Soft Sensor for Melt Index Prediction Based on Long Short-Term Memory Network
Min Jun Song, Sungkyu Kim, Seung Hwan Oh, Pil Sung Jo, Jong Min Lee
Abstract
This paper presents a soft sensor model for melt index (MI) prediction in an industrial polymerization process based on long short-term memory (LSTM) network. MI is one of the important specifcations that determine the quality and grade of thermoplastic polymers. However, lack of online measurement of MI makes it difcult to monitor and control the quality of polymer products. Thus, there has been a great efort to build accurate soft sensor models to predict MI with easy-to-measure process variables by using black-box modeling approaches. However, real chemical processes have strong nonlinearity and complicated temporal correlations between the process and quality variables, which is very challenging for traditional static black-box models to handle. Recently, LSTM network that is an advanced form of recurrent neural network (RNN) has shown great advantages in capturing and modeling the long-term dynamic nature of complex industrial processes. We develop an LSTM-based MI prediction model for an industrial styrene-acrylonitrile (SAN) polymerization process in Korea. The developed model provides the most accurate predictions compared to other soft sensor models based on partial least squares (PLS), support vector machines (SVM), Gaussian process regression (GPR), and feedforward artificial neural network (ANN).