Ammonia decomposition in a porous catalytic reactor to enable hydrogen storage: Numerical simulation, machine learning, and response surface methodology. (30th November 2022)
- Record Type:
- Journal Article
- Title:
- Ammonia decomposition in a porous catalytic reactor to enable hydrogen storage: Numerical simulation, machine learning, and response surface methodology. (30th November 2022)
- Main Title:
- Ammonia decomposition in a porous catalytic reactor to enable hydrogen storage: Numerical simulation, machine learning, and response surface methodology
- Authors:
- Pourali, Mostafa
Esfahani, Javad Abolfazli
Jahangir, Hosein
Farzaneh, Ali
Kim, Kyung Chun - Abstract:
- Abstract: Ammonia decomposition is a promising technique for storing and producing hydrogen without carbon emissions. Herein, the potential of hydrogen production via ammonia decomposition in a porous catalytic shell and tube reactor is studied for the first time. The underlying relationship between eight process variables, including reactor structural and operational ones, and the system performance is developed by the aids of computational fluid dynamics (CFD), artificial neural network (ANN), and response surface methodology (RSM). It is found that the reactor (shell-side) inlet velocity, tube inlet temperature, reactor inlet temperature, and porosity are the most influential parameters in ammonia conversion, system efficiency, hydrogen flow rate, and pressure drop, respectively. Moreover, three optimal designs with minimal pressure drop are proposed, considering different optimization objectives. The suggested designs, that are suitable for constructing experimental prototypes, have a porosity of around 0.8 and pore diameters >1.5 mm. Highlights: Intensification of ammonia decomposition in a porous reactor is presented. Optimization is conducted by machine learning and response surface methodology. Correlations for NH3 conversion, H2 production rate and pressure drop are provided. Reactor inlet velocity is the leading parameter in NH3 conversion. A porosity >0.6 is desired to minimize pressure drop in the reactor.
- Is Part Of:
- Journal of energy storage. Volume 55:Part D(2022)
- Journal:
- Journal of energy storage
- Issue:
- Volume 55:Part D(2022)
- Issue Display:
- Volume 55, Issue D (2022)
- Year:
- 2022
- Volume:
- 55
- Issue:
- D
- Issue Sort Value:
- 2022-0055-NaN-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-11-30
- Subjects:
- Ammonia decomposition -- Porous reactor -- CFD simulation -- Machine learning -- Response surface methodology
Energy storage -- Periodicals
Energy storage -- Research -- Periodicals
621.3126 - Journal URLs:
- http://www.sciencedirect.com/science/journal/2352152X ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.est.2022.105804 ↗
- Languages:
- English
- ISSNs:
- 2352-152X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 24412.xml