Steady state inferential modeling of temperature and pressure in an air-swept coal pulverizing ball mill

Abstract

Mill discharge temperature and differential pressure have a strong effect on efficiency and safety of a coal fired power plant. Therefore, it is imperative that they are closely monitored and controlled during mill operation to keep their levels within a predetermined safe and efficient operating range regardless of the rate at which the raw coal is fed to the mill. One way to achieve this is through a control schedule that compares the value obtained from the process to the stored set-point value to determine if there is any deviation that requires correction. This paper describes a steady state model that could be used alongside conventional controllers as an on-line shadow that provides inferential estimates of desired temperatures and pressure drops in the mill circuit which can be continuously compared with the actual values for adjustment. This would not only help to avoid the difficulties associated with direct measurement but also provide a means for early detection of drifts and failing sensors and serve as a temporal back-up for the out-of-order sensors. The model was tested using industrial data collected from four ball mills at a coal fired power plant in South Africa and the results show a reasonable agreement between the measured data and model predictions both qualitatively and quantitatively within a 5% error margin. The model outputs were found to be highly sensitive to the variation in mill loading, the primary air (PA) flow and the mill channel dimensions. Therefore, for validity of this model, accurate determination of all significant parameters is essential. For now, the model is only valid for the ball mills involved in the current study, but with availability of data it can be reproduced elsewhere.