctuations of first stage grinding, magnetic separation slurry flowrate and discharge regrinded flowrate generated from the distribution of hematite particles which changes in a large range will cause large variations in the sump level and frequent fluctuation between inner and outer loop. In this paper, a novel dual-rate interval controller of sump level and feeding pressure is developed by representing the variations of dynamics characters as the unmodeled dynamics. In the proposed controller design, a compensation signal is constructed and added onto the control signal obtained from the linear deterministic model based on feedback control design. Such a compensation signal aims at eliminating the previous moment unmodeled dynamics. Meantime, the stability and convergence analysis of the proposed algorithm is given. A simulated experiment on the hardware-in-the-loop simulation system is conducteded, and it is shown that the sump level and the changing rate of feeding pressure in frequent random fluctuations can be well controlled inside their targeted ranges under the proposed control algorithm.
-
Key words:
- Regrinding process /
- unmodeled dynamic compensation /
- PI control /
- dual-rate control
-
表 1 模型参数表
Table 1 Model parameters
变量 描述 $q_1(t)$ 一段磨矿矿浆和磁选矿浆流量 ${q_2}({y_1}(t),{D_H},{f_d},{\phi _H}) $ 再磨排矿流量 $q_3(t)$ 污水与冲洗水流量 $q_4(t)$ 泵池补加水流量 $D_H(t)$ 旋流器给矿浓度 $f_d(t)$ 旋流器给矿粒度分布 $\phi_H$ 旋流器结构参数 $A$ 泵池横截面积 $K_p$ 矿浆泵比例系数 $K_d$ 矿浆泵流量比例系数 $\tau$ 惯性时间常数 -
[1] Li X, McKee D J, Horberry T, Powell M S. The control room operator: the forgotten element in mineral process control. Minerals Engineering, 2011, 24(8): 894-902 doi: 10.1016/j.mineng.2011.04.001 [2] van Vuuren M J J, Aldrich C, Auret L. Detecting changes in the operational states of hydrocyclones. Minerals Engineering, 2011, 24(14): 1532-1544 doi: 10.1016/j.mineng.2011.08.002 [3] Wei D H, Craig I K. Grinding mill circuits——a survey of control and economic concerns. International Journal of Mineral Processing, 2009, 90(1-4): 55-56 [4] Duarte M, Sepúlveda F, Castillo A, Contreras A, Lazcano V, Giménez P, Castelli L. A comparative experimental study of five multivariable control strategies applied to a grinding plant. Powder Technology, 1999, 104(1): 1-28 doi: 10.1016/S0032-5910(98)00210-1 [5] 楚云飞, 徐文立, 王峻, 万维汉.基于切换控制的均匀液位控制.清华大学学报(自然科学版), 2005, 45(1): 107-110 http://www.cnki.com.cn/Article/CJFDTOTAL-QHXB200501028.htmChu Yun-Fei, Xu Wen-Li, Wang Jun, Wan Wei-Han. Averaging level control based on switching control. Journal of Tsinghua University (Science and Technology), 2005, 45(1): 107-110 http://www.cnki.com.cn/Article/CJFDTOTAL-QHXB200501028.htm [6] Pomerleau A, Hodouin D, Desbiens A, Gagnon É. A survey of grinding circuit control methods: from decentralized PID controllers to multivariable predictive controllers. Powder Technology, 2000, 108(2-3): 103-115 doi: 10.1016/S0032-5910(99)00207-7 [7] 梁蕾, 李振国.选矿过程矿浆液位的模糊控制算法研究.