球磨机的预测控制策略及应用研究
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摘要
球磨机是选矿厂磨矿过程和火电厂锅炉制粉系统的关键设备之一,它的工作状态直接影响到全厂的生产能力、能耗等各项技术经济指标。球磨机是一个生产工况变化大、非线性、时变、大滞后和多变量耦合的复杂系统,球磨机系统难以建立精确的数学模型,常规控制方法很难达到理想的控制效果,球磨机的自动控制已经成为其推广应用的障碍之一。因此,本文从以上问题出发,研究球磨机的预测控制策略及应用。主要内容如下:
(1)简要介绍了球磨机的工作原理、动态特性及其工业应用,系统地总结了球磨机控制策略的研究情况。
(2)综述了模型预测控制算法、广义预测控制算法和预测函数控制算法等的基本原理,研究了单变量系统基于有限阶跃响应模型的预测函数控制算法。
(3)针对球磨机磨矿回路的复杂特性,采用一种基于有限阶跃响应模型的预测函数控制算法,并将其从单变量系统推广到多变量系统,通过实验型球磨机磨矿回路的仿真研究,表明该控制策略比多回路PID解耦控制具有较好的控制品质。
(4)在分析和研究钢球磨煤机动态特性与动态模型的基础上,设计了一种基于广义预测控制的钢球磨煤机控制策略,为改善钢球磨煤机运行的安全性和经济性提供了保障。仿真研究结果表明,该控制策略具有较好的跟踪性能、抗干扰能力和鲁棒性,可以达到较为理想的控制效果。
最后是全文的总结和展望。
The ball mills is one of the key equipments of grinding process in ore dressing plants and pulverizing system in power plants. Its running state effects main economic and technologic targets of the process systems directly, such as production ability and energy consumption. Ball mill is a complex system with nonlinear, time-variation, large delay and multivariable coupling feature, and its operating conditions often vary violently. It's difficult to build a precise mathematic model of the ball mill and to acquire the ideal control performance with classical control methods. The automation of ball mill has become the obstacle for its commercial application. From these problems, this thesis researches the predictive control strategy and its application on the ball mill. The main works of the thesis are as follows:
(1) The principle, dynamic performance and industrial applications of the ball mill are briefly introduced. Some control strategies for ball mill are researched and summarized systemically.
(2) An overview of Model predictive control (MPC)、Generalized predictive control (GPC) and Predictive functional control (PFC) algorithms is given. PFC algorithm based on finite step response (FSR) model for single input and single output (SISO) system is introduced.
(3) A PFC algorithm based on finite step response (FSR) model is extended to multivariable case for the complex characteristic of the ball mill. Simulation results on a pilot ball mill grinding circuit show that the PFC based on multivariable system has better control performance than the decoupled multi-loop PID control.
(4) Based on analyzing the dynamic characteristic and modeling of ball coal mill, the generalized predictive controller is designed to improve the security and economics of the process. Simulation results show that the control strategy can achieve better control performance of setpoint tracking, disturbance rejection and robustness to model mismatch.
The last part is summary and perspective.
(1)简要介绍了球磨机的工作原理、动态特性及其工业应用,系统地总结了球磨机控制策略的研究情况。
(2)综述了模型预测控制算法、广义预测控制算法和预测函数控制算法等的基本原理,研究了单变量系统基于有限阶跃响应模型的预测函数控制算法。
(3)针对球磨机磨矿回路的复杂特性,采用一种基于有限阶跃响应模型的预测函数控制算法,并将其从单变量系统推广到多变量系统,通过实验型球磨机磨矿回路的仿真研究,表明该控制策略比多回路PID解耦控制具有较好的控制品质。
(4)在分析和研究钢球磨煤机动态特性与动态模型的基础上,设计了一种基于广义预测控制的钢球磨煤机控制策略,为改善钢球磨煤机运行的安全性和经济性提供了保障。仿真研究结果表明,该控制策略具有较好的跟踪性能、抗干扰能力和鲁棒性,可以达到较为理想的控制效果。
最后是全文的总结和展望。
The ball mills is one of the key equipments of grinding process in ore dressing plants and pulverizing system in power plants. Its running state effects main economic and technologic targets of the process systems directly, such as production ability and energy consumption. Ball mill is a complex system with nonlinear, time-variation, large delay and multivariable coupling feature, and its operating conditions often vary violently. It's difficult to build a precise mathematic model of the ball mill and to acquire the ideal control performance with classical control methods. The automation of ball mill has become the obstacle for its commercial application. From these problems, this thesis researches the predictive control strategy and its application on the ball mill. The main works of the thesis are as follows:
(1) The principle, dynamic performance and industrial applications of the ball mill are briefly introduced. Some control strategies for ball mill are researched and summarized systemically.
(2) An overview of Model predictive control (MPC)、Generalized predictive control (GPC) and Predictive functional control (PFC) algorithms is given. PFC algorithm based on finite step response (FSR) model for single input and single output (SISO) system is introduced.
(3) A PFC algorithm based on finite step response (FSR) model is extended to multivariable case for the complex characteristic of the ball mill. Simulation results on a pilot ball mill grinding circuit show that the PFC based on multivariable system has better control performance than the decoupled multi-loop PID control.
(4) Based on analyzing the dynamic characteristic and modeling of ball coal mill, the generalized predictive controller is designed to improve the security and economics of the process. Simulation results show that the control strategy can achieve better control performance of setpoint tracking, disturbance rejection and robustness to model mismatch.
The last part is summary and perspective.
引文
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