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.

Item Type:	Refereed Article
Keywords:	support vector regression (SVR), particle swarm optimisation (PSO), computational fluid dynamic (CFD) simulation, bubbling fluidised bed reactor, fast pyrolysis process
Research Division:	Engineering
Research Group:	Automotive engineering
Research Field:	Automotive combustion and fuel engineering
Objective Division:	Energy
Objective Group:	Renewable energy
Objective Field:	Biofuel energy
UTAS Author:	Jalalifar, S (Mr Salman Jalalifar)
UTAS Author:	Garaniya, V (Associate Professor Vikram Garaniya)
ID Code:	136204
Year Published:	2020 (online first 2019)
Web of Science® Times Cited:	20
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2019-12-05
Last Modified:	2020-04-20
Downloads:	0