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Computational Modeling of Multiphase Reactors

Computational Modeling of Multiphase Reactors Multiphase reactors are very common in chemical industry, and numerous review articles exist that are focused on types of reactors, such as bubble columns, trickle beds, fluid catalytic beds, etc. Currently, there is a high degree of empiricism in the design process of such reactors owing to the complexity of coupled flow and reaction mechanisms. Hence, we focus on synthesizing recent advances in computational and experimental techniques that will enable future designs of such reactors in a more rational manner by exploring a large design space with high-fidelity models (computational fluid dynamics and computational chemistry models) that are validated with high-fidelity measurements (tomography and other detailed spatial measurements) to provide a high degree of rigor. Understanding the spatial distributions of dispersed phases and their interaction during scale up are key challenges that were traditionally addressed through pilot scale experiments, but now can be addressed through advanced modeling. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annual Review of Chemical and Biomolecular Engineering Annual Reviews

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Publisher
Annual Reviews
Copyright
Copyright © 2015 by Annual Reviews. All rights reserved
ISSN
1947-5438
eISSN
1947-5446
DOI
10.1146/annurev-chembioeng-061114-123229
pmid
26134737
Publisher site
See Article on Publisher Site

Abstract

Multiphase reactors are very common in chemical industry, and numerous review articles exist that are focused on types of reactors, such as bubble columns, trickle beds, fluid catalytic beds, etc. Currently, there is a high degree of empiricism in the design process of such reactors owing to the complexity of coupled flow and reaction mechanisms. Hence, we focus on synthesizing recent advances in computational and experimental techniques that will enable future designs of such reactors in a more rational manner by exploring a large design space with high-fidelity models (computational fluid dynamics and computational chemistry models) that are validated with high-fidelity measurements (tomography and other detailed spatial measurements) to provide a high degree of rigor. Understanding the spatial distributions of dispersed phases and their interaction during scale up are key challenges that were traditionally addressed through pilot scale experiments, but now can be addressed through advanced modeling.

Journal

Annual Review of Chemical and Biomolecular EngineeringAnnual Reviews

Published: Jul 24, 2015

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