TY - JOUR
T1 - A hybrid SVR-PSO model to predict a CFD-based optimised bubbling fluidised bed pyrolysis reactor
AU - Jalalifar, Salman
AU - Masoudi, Mojtaba
AU - Abbassi, Rouzbeh
AU - Garaniya, Vikram
AU - Ghiji, Mohammadmahdi
AU - Salehi, Fatemeh
PY - 2020/1/15
Y1 - 2020/1/15
N2 - 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.
AB - 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.
KW - Support vector regression (SVR)
KW - Particle swarm optimisation (PSO)
KW - Computational fluid dynamic (CFD) simulation
KW - Bubblingfluidised bed reactor
KW - Fast pyrolysis process
UR - http://www.scopus.com/inward/record.url?scp=85075540783&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2019.116414
DO - 10.1016/j.energy.2019.116414
M3 - Article
AN - SCOPUS:85075540783
SN - 0360-5442
VL - 191
SP - 1
EP - 12
JO - Energy
JF - Energy
M1 - 116414
ER -