A hybrid SVR-PSO model to predict a CFD-based optimised bubbling fluidised bed pyrolysis reactor. (15th January 2020)

• Record Type: Journal Article
• Authors: Jalalifar, Salman; Masoudi, Mojtaba; Abbassi, Rouzbeh; Garaniya, Vikram; Ghiji, Mohammadmahdi; Salehi, Fatemeh
• Is Part Of: Energy. Volume 191(2020)
• Publication Date: 2020-01-15
• Subjects: Support vector regression (SVR) -- Particle swarm optimisation (PSO) -- Computational fluid dynamic (CFD) simulation -- Bubbling fluidised bed reactor -- Fast pyrolysis process; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116414
• Languages: English
• ISSNs: 0360-5442

Abstract

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. Highlights: 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.