Implementation of Species-Transport Model in Numerical Simulation to Predict the Performance of Downdraft Gasifier with Biomass Blends
Murugan P. C.1, G. Arun Prasad2 & S. Joseph Sekhar3 Show affiliations
- Kongu Engineering College, India
- St. Xavier’s Catholic College of Engineering, India
- Shinas College of Technology, Oman
Energy Research Journal, Volume 10, Issue 1, pp. 36–47 · doi.org/10.3844/erjsp.2019.36.47
Abstract
The demand for energy from biomass gasification is increasing due to the depletion of fossil fuels and serious environmental issues. Due to this, usage of individual biomass in a gasifier is not sufficient to meet the demand in rural areas. However, an enormous study have been reported on the gasifier with single biomass as working fuel. In addition, only a few works have been carried out on biomass blends in a gasifier through equilibrium modelling and experimental approach. The numerical simulation for the biomass blends are in scarce. Hence, in this study, a numerical and experimental studies were conducted on a 40kWth downdraft biomass gasifier to find out the possibility of rubber seed kernel shell and cassava stalk biomass with different blend ratio. The species-transport CFD model is used for numerical simulation with the k-ε turbulence model. The effect of species concentration, higher heating value and conversion efficiency with respect to different equivalence ratio have been observed. The simulated results were also validated through experimentation. Among different blending ratio 80:20 blend shows better performance. The result reveals that the species concentration and higher heating value at 0.2 equivalence ratio has an average value of CO-23.5 %, H2-19.2%, CH4-3.1% and 6.62 MJ/Nm3 with an efficiency of 58.23% respectively. The viability of using the species transport model for biomass blends is confirmed.
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C., M. P., Prasad, G. A. & Sekhar, S. J. (2019). Implementation of Species-Transport Model in Numerical Simulation to Predict the Performance of Downdraft Gasifier with Biomass Blends. Energy Research Journal, 10(1), 36–47. https://doi.org/10.3844/erjsp.2019.36.47
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