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The accuracy of the predicted value of specific surface area plays a significant guiding function in the production and scheduling of cement, as it is a major index impacting the quality of cement. However, due to data characteristics in the cement grinding process, such as time delay, strong coupling and nonlinearity, and the traditional prediction model being a static small sample feature acquisition method based on time correlation, problems such as poor data feature representation and low accuracy exist. In order to achieve an accurate prediction of cement specific surface area, we propose a deep learning prediction model based on Dual Temporal Extraction Network (DTENet). This network is divided into two parts: encoder and decoder. The network uses a dual temporal feature extraction mechanism in the encoder part. Different from the LSTM network which only focuses on the correlation information of a single time step, we realize the mechanism of temporal feature extraction in the short term and long term respectively by constructing a two-stage sliding window to send the data into different temporal feature extraction networks. This network can greatly improve the prediction accuracy for cement specific surface area data with large time lag, redundancy and variable working conditions. In the decoder part, we use the channel attention approach to enhance the spatial information extraction capability of the model. The result of the experiments shows that our model has superior accuracy in cement specific surface area prediction when compared to LSTM, XGBoost and ARIMA models.

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As the most critical equipment in the pre-calcination process of dry cement production, the temperature of the precalciner is an essential factor affecting the quality of cement. However, the cement calcination system is time-delayed, nonlinear, and multi-disturbance, which makes it difficult to predict and control the precalciner temperature. In this study, a deep learning-based Hammerstein model is proposed, and a model predictive control system is built to predict and control the precalciner temperature. In the prediction model, the CNN-GRU network architecture is used to extract the operating states of the precalciner, and an attention mechanism is employed to find and emphasize the important historical information in the extracted states. Then, an ARX model is built to predict the temperature of the precalciner using the extracted operating state information. The complex nonlinear model solution in the control system is formed into a linear control problem and an inverse solution problem. The generalized predictive control (GPC) is used for linear control, and the improved sparrow search algorithm (ISSA) is used for the problem of an inverse solution. Tested with data from a cement plant in Hebei, China, the prediction accuracy of the model proposed in this paper is 99%, and the established control algorithm has less overshoot compared to PID and better stability in anti-disturbance tests. It is demonstrated that the prediction model developed in this study has better accuracy and the control strategy based on this model has good robustness.