A spatio-temporal data decoupling convolution network model for specific surface area prediction in cement grind process

The specific surface area is one of the important indicators for measuring the quality of cement products. Realizing accurate prediction for specific surface area is very important for the production scheduling of the cement industry, energy conservation and consumption reduction and improvement of cement performance. However, due to the non-linearity, uncertainty, multiple interference, dynamic time-varying delay and multi scales in cement grinding process, it is difficult to establish an accurate soft-sensing model for cement quality prediction. Aiming at the above problems, we proposed a spatio-temporal decoupling convolution neural network model (STG-DCNN) to predict specific surface area by extracting and fusing data features in temporal and spatial dimension. To complete the prediction of specific surface area, we established the temporal series map and spatial series map by the production variables data according to the mechanism of cement grinding process. Then, sliding window technique was utilized to match the time scale in temporal series map and construct variable coupling relationship in spatial series map. The prediction accuracy, robustness and superiority of the proposed method were demonstrated by experiments results implemented on the actual cement grinding quality management database in a cement production enterprise.