Probabilistic Sequential Network for Deep Learning of Complex Process Data and Soft Sensor Application

Abstract

Soft sensing of quality/key variables is critical to the control and optimization of industrial processes. One of the main drawbacks of data-driven soft sensors is to deal with the dynamic and nonlinear characteristics of process data. This paper proposes a deep learning structure and corresponding training algorithm for the purpose of soft sensor, which is called probabilistic sequential network. The proposed model merges unsupervised feature extraction and supervised dynamic modeling approaches to improve the prediction performance. It is mainly based on the Gaussian-Bernoulli restricted Boltzmann machine and the recurrent neural network structure. To avoid the overfitting problem in the training procedure of deep learning algorithms, the L2 regularization and dropout technique are adopted. The new method can not only deeply extract the nonlinear feature but also widely capture dynamic characteristic of process data. Effectiveness and superiority of the new method are validated through an actual CO2 absorption column, compared to traditional methods.