An uncertainty-aware hybrid modelling approach using probabilistic machine learning

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Abstract

Hybrid modelling has caught renewed attention in many fields of engineering in the last two decades. By combining machine learning with first principles modelling, hybrid modelling is in many cases a more pragmatic modelling approach compared to first principles modelling, and at the same time a more robust alternative to data-driven modelling. However, quantifying uncertainty associated with hybrid models has not been investigated in detail thus far. Thereby, in practice, some models fail to reliably provide information for their performance under uncertainty. In this work, an integrated probabilistic modelling approach is presented for simultaneous modelling and uncertainty quantification using a hybrid model structure. The approach accounts for three types of uncertainty, including training data uncertainty, process stochasticity and model structure uncertainty. To demonstrate the advantages of this approach, the modelling strategy is highlighted through the modelling of a flocculation process. Here, mass and population balance models are combined with a probabilistic machine learning based kinetic model for estimating the particle phenomena kinetics. The model predictions are compared to predictions from a deterministic hybrid model counterpart.
Original languageEnglish
Title of host publicationProceedings of the 31th European Symposium on Computer Aided Process Engineering (ESCAPE30)
EditorsMetin Türkay, Rafiqul Gani
Volume50
Place of PublicationAmsterdam
PublisherElsevier
Publication date2021
Pages591-597
ISBN (Electronic)978-0-323-98325-9
DOIs
Publication statusPublished - 2021
Event31st European Symposium on Computer Aided Process Engineering (ESCAPE 31) - Istanbul, Turkey
Duration: 6 Jun 20219 Jun 2021

Conference

Conference31st European Symposium on Computer Aided Process Engineering (ESCAPE 31)
Country/TerritoryTurkey
CityIstanbul
Period06/06/202109/06/2021
SeriesComputer Aided Chemical Engineering
ISSN1570-7946

Keywords

  • Hybrid modelling
  • Probabilistic modelling
  • Machine learning

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