Optimization of methane catalytic decomposition in a fluidized bed reactor: a computational approach

The catalytic decomposition of methane (CDM) in fluidized bed reactors offers a solution for turquoise hydrogen generation with valuable solid carbon as a byproduct. Precise numerical simulations are essential for optimizing reaction processes. This paper presents a novel 2D computational fluid dyna...

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Main Authors: Tong, Sirui, Miao, Bin, Zhang, Weike, Zhang, Lan, Chan, Siew Hwa.
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/173191
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1731912024-01-20T16:48:41Z Optimization of methane catalytic decomposition in a fluidized bed reactor: a computational approach Tong, Sirui Miao, Bin Zhang, Weike Zhang, Lan Chan, Siew Hwa. School of Mechanical and Aerospace Engineering Energy Research Institute @ NTU (ERI@N) Engineering::Mechanical engineering Turquoise Hydrogen Generation Catalytic Decomposition Of Methane The catalytic decomposition of methane (CDM) in fluidized bed reactors offers a solution for turquoise hydrogen generation with valuable solid carbon as a byproduct. Precise numerical simulations are essential for optimizing reaction processes. This paper presents a novel 2D computational fluid dynamics (CFD) model based on a multiphase Euler-Euler framework for simulating the CDM over a typical Cu-based catalyst in a fluidized bed reactor. The model incorporates an Arrhenius-based deactivation kinetics model considering catalyst deactivation behaviour when carbons are formed and deposited onto the surface of catalysts. The inclusion of catalyst deactivation in the model is crucial for simulating the dynamic fluidization behaviour in the bed layer. The model validation includes determining the minimum fluidization velocity (Umf) and evaluating the CDM performance under various operating conditions, while concurrently investigating the effect of the gas flow rate through a parametric study. The simulation results revealed that decreased flow rates extended the methane residence time in the catalyst bed layer, thereby increasing the conversion rate and effective catalyst lifespan. The findings of this study would highlight the optimization of industrial-scale CDM processes and provide valuable insights for subsequent experimental designs and improvements. Published version 2024-01-17T00:45:16Z 2024-01-17T00:45:16Z 2023 Journal Article Tong, S., Miao, B., Zhang, W., Zhang, L. & Chan, S. H. (2023). Optimization of methane catalytic decomposition in a fluidized bed reactor: a computational approach. Energy Conversion and Management, 297, 117719-. https://dx.doi.org/10.1016/j.enconman.2023.117719 0196-8904 https://hdl.handle.net/10356/173191 10.1016/j.enconman.2023.117719 2-s2.0-85173619512 297 117719 en Energy Conversion and Management © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
Turquoise Hydrogen Generation
Catalytic Decomposition Of Methane
spellingShingle Engineering::Mechanical engineering
Turquoise Hydrogen Generation
Catalytic Decomposition Of Methane
Tong, Sirui
Miao, Bin
Zhang, Weike
Zhang, Lan
Chan, Siew Hwa.
Optimization of methane catalytic decomposition in a fluidized bed reactor: a computational approach
description The catalytic decomposition of methane (CDM) in fluidized bed reactors offers a solution for turquoise hydrogen generation with valuable solid carbon as a byproduct. Precise numerical simulations are essential for optimizing reaction processes. This paper presents a novel 2D computational fluid dynamics (CFD) model based on a multiphase Euler-Euler framework for simulating the CDM over a typical Cu-based catalyst in a fluidized bed reactor. The model incorporates an Arrhenius-based deactivation kinetics model considering catalyst deactivation behaviour when carbons are formed and deposited onto the surface of catalysts. The inclusion of catalyst deactivation in the model is crucial for simulating the dynamic fluidization behaviour in the bed layer. The model validation includes determining the minimum fluidization velocity (Umf) and evaluating the CDM performance under various operating conditions, while concurrently investigating the effect of the gas flow rate through a parametric study. The simulation results revealed that decreased flow rates extended the methane residence time in the catalyst bed layer, thereby increasing the conversion rate and effective catalyst lifespan. The findings of this study would highlight the optimization of industrial-scale CDM processes and provide valuable insights for subsequent experimental designs and improvements.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Tong, Sirui
Miao, Bin
Zhang, Weike
Zhang, Lan
Chan, Siew Hwa.
format Article
author Tong, Sirui
Miao, Bin
Zhang, Weike
Zhang, Lan
Chan, Siew Hwa.
author_sort Tong, Sirui
title Optimization of methane catalytic decomposition in a fluidized bed reactor: a computational approach
title_short Optimization of methane catalytic decomposition in a fluidized bed reactor: a computational approach
title_full Optimization of methane catalytic decomposition in a fluidized bed reactor: a computational approach
title_fullStr Optimization of methane catalytic decomposition in a fluidized bed reactor: a computational approach
title_full_unstemmed Optimization of methane catalytic decomposition in a fluidized bed reactor: a computational approach
title_sort optimization of methane catalytic decomposition in a fluidized bed reactor: a computational approach
publishDate 2024
url https://hdl.handle.net/10356/173191
_version_ 1789483203621289984