An approach to hybrid modelling in chromatographic separation processes
File(s)
Published version
Author(s)
Michalopoulou, Foteini
Papathanasiou, Maria M
Type
Journal Article
Abstract
Chromatographic separation process models are described by nonlinear partial differential and algebraic equations, often leading to high computational cost that limits their applicability in real-time applications. To address this, in this work we propose a hybrid modelling approach that integrates artificial neural networks with process knowledge to describe the system nonlinear dynamics. Specifically, the separation isotherm is maintained in its mechanistic form, while the need for spatial discretisation is eliminated, reducing computational effort by 97 % in the open-loop simulation. The resulting hybrid model relies solely on experimentally measurable variables and performs well both in interpolation and extrapolation tests. It is further utilised within a process optimisation framework, for the maximisation of process yield and product purity. The results demonstrate that the hybrid model accurately captures the intricate dynamics of chromatographic separations while providing a computationally efficient alternative, making it an effective tool for development in industrial applications.
Date Issued
2025-03-01
Date Acceptance
2024-12-20
Citation
Digital Chemical Engineering, 2025, 14
ISSN
2772-5081
Publisher
Elsevier
Journal / Book Title
Digital Chemical Engineering
Volume
14
Copyright Statement
© 2024 The Authors. Published by Elsevier Ltd on behalf of Institution of Chemical Engineers (IChemE). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
10.1016/j.dche.2024.100215
Publication Status
Published
Article Number
100215
Date Publish Online
2024-12-21