Developing a digital twin of a polymerization reaction for process optimization

Bordas, Balazs; Kurt, Kutup; Bamberg, Andreas; Engell, Sebastian

doi:10.1109/asmc54647.2022.9792518

Cited by 2 publications

(3 citation statements)

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“…In our preliminary report [9] on the same system, we observed a linear increase of U with respect to time. One possible explanation for this is that the mobility of the polymer chains in the solution changes with increasing viscosity.…”

Section: Data-based Kernel Model Trainingsupporting

confidence: 53%

“…The estimation strategy follows the principles described in [9] with some modifications. It was previously observed that the kernel estimates resulting from the measurements of this process are not time-independent, therefore a separate estimation for each interval in the dataset is necessary.…”

Section: Estimation Of the Training Setmentioning

confidence: 99%

“…In the present work. we apply a previously developed serial gray-box modeling method for step-growth polymerization [9] to provide decision support for the operators of an industrial batch polymerization reactor at Merck KGaA, Darmstadt, Germany. The serial configuration was chosen because there was no model available for this reaction from the engineering phase, and the complexity of branched step-growth polymerization kinetics would make the development of a custom physical model very time-consuming.…”

Section: Introductionmentioning

confidence: 99%

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Gray‐Box Modeling of the Molecular Weight Distribution in a Batch Polymerization Reactor

Bordas

Kurt

Bamberg

et al. 2023

Chemie Ingenieur Technik

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We introduce a gray‐box approach for modeling the molecular weight distribution in step‐growth polymerization reactions using the aggregation population balance equation. The approach is based on extracting a data‐based kernel function from in‐process measurements of the molecular weight distribution. The method is applied to historical data from an industrial batch polymerization reactor. The resulting model is used for decision support in production by predicting the reaction endpoint corresponding to a target molecular weight. The accuracy of the predictions proved to be sufficient for the deployment of the method.

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Section: Data-based Kernel Model Trainingsupporting

confidence: 53%

Section: Estimation Of the Training Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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