金属矿山, 2009, 39(7): 103-105, 135 http://www.cnki.com.cn/Article/CJFDTOTAL-JSKS200907034.htmLiang Lei, Li Zhen-Guo. Fuzzy-intelligent control arithmetic for the ore pulp level in mineral separation process. Metal Mine, 2009, 39(7): 103-105, 135 http://www.cnki.com.cn/Article/CJFDTOTAL-JSKS200907034.htm [8] Aguila-Camacho N, Le Roux J D, Duarte-Mermoud M A, OrchardM E. Control of a grinding mill circuit using fractional order controllers. Journal of Process Control, 2017, 53: 80-94 doi: 10.1016/j.jprocont.2017.02.012 [9] le Roux J D, Padhi R, Craig I K. Optimal control of grinding mill circuit using model predictive static programming: a new nonlinear MPC paradigm. Journal of Process Control, 2014, 24(12): 29-40 doi: 10.1016/j.jprocont.2014.10.007 [10] Coetzee L C, Craig I K, Kerrigan E C. Robust nonlinear model predictive control of a run-of-mine ore milling circuit. IEEE Transactions on Control Systems Technology, 2010, 18(1): 222-229 doi: 10.1109/TCST.2009.2014641 [11] Matthews B, Craig I K. Demand side management of a run-of-mine ore milling circuit. Control Engineering Practice, 2013, 21(6): 759-768 doi: 10.1016/j.conengprac.2013.02.005 [12] 赵大勇, 柴天佑.再磨过程泵池液位区间与给矿压力模糊切换控制.自动化学报, 2013, 39(5): 556-564 http://www.aas.net.cn/CN/abstract/abstract17816.shtmlZhao Da-Yong, Chai Tian-You. Fuzzy switching control for sump level interval and hydrocyclone pressure in regrinding process. Acta Automatica Sinica, 2013, 39(5): 556-564 http://www.aas.net.cn/CN/abstract/abstract17816.shtml [13] Zhao D Y, Chai T Y, Wang H, Fu J. Hybrid intelligent control for regrinding process in hematite beneficiation. Control Engineering Practice, 2014, 22: 217-230 doi: 10.1016/j.conengprac.2013.02.015 [14] Sanchis R, Romero J A, Martín J M. A new approach to averaging level control. Control Engineering Practice, 2011, 19(9): 1037-1043 doi: 10.1016/j.conengprac.2011.04.010 [15] 王泽红, 陈晓龙, 袁致涛, 于福家, 李丽匣.选矿数学模型.北京:冶金工业出版社, 2015.Wang Ze-Hong, Chen Xiao-Long, Yuan Zhi-Tao, Yu Fu-Jia, Li Li-Xia. Mathematical Model of Mineral Processing. Beijing: Metallurgical Industry Press, 2015. [16] 吴学娟, 郎朗.模糊自适应控制在变频恒压供水系统中的应用.工业控制计算机, 2010, 23(11): 53-54 doi: 10.3969/j.issn.1001-182X.2010.11.025Wu Xue-Juan, Lang Lang. Fuzzy Adaptive control of constant pressure water supplying system with frequency conversion. Industrial Control Computer, 2010, 23(11): 53-54 doi: 10.3969/j.issn.1001-182X.2010.11.025 [17] Liu F Z, Gao H J, Qiu J B, Yin S, Fan J L, Chai T Y. Networked multirate output feedback control for setpoints compensation and its application to rougher flotation process. IEEE Transactions on Industrial Electronics, 2014, 61(1): 460-468 doi: 10.1109/TIE.2013.2240640 [18] Chai T Y, Zhang Y J, Wang H, Su C Y, Sun J. Data-based virtual unmodeled dynamics driven multivariable nonlinear adaptive switching control. IEEE Transactions on Neural Networks, 2011, 22(12): 2154-2172 doi: 10.1109/TNN.2011.2167685 [19] Jia Y, Chai T Y. A data-driven dual-rate control method for a heat exchanging process. IEEE Transactions on Industrial Electronics, 2017, 64(5): 4158-4168 doi: 10.1109/TIE.2016.2608878 [20] Chai T Y, Zhai L F, Yue H. Multiple models and neural networks based decoupling control of ball mill coal-pulverizing systems. Journal of Process Control, 2011, 21(3): 351-366 doi: 10.1016/j.jprocont.2010.11.007 [21] Zhang Y J, Chai T Y, Wang D H. An alternating identification algorithm for a class of nonlinear dynamical systems. IEEE Transactions on Neural Networks and Learning Systems, 2016, PP(99): 1-12 https://www.researchgate.net/publication/301278679_An_Alternating_Identification_Algorithm_for_a_Class_of_Nonlinear_Dynamical_Systems?_sg=0GrGvPEsUQO8xrHfwsP-YyaZoz3IXJzhTLh9wx6sBEYsfD1rteepDimRIMqZ-HKCo1BilrLdiewJkJagj94BSA