A data-driven digital twin for water ultrafiltration

Abstract Membrane-based separations are proven and useful industrial-scale technologies, suitable for automation. Digital twins are models of physical dynamical systems which continuously couple with data from a real world system to help understand and control performance. However, ultrafiltration a...

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Bibliographic Details
Published in:Communications engineering 2022-09, Vol.1 (1), p.23, Article 23
Main Authors: Møller, Jan Kloppenborg, Goranović, Goran, Brath, Per, Madsen, Henrik
Format: Article
Language:English
Subjects:
Control algorithms
Control theory
Differential equations
Digital twins
Energy consumption
Membrane separation
Membranes
Microfiltration
Modelling
Ordinary differential equations
Osmosis
Partial differential equations
Process controls
Separation
Ultrafiltration
Uncertainty
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Summary:Abstract Membrane-based separations are proven and useful industrial-scale technologies, suitable for automation. Digital twins are models of physical dynamical systems which continuously couple with data from a real world system to help understand and control performance. However, ultrafiltration and microfiltration membrane separation techniques lack a rigorous theoretical description due to the complex interactions and associated uncertainties. Here we report a digital-twin methodology called the Stochastic Greybox Modelling and Control (SGMC) that can account for random changes that occur during the separation processes and apply it to water ultrafiltration. In contrast to recent probabilistic approaches to digital twins, we use a physically intuitive formalism of stochastic differential equations to assess uncertainties and implement updates. We demonstrate the application of our digital twin model to control the filtration process and minimize the energy use under a fixed water volume in a membrane ultrafiltration of artificially simulated lakewater. The explicit modelling of uncertainties and the adaptable real-time control of stochastic physical states are particular strengths of SGMC, which makes it suited to real-world problems with inherent unknowns.
ISSN:2731-3395
2731-3395
DOI:10.1038/s44172-022-00023-6