Soft sensor model for dynamic processes based on multichannel convolutional neural network

Abstract

Soft sensors have been extensively used to predict the difficult-to-measure key quality variables. The robust soft sensors should be able to sufficiently extract the local dynamic and nonlinear features of process data for accurate prediction. Convolutional neural network (CNN) has shown powerful performance in local feature representation that is suitable for soft sensor modeling. However, the process variables that have a distant topological structure usually cannot be covered within the same convolution kernel when applying CNN to process data, which results in the fact that local correlations of those distant process variables are not captured. Therefore, a new multichannel CNN (MCNN) is proposed for various local dynamic feature representation. As a key step, a multichannel 3-D tensor is augmented for each sample as the input to the MCNN model. For the 3-D tensor, each channel has specific local correlations of certain variables, while the variables have different neighborhood relationships for different channels, which refer the various local correlations of different combination variables. Combining with the time axis of each channel, the various local dynamic correlations of different variable combinations can be learnt using MCNN regardless of their distance. The feasibility and effectiveness of MCNN-based soft sensor are demonstrated on the industrial debutanizer column and hydrocracking process.