4.7 Article

Automatic Generation of Kinetic Skeletal Mechanisms for Biomass Combustion

Journal

ENERGY & FUELS
Volume 27, Issue 11, Pages 6979-6991

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/ef400949h

Keywords

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Funding

  1. Research Council of Norway
  2. industrial partners in the CenBio-Bioenergy Innovation Centre

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We present in this paper simplified chemical mechanisms for gas phase biomass combustion based on automatic reduction of detailed and comprehensive kinetics. The reduction method that has been employed is a combined reaction flow and sensitivity analysis well-known to combustion, resulting in a necessity index ranking all chemical species for automatic reduction. The objective is to obtain more compact chemical models, so-called skeletal mechanisms, for implementation into computational fluid dynamics, CFD, in order to reduce computational time. In the current work, the physical system used for the development and validation of the chemical models is that of a tubular reactor, or plug flow reactor, with operating conditions typically found in biomass reactors. The focus has been on gas phase reactions only, and the fuel composition is based on experimental values from biomass and coal gasification. Emphasis has been on the reliability of the simplified models and the correct prediction of important emission parameters such as NOx and important intermediate species. The original chemical model, consisting of several sub models for important reaction paths known in biomass combustion, contained 81 species and 1401 reactions. This was successfully reduced down to 36 species, providing a compact and reliable chemical model for implementation into CFD. The model still contains the reaction paths of C-2 species, allowing for more realistic fuel gas compositions. The model has been experimentally validated for a wide range of temperatures including low temperature chemistry and reducing conditions for NOx. The computational time saved using the simplified models was significant with over 80% reduction in CPU time.

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