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 and Fatemeh Salehi

Energy, 2020, vol. 191, issue C

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.

Keywords: Support vector regression (SVR); Particle swarm optimisation (PSO); Computational fluid dynamic (CFD) simulation; Bubbling fluidised bed reactor; Fast pyrolysis process (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:191:y:2020:i:c:s0360544219321097

DOI: 10.1016/j.energy.2019.116414

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