Search

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.

Multicolor Flow Cytometry (MFC)-based gating allows the selection of cellular (pheno)types based on their unique marker expression. Current manual gating practice is highly subjective and may remove relevant information to preclude discovery of cell populations with specific co-expression of multiple markers. Only multivariate approaches can extract such aspects of cell variability from multi-dimensional MFC data. We describe the novel method ECLIPSE (Elimination of Cells Lying in Patterns Similar to Endogeneity) to identify and characterize aberrant cells present in individuals out of homeostasis. ECLIPSE combines dimensionality reduction by Simultaneous Component Analysis with Kernel Density Estimates. A Difference between Densities (DbD) is used to eliminate cells in responder samples that overlap in marker expression with cells of controls. Thereby, subsequent data analyses focus on the immune response-specific cells, leading to more informative and focused models. To prove the power of ECLIPSE, we applied the method to study two distinct datasets: the in vivo neutrophil response induced by systemic endotoxin challenge and in studying the heterogeneous immune-response of asthmatics. ECLIPSE described the well-characterized common response in the LPS challenge insightfully, while identifying slight differences between responders. Also, ECLIPSE enabled characterization of the immune response associated to asthma, where the co-expressions between all markers were used to stratify patients according to disease-specific cell profiles.

To make industrial processes lean, inclusion of technical process information is required into statistical modelling. Understanding how parts of a process are related to other parts and to output quality is key to understanding and controlling processes. In this work, we show how PLS path modelling can be used to incorporate process knowledge into predictive chemical process analysis. The result is a wealth of information which is not obtained by standard data analytic techniques commonly used by analytical chemists or process engineers. By comparing model parameters across multiple data sets from different batches of the same process, model parameters could be used as soft sensors. Some variables which would normally be discarded as uninformative were highly predictive of production costs. The methodology reported here improves chemical process understanding through the analysis of complementary historical process data, which may serve as the basis for development of improved process conditions and control.