Dynamic optimization of dry reformer under catalyst sintering using neural networks. (1st February 2018)
- Record Type:
- Journal Article
- Title:
- Dynamic optimization of dry reformer under catalyst sintering using neural networks. (1st February 2018)
- Main Title:
- Dynamic optimization of dry reformer under catalyst sintering using neural networks
- Authors:
- Azzam, Mazen
Aramouni, Nicolas Abdel Karim
Ahmad, Mohammad N.
Awad, Mariette
Kwapinski, Witold
Zeaiter, Joseph - Abstract:
- Highlights: Artificial neural networks (ANN's) used to model dry reformer operation. Genetic algorithm (GA) developed to design optimal ANN architectures. GA generated ANN model used to optimize reactor performance under catalyst sintering. Optimum conditions of high temperatures with increasing pressure to compensate for sintering. Abstract: Artificial neural networks (ANN's) have been used to optimize the performance of a dry reformer with catalyst sintering taken into account. In particular, we study the effects of temperature, pressure and catalyst diameter on the methane and CO2 conversions, as well the H2 to CO ratio and the molar percentage of solid carbon deposited on the catalyst. The design of the ANN was automated using a genetic algorithm (GA) with indirect binary encoding and an objective function that uses the effective number of parameters provided by Bayesian regularization. Results show that an industrially-acceptable catalyst lifespan for a dry reformer can be achieved by periodically optimizing temperatures and pressures to accommodate for the change in catalyst diameter caused by sintering. In particular, it was found that the reactor's operation favors high temperatures of almost 1000 °C, while the pressure must be gradually increased from 1 to 5 bars to remain as far as possible from carbon limits and ensure acceptable conversions and molar ratios in the syngas.
- Is Part Of:
- Energy conversion and management. Volume 157(2018)
- Journal:
- Energy conversion and management
- Issue:
- Volume 157(2018)
- Issue Display:
- Volume 157, Issue 2018 (2018)
- Year:
- 2018
- Volume:
- 157
- Issue:
- 2018
- Issue Sort Value:
- 2018-0157-2018-0000
- Page Start:
- 146
- Page End:
- 156
- Publication Date:
- 2018-02-01
- Subjects:
- Artificial neural networks -- Genetic algorithm -- Reforming -- Syngas -- Ni catalyst
Direct energy conversion -- Periodicals
Energy storage -- Periodicals
Energy transfer -- Periodicals
Énergie -- Conversion directe -- Périodiques
Direct energy conversion
Periodicals
621.3105 - Journal URLs:
- http://www.sciencedirect.com/science/journal/01968904 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.enconman.2017.11.089 ↗
- Languages:
- English
- ISSNs:
- 0196-8904
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3747.547000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 17947.xml