Brown, S.F., Ogden, M.D. and Fraga, E.S. (2018) Efficient simulation of chromatographic separation processes. Computers and Chemical Engineering, 110. pp. 69-77. ISSN 0098-1354
Abstract
This work presents the development and testing of an efficient, high resolution algorithm developed for the solution of equilibrium and non-equilibrium chromatographic problems as a means of simultaneously producing high fidelity predictions with a minimal increase in computational cost. The method involves the coupling of a high-order WENO scheme, adapted for use on non-uniform grids, with a piecewise adaptive grid (PAG) method to reduce runtime while accurately resolving the sharp gradients observed in the processes under investigation. Application of the method to a series of benchmark chromatographic test cases, within which an increasing number of components are included over short and long spatial domains and containing shocks, shows that the method is able to accurately resolve the discontinuities and that the use of the PAG method results in a reduction in the CPU runtime of up to 90%, without degradation of the solution, relative to an equivalent uniform grid.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Elsevier. This is an author produced version of a paper subsequently published in Computers and Chemical Engineering. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Column chromatography; WENO scheme; Adaptive mesh refinement |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Chemical and Biological Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Jan 2018 10:11 |
Last Modified: | 20 Dec 2018 01:38 |
Published Version: | https://doi.org/10.1016/j.compchemeng.2017.12.006 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.compchemeng.2017.12.006 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:125592 |