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A hybrid SVR-PSO model to predict a CFD-based optimised bubbling fluidised bed pyrolysis reactor
Citation
Jalalifar, S and Masoudi, M and Abbassi, R and Garaniya, V and Ghiji, M and Salehi, C, A hybrid SVR-PSO model to predict a CFD-based optimised bubbling fluidised bed pyrolysis reactor, Energy, 191 Article 116414. ISSN 0360-5442 (2020) [Refereed Article]
Copyright Statement
© 2019 Elsevier Ltd. All rights reserved.
DOI: doi:10.1016/j.energy.2019.116414
Abstract
Comprehensive scrutiny is necessary to achieve an optimised set of operating conditions for a pyrolysis reactor to attain the maximum amount of the desired product. To reach this goal, a computational fluid dynamic (CFD) model is developed for biomass fast pyrolysis process and then validated using the experiment of a standard lab-scale bubbling fluidised bed reactor. This is followed by a detailed CFD parametric study. Key influencing parameters investigated are operating temperature, biomass flow rate, biomass and sand particle sizes, carrier gas velocity, biomass injector location, and pre-treatment temperature. Machine learning algorithms (MLAs) are then employed to predict the optimised conditions that lead to the maximum bio-oil yield. For this purpose, support vector regression with particle swarm optimisation algorithm (SVR-PSO) is developed and applied to the CFD datasets to predict the optimum values of parameters. The maximum bio-oil yield is then computed using the optimum values of the parameters. The CFD simulation is also performed using the optimum parameters obtained by the SVR-PSO. The CFD results and the values predicted by the MLA for the product yields are finally compared where a good agreement is achieved.
Item Details
Item Type: | Refereed Article |
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Keywords: | support vector regression (SVR), particle swarm optimisation (PSO), computational fluid dynamic (CFD) simulation, bubbling fluidised bed reactor, fast pyrolysis process |
Research Division: | Engineering |
Research Group: | Automotive engineering |
Research Field: | Automotive combustion and fuel engineering |
Objective Division: | Energy |
Objective Group: | Renewable energy |
Objective Field: | Biofuel energy |
UTAS Author: | Jalalifar, S (Mr Salman Jalalifar) |
UTAS Author: | Garaniya, V (Associate Professor Vikram Garaniya) |
ID Code: | 136204 |
Year Published: | 2020 (online first 2019) |
Web of Science® Times Cited: | 20 |
Deposited By: | NC Maritime Engineering and Hydrodynamics |
Deposited On: | 2019-12-05 |
Last Modified: | 2020-04-20 |
Downloads: | 0 |
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