A benchmark of industrial polymerization process for thermal runaway process monitoring

[thumbnail of benchmark accepted version.pdf]
Text - Accepted Version
· Restricted to Repository staff only until 14 November 2025.
· Available under License Creative Commons Attribution Non-commercial No Derivatives.
Restricted to Repository staff only until 14 November 2025

Please see our End User Agreement.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Li, S., Yang, S.-h. orcid id iconORCID: https://orcid.org/0000-0003-0717-5009, Cao, Y., Jiang, X. and Zhou, C. (2025) A benchmark of industrial polymerization process for thermal runaway process monitoring. Process Safety and Environmental Protection, 193. pp. 353-363. ISSN 1744-3598 doi: 10.1016/j.psep.2024.11.057

Abstract/Summary

Polymer production is of paramount importance in the chemical manufacturing industry. However, safety concerns are prevalent due to the exothermic nature of polymerization reactions, which can cause thermal runaway. The limitations of the current industry-standard monitoring methods underscore the need for novel techniques to detect faults early. To facilitate the development and evaluation of such algorithms, benchmarks that enable direct comparisons of performance are required. Addressing this gap, the present work first introduces an open-source polymerization benchmark model and associated datasets. Derived from reaction kinetics, mass balance, and energy balance analysis, the differential equation forms the basis of our model. By manipulating relative parameters, we intentionally induce five typical faults that can lead to thermal runaway. As a result, our benchmark model serves as an invaluable tool for advancing and validating algorithms for thermal runaway process monitoring, significantly enhancing the safety of the polymerization process. The effectiveness of the model and dataset is demonstrated by testing multivariate statistical process monitoring algorithms and deep learning algorithms.

Altmetric Badge

Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/119782
Identification Number/DOI 10.1016/j.psep.2024.11.057
Refereed Yes
Divisions Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher Elsevier
Download/View statistics View download statistics for this item

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar