A Soft Sensor Model for Cement Specific Surface Area Based on TCN-ASRU Neural Network

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Abstract
One of the key indicators for evaluating finished cement products is the cement specific surface area. The soft sensor model for cement specific surface area serves as the foundation for scheduling cement production and is critical for increasing cement quality. To solve the issue of soft sensor models of cement specific surface area caused by the non-linearity, time lag and strong coupling of the cement industry’s big data, the soft sensor model of Time Convolution Network (TCN) and Attention Simple Recurrent Unit (ASRU) Network is proposed.TCN suppresses the issue of feature redundancy due to data coupling. Meanwhile, an improved network structure ASRU is established considering the long time series of process industrial data. ASRU can quickly screen out the higher value information from a vast amount of information to enhance the sensitivity to information. TCN-ASRU can capture the spatiotemporal features in the input data and the dynamic response relationship on the time sequence to solve the problems including time-varying time delay. The model was trained and validated on a cement specific surface area data-set collected. The results demonstrated that the model has the higher prediction accuracy and good generalization ability.
Funding Information
  • National Natural Science Foundation of China (62073281)
  • Natural Science Foundation of Hebei Province (F2022203088)
  • Hebei Province Innovation Ability Enhancement Program (22567619H)

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