Integration of process knowledge and statistical learning for the Dow data challenge problem. (October 2021) - Joe Qin, S.; Guo, Siyi; Li, Zheyu; Chiang, Leo H.; Castillo, Ivan; Braun, Birgit; Wang, Zhenyu
Highlights: A statistical learning procedure for Dow data challenge problem (Braun et al., 2020) is presented that integrates process knowledge in all steps from pre-processing and model interpretation. An accurate inferential sensor model is built with online bias learning based on new data to predict the impurity in the product stream with apparent drifts. Least angle regression solution (LARS) is shown to select only one variable among a set of collinear variables. We report the detection of an equipment-switching operation in the data and interpolations found in the impurity data, which leads to unique data pre-processing measures. Using a softplus function, we propose a method to deal with non-negative physical property modeling. Abstract: In this paper, we propose a statistical learning procedure that integrates process knowledge for the Dow data challenge problem presented in Braun et al. (2020). The task is to build an accurate inferential sensor model to predict the impurity in the product stream with apparent drifts. The proposed method consists of i) process data exploratory analysis, ii) a method for variable selection, iii) a method to deal with non-negative physical property modeling using a softplus function; and iv) a method for online bias updating based on known data. We make use of process operation knowledge in all steps of data analytics, including exploratory analysis and feature selection. We report the detection of equipment-switching operations in the data and interpolations found in the impurity data. Partial least squares (PLS) and least angle regression solution (LARS) are adopted to model the data with strong collinearity. Pros and cons of LARS and PLS are given with practical implications.