Understanding chemical production processes by using PLS path model parameters as soft sensors

Geert H. van Kollenburg (Corresponding author), Jacoline van Es, Jan Gerretzen, Heleen Lanters, Roel Bouman, Willem Koelewijn, Anthony N. Davies, Lutgarde M. C. Buydens, Henk-Jan van Manen, Jeroen J. Jansen

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (Scopus)


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.
Original languageEnglish
Article number106841
Number of pages8
JournalComputers and Chemical Engineering
Publication statusPublished - 4 Aug 2020
Externally publishedYes


This research was in part funded by the Netherlands Organization for Scientific Research (NWO) through the PTA-COAST3 “Outfitting the Factory of the Future with Online analysis” (OFF/On) consortium. The authors would like to thank Kamiel Mellema and Rianne Timmermans for their shared expertise on the modelled production process.

FundersFunder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek


    • PAT
    • Soft sensors
    • PLS-PM
    • Predictive modelling
    • Process analytics
    • Chemometrics


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