ConvLSTM and Self-Attention Aided Canonical Correlation Analysis for Multioutput Soft Sensor Modeling

Abstract

Polymerization process produces industrially important products; hence, its monitoring and control are of paramount importance. However, the non-availability of real time (on demand) measurement of quality variables gives rise to difficulties in achieving effective monitoring and control. To overcome this hurdle, a novel multi-output soft sensor algorithm is proposed for simultaneous estimation of four quality variables (rate of esterification, degree of polymerization, average molecular weight and melt viscosity index) of the industrial polymerization process. The proposed soft sensor is established on canonical correlation analysis with the help of deep learning techniques. It is tested through process data collected from a real polyester plant. The results of the proposed soft sensor are compared with those of the soft sensors constructed using state-of-the-art machine learning algorithms. It is found that the proposed soft sensor shows superior prediction accuracy. The proposed soft sensor has advantages such as ability to extract complex feature extraction, capable of dealing with overfitting and offering quick estimations for the quality variables of interest. In addition, both statistical analysis and sensitivity analysis are carried out on the proposed soft sensor.