A hybrid SVR-PSO model to predict a CFD-based optimised bubbling fluidised bed pyrolysis reactor
Jalalifar, S and Masoudi, M and Abbassi, R and Garaniya, V and Ghiji, M and Salehi, C, A hybrid SVR-PSO model to predict a CFD-based optimised bubbling fluidised bed pyrolysis reactor, Energy, 191 Article 116414. ISSN 0360-5442 (2020) [Refereed Article]
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.
support vector regression (SVR), particle swarm optimisation (PSO), computational fluid dynamic (CFD) simulation, bubbling fluidised bed reactor, fast pyrolysis process