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Modelling of reactive absorption in gas-liquid flows on structured packing

Modelling of reactive absorption in gas-liquid flows on structured packing
Modelling of reactive absorption in gas-liquid flows on structured packing
Carbon capture & storage (CCS) is at the technological forefront in the challenge of reducing carbon emissions. The most viable approach to implementing CCS within existing coal and natural gas power stations is the post-combustion capture of CO2 by absorption into amine solutions within packed column absorbers.

CFD modelling is an important aspect in the design and optimisation of this process. However, significant challenges arise due to the large range of spatial scales and the complexity of the physics being modelled. Therefore, simplification of the problem is required to complete such simulations using the computational resources currently available.

This thesis explores some of the approaches used to model flow within packed columns. It concludes that, with current computing resources, standard modelling approaches are not viable for large scale simulations of CCS. This led to the development of the Enhanced Surface Film (ESF) model. The ESF approach was able to simulate chemically enhanced absorption of gaseous species into thin liquid films. The method significantly reduced the computational resources required and is a significant step to enable future researchers to model larger domains in CCS.

The ESF approach has wide ranging applications due to the ubiquitous nature of liquid films across the industrial and environmental sectors. In many industries the dynamics of thin liquid films play a crucial role in the overall performance. Further applications may include thin film microreactors, surface coating, biofluids and medical applications.
Cooke, Jason
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Cooke, Jason
4d1bd597-c335-4ca3-8cd1-7379f4eb3a56
Armstrong, Lindsay-Marie
db493663-2457-4f84-9646-15538c653998

Cooke, Jason (2016) Modelling of reactive absorption in gas-liquid flows on structured packing. University of Southampton, Faculty of Engineering and the Environment, Doctoral Thesis, 203pp.

Record type: Thesis (Doctoral)

Abstract

Carbon capture & storage (CCS) is at the technological forefront in the challenge of reducing carbon emissions. The most viable approach to implementing CCS within existing coal and natural gas power stations is the post-combustion capture of CO2 by absorption into amine solutions within packed column absorbers.

CFD modelling is an important aspect in the design and optimisation of this process. However, significant challenges arise due to the large range of spatial scales and the complexity of the physics being modelled. Therefore, simplification of the problem is required to complete such simulations using the computational resources currently available.

This thesis explores some of the approaches used to model flow within packed columns. It concludes that, with current computing resources, standard modelling approaches are not viable for large scale simulations of CCS. This led to the development of the Enhanced Surface Film (ESF) model. The ESF approach was able to simulate chemically enhanced absorption of gaseous species into thin liquid films. The method significantly reduced the computational resources required and is a significant step to enable future researchers to model larger domains in CCS.

The ESF approach has wide ranging applications due to the ubiquitous nature of liquid films across the industrial and environmental sectors. In many industries the dynamics of thin liquid films play a crucial role in the overall performance. Further applications may include thin film microreactors, surface coating, biofluids and medical applications.

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More information

Published date: 1 May 2016
Organisations: University of Southampton, Energy Technology Group

Identifiers

Local EPrints ID: 397079
URI: http://eprints.soton.ac.uk/id/eprint/397079
PURE UUID: e92da478-2958-4c37-91c1-c315451779e1

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Date deposited: 11 Jul 2016 14:36
Last modified: 15 Mar 2024 01:05

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Contributors

Author: Jason Cooke
Thesis advisor: Lindsay-Marie Armstrong

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