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

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

Research output: Contribution to journalArticleResearchpeer-review

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.

LanguageEnglish
Article number116414
Pages1-12
Number of pages12
JournalEnergy
Volume191
DOIs
Publication statusPublished - 15 Jan 2020

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Particle swarm optimization (PSO)
Computational fluid dynamics
Pyrolysis
Biomass
Learning algorithms
Learning systems
Dynamic models
Sand
Particle size
Flow rate
Temperature
Computer simulation
Gases
Experiments
Oils

Keywords

  • Support vector regression (SVR)
  • Particle swarm optimisation (PSO)
  • Computational fluid dynamic (CFD) simulation
  • Bubblingfluidised bed reactor
  • Fast pyrolysis process

Cite this

Jalalifar, Salman ; Masoudi, Mojtaba ; Abbassi, Rouzbeh ; Garaniya, Vikram ; Ghiji, Mohammadmahdi ; Salehi, Fatemeh. / A hybrid SVR-PSO model to predict a CFD-based optimised bubbling fluidised bed pyrolysis reactor. In: Energy. 2020 ; Vol. 191. pp. 1-12.
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A hybrid SVR-PSO model to predict a CFD-based optimised bubbling fluidised bed pyrolysis reactor. / Jalalifar, Salman; Masoudi, Mojtaba; Abbassi, Rouzbeh; Garaniya, Vikram; Ghiji, Mohammadmahdi; Salehi, Fatemeh.

In: Energy, Vol. 191, 116414, 15.01.2020, p. 1-12.

Research output: Contribution to journalArticleResearchpeer-review

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