TY - JOUR
T1 - High-definition simulation of packed-bed liquid chromatography
AU - Rao, Jayghosh Subodh
AU - Püttmann, Andreas
AU - Khirevich, Siarhei
AU - Tallarek, Ulrich
AU - Geuzaine, Christophe
AU - Behr, Marek
AU - von Lieres, Eric
N1 - KAUST Repository Item: Exported on 2023-09-06
Acknowledgements: 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.
PY - 2023/7/31
Y1 - 2023/7/31
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/694116
UR - https://linkinghub.elsevier.com/retrieve/pii/S0098135423002259
UR - http://www.scopus.com/inward/record.url?scp=85166539312&partnerID=8YFLogxK
U2 - 10.1016/j.compchemeng.2023.108355
DO - 10.1016/j.compchemeng.2023.108355
M3 - Article
SN - 0098-1354
VL - 178
SP - 108355
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
ER -