Abstract
Numerical simulations of chromatography are conventionally performed using reduced-order models that homogenize aspects of flow and transport in the radial and angular dimensions. This enables much faster simulations at the expense of lumping the effects of inhomogeneities into a column dispersion coefficient, which requires calibration via empirical correlations or experimental results. We present a high-definition model with spatially resolved geometry. A stabilized space–time finite element method is used to solve the model on massively parallel high-performance computers. We simulate packings with up to 10,000 particles. The impact of particle size distribution on velocity and concentration profiles as well as breakthrough curves is studied. Our high-definition simulations provide unique insight into the process. The high-definition data can also be used as a source of ground truth to identify and calibrate appropriate reduced-order models that can then be applied for process design and optimization.
Original language | English (US) |
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Pages (from-to) | 108355 |
Journal | Computers and Chemical Engineering |
Volume | 178 |
DOIs | |
State | Published - Jul 31 2023 |
Bibliographical note
KAUST Repository Item: Exported on 2023-09-06Acknowledgements: This work was conducted during the Ph.D. studies of Andreas Püttmann and Jayghosh Rao. The authors gratefully acknowledge the funding and support of the JARA-SSD program. The authors also gratefully acknowledge the computing time granted through JARA on the supercomputer JURECA ( Centre, 2018; Jülich Supercomputing Centre, 2021) at Forschungszentrum Jülich. Figures 1 and 2 reprinted from Püttmann et al. (2014) with permission from Elsevier.
ASJC Scopus subject areas
- General Chemical Engineering
- Computer Science Applications