In the literature, machine learning (ML) and artificial intelligence (AI) applications tend to
start with examples that are irrelevant to process engineers (eg classification of images …

This paper presents an interpretable ensemble modelling method, in which the predictions
of several individual base learners are combined together through Stacked generalisation …

Abstract In the Industry 4.0 era, the chemical industry is embracing broad adoption of
artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic …

In this article, we explore the connection of partial least squares (PLS) to other regularized
regression algorithms including the Lasso and ridge regression, and consider a steepest …

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 …

Although the Lasso method has been popular for variable selection in regression modeling,
it has been known to yield very different model structures with minor perturbations of the …

Latent variable regression (LVR) constructs the latent structure by maximizing the projection
of quality variables on the latent spaces of the process variables, which has demonstrated its …

In this paper, a two-step sparse learning approach is proposed for variable selection and
model parameter estimation with optimally tuned hyperparameters in each step. In Step one …

Industrial data are usually collinear, which can cause pure data-driven sparse learning to
deselect physically relevant variables and select collinear surrogates. In this article, a novel …

In the process industry, the challenge of missing data significantly impairs the efficacy of
data-driven process monitoring systems and soft sensor modeling, particularly due to issues …