Abstract
ABSTRACTJatropha curcas seed cake is a potential candidate for co-firing with coal. Combustion modelling using Ansys Fluent 14.0 was carried out to assess the combustion and co-firing characteristics of untorrefied and torrefied Jatropha curcas seed cake. The effect of torrefaction on the devolatilisation characteristics, flame properties and consequently NOx pollutant formation was established. Compared to the torrefied biomass, the untorrefied seed cake devolatilised earlier, had a more dispersed flame and higher NO formation. The higher reactivity of the biomass was shown to have a positive effect on the devolatilisation rate of the less reactive coal under co-firing simulations.
Highlights
Progress in the field of combustion technology – with traditional fossil fuels such as coal – has been dependent on empirical data
A significant degree of uncertainty would be expected considering the several variables that affect the operation of the drop tube furnace (DTF), in addition to the unique challenges posed by biomass testing such as the low collection efficiency and inhomogeneity within the biomass itself
The NO flow rates at the outlet were 4.9x10-11 kg/s and 1.3x10-11 kg/s for the untorrefied and torrefied cases, respectively. These results demonstrate the link between the temperature distribution and the NO formation whereby higher NO levels are formed with larger, higher-temperature flames, which is in agreement with the findings reported in the literature (Li et al 2013)
Summary
Progress in the field of combustion technology – with traditional fossil fuels such as coal – has been dependent on empirical data. This data can be sourced from either large-scale plants or laboratory/pilot-scale experiments. Combustion modelling provides an intermediate stage in the process of designing and improving of combustion systems, whereby the two modes of empirical data can be linked (Eaton et al 1999). For a model’s predictions to be useful, they should be validated against experimental data. The usage of modelling allows confident predictions to be made using a small pool of experimental data, representing a cost saving (Barnes 2014)
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