Abstract
Chemical production processes benefit from intelligent data analysis. Previous work showed how process knowledge can be included in a structural equation modelling framework. While predictive models increase process value, currently available methods have limitations that hinder applicability to many (industrial) processes. This paper describes the Process PLS algorithm which can analyze multi-block, multistep and/or multidimensional processes. Process PLS was benchmarked on a simulated crude oil distillation process. Analysis of 22 empirical data sets from a production process at Nouryon illustrated how Process PLS solves limitations of PLS path modelling. In the analysis of the benchmark Val de Loire data, Process PLS revealed substantially meaningful effects which the recently proposed Sequential Orthogonalized PLS path modelling completely missed. Process PLS is a promising approach that enables data-driven analysis of process data using information on the complex process structure, to demonstrably increase insight in the underlying system, making model-based predictions much more valuable.
Highlights
Improving chemical production processes in an intelligent and data-driven manner is key to the Industry 4.0 directive (Hermann et al, 2016; Lasi et al, 2014)
It was shown that a structural equation modelling (SEM) approach enables the incorporation of various types of process knowledge into imposed associations between process variables within a predictive PLS model
The fact that a linear model like Process PLS can describe most of the variation in the data so accurately is promising, considering that industrial processes may have many non-linear relations (Wang, Liu & Srinivasan, 2010)
Summary
Improving chemical production processes in an intelligent and data-driven manner is key to the Industry 4.0 directive (Hermann et al, 2016; Lasi et al, 2014). One of the main limitations is that PLS-PM assumes that each block of variables can be described with a single latent variable. This so-called unidimensionality assumption is rarely appropriate in physical systems like those found in applications on industrial processes(Ferrer et al, 2008). It was found that multicollinearity, which is common in chemical and physical processes, may lead to unstable models with, for example, standardized regression coefficients greater than 1. Such unstable model parameters limit intuitive model interpretation. The interested reader is referred to Rönkkö et al (2016) for a discussion on the fundamental limitations of PLS-PM
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