A Soft Sensor Model for Predicting Cement-Specific Surface Area Based on Convolution Fast Recurrent Unit Network

Abstract

Due to the time lag, nonlinearity, and variable coupling in the process sector, production variables are hard to measure directly and require accurate soft sensor models. In light of this, this research proposes a soft sensor convolution neural network fast recurrent unit (CNN-FRU) network model. Convolution is used to get spatial features and get rid of redundancy data in variables. FRU changes the linear coupling constraint within a gated recurrent unit (GRU) and solves the problem of blocked information flow in GRU. It can be used to extract time-varying delay characteristics from complex processes industry data. FRU is compared with GRU and long short-term memory (LSTM) on a standard dataset to demonstrate the advantages of FRU in terms of training speed and accuracy. As a specific application in the process industry, the proposed soft sensor model is trained and validated on the cement-specific surface area dataset. The experimental results demonstrate that the soft sensor model can be generalized better.