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

• Published in: Energy (Oxford) Vol. 191; p. 116414
• Main Authors: Jalalifar, Salman, Masoudi, Mojtaba, Abbassi, Rouzbeh, Garaniya, Vikram, Ghiji, Mohammadmahdi, Salehi, Fatemeh
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.01.2020; Elsevier BV
• Subjects: Algorithms; biofuels; Biomass; Bubbling; Bubbling fluidised bed reactor; Carrier gases; Computational fluid dynamic (CFD) simulation; Computational fluid dynamics; Computer applications; Computer simulation; data collection; energy; Fast pyrolysis process; Flow rates; Flow velocity; fluid mechanics; Fluidized beds; Learning algorithms; Machine learning; Mathematical models; Operating temperature; Parameters; Particle swarm optimisation (PSO); Particle swarm optimization; Pretreatment; Pyrolysis; Reactors; regression analysis; sand; Support vector machines; Support vector regression (SVR); temperature; Bubbling fluidised bed reactor; Support vector regression (SVR); Fast pyrolysis process; Computational fluid dynamic (CFD) simulation; Particle swarm optimisation (PSO)
• ISSN: 0360-5442; 1873-6785; 1873-6785
• DOI: 10.1016/j.energy.2019.116414

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. •Outlines a detailed CFD model validated with experimental data in a fast pyrolysis reactor.•Details development of a new model by integrating machine learning tools and CFD simulations.•Predicts the performance of a fast pyrolysis reactor in different operational conditions.•Optimizes the bio-oil yield in a fact pyrolysis reactor considering the variability in effective parameters.