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Highlights • A new two-stage feature selection methodology for high-dimensional regression problems is proposed. • It is called wide spectrum feature selection for regression (WiSe). • Pearson correlation, Spearman rank correlation, Symmetrical uncertainty, and all their pairwise combinations are considered in the first stage. • In the second stage, the first-pass screened features are further selected using regression-dependent approaches. • Results confirm the increased predictive accuracy of models built after filtering out irrelevant features. Abstract Developing predictive models from industrial datasets implies the consideration of many possible predictor variables (features). Using all available features for data-driven modelling is not recommended, as most of them are expected to be irrelevant and their inclusion in the model may compromise robustness and accuracy. In this work, we present, test and compare a new two-stage feature selection method called wide spectrum feature selection for regression (WiSe). In the first stage, a combination of efficient bivariate filters analyzes linear and non-linear association patterns between predictors and responses, screening out clearly noisy features. In the second stage, the reduced set of retained features is subject to further selection in the scope of the predictive methods considered, optimizing their predictive performance. Three simulated datasets and an industrial case illustrate the effectiveness and benefits of applying WiSe to support model development in a wide range of high-dimensional regression problems. [ABSTRACT FROM AUTHOR]

• 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. 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. [ABSTRACT FROM AUTHOR]

This paper explores dimensionality reduction (DR) approaches for visualizing high dimensional data in chemical processes. Visualization provides powerful insight and process understanding in the industrial context, and accelerates process troubleshooting. A diverse array of existing, easy-to-use DR methods are evaluated in three case studies on large-scale industrial manufacturing plants. Supervised and unsupervised cases are presented with the objective of solving typical industrial problems related to unplanned events, plant performance improvement, and quality underperformance troubleshooting. For the unsupervised case, the evaluation aims to identify approaches that provide insight beyond those of PCA (Principal Component Analysis), and also examines quality metrics of the reduced (latent) space which characterize the degree of trust in the DR. UMAP (Uniform Manifold Approximation and Projection) outperforms other techniques, bringing new insights when comparing with other methods. For the supervised case, UMAP is combined with traditional variable selection methods, such as VIP (Variable Influence on Projection) weights from PLS-DA (Partial Least Squares Discriminant Analysis), in order to improve latent space visualization by increasing separation between classes. • Dimensionality reduction techniques are applied to three industrial case studies. • UMAP has outperformed other methods that solve unsupervised problems. • Combining UMAP with feature selection techniques improves latent space visualization. [ABSTRACT FROM AUTHOR]