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A hybrid SVR-PSO model to predict a CFD-based optimised bubbling fluidised bed pyrolysis reactor

Jalalifar, S ORCID: 0000-0003-3921-9846, Masoudi, M, Abbassi, R, Garaniya, V ORCID: 0000-0002-0090-147X, Ghiji, M and Salehi, C 2019 , 'A hybrid SVR-PSO model to predict a CFD-based optimised bubbling fluidised bed pyrolysis reactor' , Energy, vol. 191 , pp. 1-12 , doi: 10.1016/

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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 Type: Article
Authors/Creators:Jalalifar, S and Masoudi, M and Abbassi, R and Garaniya, V and Ghiji, M and Salehi, C
Keywords: support vector regression (SVR), particle swarm optimisation (PSO), computational fluid dynamic (CFD) simulation, bubbling fluidised bed reactor, fast pyrolysis process
Journal or Publication Title: Energy
Publisher: Pergamon-Elsevier Science Ltd
ISSN: 0360-5442
DOI / ID Number: 10.1016/
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© 2019 Elsevier Ltd. All rights reserved.

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