Process PLS: Incorporating substantive knowledge into the predictive modelling of multiblock, multistep, multidimensional and multicollinear process data

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. (C) 2021 The Author(s). Published by Elsevier Ltd.