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

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  • Jalalifar, Salman
  • Masoudi, Mojtaba
  • Abbassi, Rouzbeh
  • Garaniya, Vikram
  • Ghiji, Mohammadmahdi
  • Salehi, Fatemeh

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.

Suggested Citation

  • Jalalifar, Salman & Masoudi, Mojtaba & Abbassi, Rouzbeh & Garaniya, Vikram & Ghiji, Mohammadmahdi & Salehi, Fatemeh, 2020. "A hybrid SVR-PSO model to predict a CFD-based optimised bubbling fluidised bed pyrolysis reactor," Energy, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219321097
    DOI: 10.1016/j.energy.2019.116414
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