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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.

Item Type:	Article
Authors/Creators:	Jalalifar, S and Masoudi, M and Abbassi, R and Garaniya, V and Ghiji, M and Salehi, C
Keywords:	support vector regression (SVR), particle swarm optimisation (PSO), computational fluid dynamic (CFD) simulation, bubbling fluidised bed reactor, fast pyrolysis process
Journal or Publication Title:	Energy
Publisher:	Pergamon-Elsevier Science Ltd
ISSN:	0360-5442
DOI / ID Number:	10.1016/j.energy.2019.116414
Copyright Information:	© 2019 Elsevier Ltd. All rights reserved.
Related URLs:	Google Scholar: Garaniya, V UTAS Profile: Garaniya, V
Item Statistics:	View statistics for this item

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