Real‐time optimization using machine learning models applied to the 4,4′‐diphenylmethane diisocyanate production process

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dc.contributor.authorEhlhardt, Jens
dc.contributor.authorAhmad, Afaq
dc.contributor.authorWolf, Inga
dc.contributor.authorEngell, Sebastian
dc.date.accessioned2025-01-31T10:34:23Z
dc.date.available2025-01-31T10:34:23Z
dc.date.issued2023-04-03
dc.description.abstractIn this work, the optimal time-varying allocation of steam in a large-scale industrial isocyanate production process is addressed. This is a problem that falls into the category of real-time optimization (RTO). The application of RTO in practice faces two problems: First the available rigorous process models may not be suitable for use in real-time connected to the process. Second, there is always a mismatch between the predictions of the model and the behavior of the real plant. We address the first problem by training a neural net model as a surrogate to data generated by a rigorous simulation model so that the model is simple to implement and short execution times result. The second problem is tackled by adapting the optimization problem based on measured data such that convergence to the optimal operating conditions for the real plant is achieved.en
dc.identifier.urihttp://hdl.handle.net/2003/43394
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-25226
dc.language.isoen
dc.relation.ispartofseriesChemie - Ingenieur - Technik; 95(7)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectIsocyanate productionen
dc.subjectModifier adaptation with quadratic approximationen
dc.subjectReal-time optimizationen
dc.subjectSurrogate modelsen
dc.subject.ddc660
dc.titleReal‐time optimization using machine learning models applied to the 4,4′‐diphenylmethane diisocyanate production processen
dc.typeText
dc.type.publicationtypeResearchArticle
dcterms.accessRightsopen access
eldorado.secondarypublicationtrue
eldorado.secondarypublication.primarycitationEhlhardt, J., Ahmad, A., Wolf, I. and Engell, S. (2023), Real-Time Optimization Using Machine Learning Models Applied to the 4,4′-Diphenylmethane Diisocyanate Production Process. Chemie Ingenieur Technik, 95: 1096-1103. https://doi.org/10.1002/cite.202200244
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1002/cite.202200244

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