Dual-layer feature extraction based soft sensor methods and applications to industrial polyethylene processes

Abstract

In chemical processes, products of different grades are often produced. Data measured in each grade have different latent features. Multiple data sets can be measured corresponding to different grades. To handle the modeling of multiple nonlinear data sets, multiple two-dimensional matrices of various grades are stacked into a three-dimensional tensor. Then a high-order tensor-based method, named high-order partial least squares (HOPLS), determines the common features among the multiple data sets. PLS is sequentially used to calculate the implied special features of each grade so that the grades can be distinguished from one another. A dual-layer feature extraction method is also proposed. To keep the prediction accuracy of HOPLS-PLS in nonlinear multi-grade processes, a just-in-time learning strategy, named JHOPLS-PLS, is used to establish local HOPLS-PLS models for each query sample. JHOPLS-PLS can extract both common and special features of multi-grade process data. The proposed JHOPLS-PLS has obvious advantages over existing methods.