A Soft-Sensing Model of Cement-Specific Surface Area Based on MTENet

Due to the prominent time-delay problem in the data characteristics of the cement grinding process, traditional soft-sensing models are difficult to effectively deal with this problem, leading to low measurement accuracy. We propose a soft-sensing method based on Multistep Time Extraction Network (MTENet). This method can effectively handle the time-delay problem, thus realizing the accurate soft-sensing of the cement-specific surface area. The encoder contains a new time feature extraction unit we proposed, which can effectively solve the time-delay problem of feature data through a clever combination with ConvLSTM. We also introduce parallel units to make the network aware of the surface area values. In the decoder part, we adopt the method of concatenating parallel features to ensure the accuracy of the final decoding result. Compared with some representative models on real data, the experimental results show that MTENet has a higher accuracy in cement-specific surface area soft-sensing.