Abstract
This paper presents a coal mill model that considers the effect of coal moisture on its accuracy. This mathematical model is derived through the analysis of mass flow, heat exchange, and energy transferring balances in which all heat input into or output from the coal mill are calculated quantitatively to reduce the number of unknown parameters that need to be identified. The work presented in this paper focuses on modeling Mill Parter Ship-type coal mills that are widely used in the coal-fired power plants in China. The unknown model parameters are identified using a real-coded genetic algorithm. Simulation results indicate that the model effectively represents the mid-high process of coal mill dynamics and can be used to estimate the key parameters in coal mills, which are difficult to measure or cannot be measured. On this basis, the model can be used for the state monitoring, control optimization, and fault diagnosis of coal mills.
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