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A hybrid SVR-PSO model to predict a CFD-based optimised bubbling fluidised bed pyrolysis reactor
journal contribution
posted on 2023-05-20, 08:46 authored by Jalalifar, S, Masoudi, M, Abbassi, R, Vikrambhai GaraniyaVikrambhai Garaniya, Ghiji, M, Salehi, CComprehensive 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.
History
Publication title
EnergyVolume
191Article number
116414Number
116414Pagination
1-12ISSN
0360-5442Department/School
Australian Maritime CollegePublisher
Pergamon-Elsevier Science LtdPlace of publication
The Boulevard, Langford Lane, Kidlington, Oxford, England, Ox5 1GbRights statement
© 2019 Elsevier Ltd. All rights reserved.Repository Status
- Restricted