Abstract
Cement rotary kiln is one of the important equipment in cement production. It is mainly used to calcine cement clinker. The temperature of rotary kiln determines the quality of cement clinker. However, in actual production, due to the strong coupling relationship between various variables, it is difficult to directly measure the temperature of the firing zone. Therefore, the temperature of the flue gas chamber at the kiln tail is used to approximately reflect the temperature of the firing zone. In this paper, a data-driven autoregressive moving average (ARMAX) model is established to predict the temperature of the flue gas chamber at the kiln tail. Based on the Akaike information criterion (AIC), the goodness of fit method is introduced to identify the order of the model, and the recursive least squares (RLS) method is used to identify the model parameters. On this basis, model predictive control (MPC) is used to control the rotary kiln. From the simulation results, the improved AIC has higher model accuracy and can control the stability of the rotary kiln in a short time.
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