Nonlinear coal mill modeling and its application to model predictive control

Abstract Coal mills play an important role in the overall dynamic response of coal fired power plants and there is significant potential to improve the load ramp rates of coal fired power plants through improvements of coal mill control strategies. This potential can be employed to compensate for the power fluctuations generated by renewable but intermittent energy sources such as wind and solar in a more efficient way. In this paper a three state coal mill model based on heat and mass balances as well as a single step coarse to fine particle grinding relationship is presented with the purpose of predicting the dynamic behavior of coal mills during both start-up and in normal operation. The parameters of the model were identified and later validated with measurements obtained from a hard coal fired power plant. During these studies several parameters were found to be time varying. In order to estimate the values of these time varying parameters and the internal states of the coal mill an extended Kalman filter was designed. The proposed solution is observed to achieve very good agreement with measurements and can be used for various applications such as model-based control, performance monitoring, fault detection, and maintenance scheduling. In order to demonstrate one of these use cases a nonlinear model predictive control (NMPC) application was developed based on the coal mill model and the performance of the NMPC was compared to a conventional coal mill control strategy for tracking load change references and for rejecting disturbances caused by variations in coal moisture. The results demonstrate that the coal mill control system performance can be significantly improved through the use of the model presented in this paper.