Modeling And Nonlinear Predictive Control Of Coal Mill System

As an important auxiliary equipment in the power plant, coal mill plays an important role in the safety and economical operation of the whole unit. Coal mill system is a complex multi-variable object with nonlinearity, large time-delay and coupling. Therefore, establishing a first principle model and analyzing its dynamics are of great theoretical and practical significance. This paper firstly establishes a differential equation based mathematical model of single coal mill system. Then, it solves the key problem in online measument of outlet coal using a moving horizon estimator. Finally, a nonlinear predictive control algorithm is presented and applied in the coal mill system. The contents of this paper can be listed as follows:1. Introducing the structure of the coal mill system, and establishing the mathematical models of the coal feeder, air pipes and coal mill. The parameter identification for this system is carried out by means of Prediction Error Method, using working datas when plant runs at different condition. After that, this paper introduces the step response test to study the dynamic characteristics of the system.2. Estimating the outlet coal by Moving Horizon Estimation (MHE) and comparing estimation results with Extended Kalman Filter. It analyses the robustness of MHE and shows the effect of the length of moving horizon. The results illustrate that MHE performs better and has strong robustness.3. Introducing a 3 input-3 output inferential nonlinear predictive control method on the basis of mathematical coal mill model and estimated outlet coal. A conventional controller is obtained to conduct comparison. Then, this paper carries out 4 simulaitons(including coal feeding disturbance, moisture changing, ash changing and the operating condition chaning) to illustrate how does this algorithm manage to avoid the limitations of conventional control strategy.