DHP Algorithm Based Multi-variable Optimal Control for Cement Calcination Process
Cement precalciner kiln(PCK) clinker calcination process is a matter of mass transfer, heat transfer, physical and chemical reactions, and more complex multi-variable nonlinear system with more disturbances. In order to reduce energy consumption and to ensure the quality of cement clinker burning, one needs to explore different control methods from the traditional way. In this paper, PCK technology is conducted a detailed analysis, and its model is established by artificial neural network. New controller has been designed to control the model by choosing the appropriate control variables. Dual Heuristic Programming (DHP) is the advanced form of Adaptive Dynamic Programming (ADP) algorithm. Typical DHP structure is consists of three modules: Critic Network, Action Network, and model network. Its Critic network output cost function J’s partial derivative to the state variable and therefore have a higher accuracy, with the corresponding its calculation is much more complex. Its purpose is when minimizing the cost-to-go function, one can find the optimal or sub-optimal control signal, so that the discrete-time nonlinear systems to obtain the desired control trajectory. Simulation results show that the controller response time faster, the parameters have small overshoot which help the stability of the actual system operation. DHP approach with multi-variable control of the clinker calcination process, is an effective way and demonstrate the potential of real-time optimal control.
DHP Neural network Precalciner kiln Clinker calcination process Optimal control
Baosheng Yang Xiushui Ma
Department of Computer Science and Technology, Suzhou University, Suzhou 234000, China Ningbo Institute of Technology, Zhejiang University,Ningbo 315100, China
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
3707-3712
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）