Automatically optimizing dynamic synchronization of individual industrial process variables for statistical modelling

Statistical modelling of industrial production data can lead to improved understanding of the process to benefit process monitoring and control routines. The production data required for such models need however to be synchronized in time, a topic sparsely covered in literature. We propose a strateg...

Full description

Saved in:
Bibliographic Details
Published inComputers & chemical engineering Vol. 152; p. 107402
Main Authors Offermans, Tim, Szymańska, Ewa, van Kolllenburg, Geert H., Buydens, Lutgarde M.C., Jansen, Jeroen J.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.09.2021
Subjects
Automation
Dynamic synchronization
Industry
Optimization
Process control
Statistical modelling
Industry
Automation
Dynamic synchronization
Process control
Optimization
Statistical modelling
Online AccessGet full text

Cover

More Information
Summary:Statistical modelling of industrial production data can lead to improved understanding of the process to benefit process monitoring and control routines. The production data required for such models need however to be synchronized in time, a topic sparsely covered in literature. We propose a strategy for data-driven automated optimization of dynamic synchronization of industrial production data, that optimizes the synchronization per process variable and can be applied for on-line monitoring in real-time. The strategy is tested and validated for two relevant production facilities, each of which has multiple production lines or configurations. For all lines and configurations, models predicting the production quality from process variables improved in accuracy using the presented per-variable optimization strategy. Although the prediction accuracy for two models would still be insufficient for real-time monitoring and control, process operators and engineers may still obtain novel process understanding from applying the presented strategy on these models. •Process variables need to be dynamically synchronized before multivariate statistical analysis.•Different synchronization methods are optimal per process variables.•A automated strategy to find the best synchronization method per process variable has been developed and tested.•This per-variable optimization increases modelling accuracy for all five demonstrator processes.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2021.107402