A Numerical Study On Bio-Oil Gasification Using A Multicomponent Approach

Developed by: AIChE
  • Type:
    Conference Presentation
  • Conference Type:
    AIChE Annual Meeting
  • Presentation Date:
    October 17, 2011
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A Numerical Study on Bio-Oil Gasification Using A Multicomponent Approach

Bio-oil, also called pyrolysis oil, is a complex biorenewable fuel produced from fast pyrolysis of biomass. Since bio-oil is carbon dioxide neutral, it can be an ideal replacement for petroleum fuel to be used in many kinds of combustion devices. It can also be gasified to produce synthesis gas, which in turn can be used for power generation or synthesized to produce liquid fuels. The objective of this study is to investigate the gasification/vaporization characteristics of bio-oil drops under gasfier and combustor conditions. In this study, numerical methods considering the actual composition of bio-oil are mainly used in the simulation of gasification/vaporization processes. Literature using experimental approaches such as Gas Chromatography/Mass Spectrometer has shown that bio-oil components can be categorized into several groups, including carbohydrates, furans, phenols, guaiacols, syringols, water, etc. By analyzing the composition of bio-oil produced from different sources, a group of ten dominant components are identified from the above groups. Using group contribution methods, the critical and physical properties of these dominant components are estimated. Simplifying bio-oil as a mixture of these dominant components, a multi-component drop vaporization model using a discrete component approach is developed in the simulation of gasification/vaporization. Using the vaporization model, the vaporization histories of single bio-oil drops with different initial compositions, which are the mass fractions of the components determined by the feedstock and pyrolysis process, are calculated. In addition, the dynamics of bio-oil spray is also studied numerically, and the distributions of the vapors of various bio-oil components in the reactor are also predicted.

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