At high heating rates of PF flame or in a blast furnace tuyere the volatile yield is greater than that
measured by a proximate analysis. The "Q factor" is that ratio of the expected high temperature
yield compared to the proximate volatile matter. There is very little data published on the higher
temperature volatile yield of Australian coals, the data of Wall and others (1992), shown in the
figure below, does indicate high Q factors for high rank coals. The data from Ashman and others
(1999) and Haywood and others (1995) show similar trends to the data of Wall and others, though
the Q factors calculated from this data for low volatile coals were not as great. CoalTech has
compared over 11 different methods for determining high temperature volatile yield.

Lately there has been considerable literature on the use of phenomenological coal models to
predict devolatilisation, volatile nitrogen release and char formations. Generalised devolatilisation
models (network models) are based on chemical/physical descriptions of the structure and
processes of the coal particle as the particle heats up and pyrolyses. The three main coal
devolatilisation models that include nitrogen release are:
- FG-DVC [1] functional group-depolymerization vaporization cross- linking
- FLASHCHAIN [2]
- CPD [3,4,5] chemical percolation- devolatilisation
The reported predictions of these devolatilisation models are shown below.
Solomon and Fletcher reviewed the predictive ability of these network models. Brewster and others
(1995) found that FG- DVC gave improved predictions of mass loss compared to the usual two step
model for coal devolatilisation. FLASHCHAIN is being incorporated into EPRI’s software package
Coal Quality Impact Model to improve the NOx predictions.
The CPD model differs from other network models in that only one empirical parameter is used to fit
the devolatilisation of all coals, all other coal-dependent structural coefficients are taken directly
from 13C NMR measurements. Recently, Perry (1999) expanded CPD model to include nitrogen
release as tar and light gases (CPD- NLG). To extend the use of CPD to when 13C NMR
measurements were not available Genetti (1999) produced equations of best fit to allow coal
proximate and ultimate analysis to be used to determine the NMR based inputs to the model.
Some work has been carried out by CoalTech to evaluate the CPD-NLG model, this has been
limited to evaluating 8 coals using the NMR inputs calculated based on Genetti’s work. Reasonable
agreement was found with Entrained flow reactor data, but for wire- mesh data (higher
temperatures) the CPD-NLG model predicts a smaller increase in volatile yield than the actual
data. Typical yield curves for char (fchar), tar (ftar), light gases (fgas) and total volatiles (ftot) with
the fraction conversion of nitrogen to the major species are given in Figure below.

To ascertain the influence of peak temperature on the CPD-NLG model predictions two final
temperatures were used, 1500 C and 2000 C, with a heat up time of 300 ms. For a high volatile
coal, the volatile yield did not increase significantly (Q factor of 1.01), the volatile nitrogen increased
by about 5% and the char nitrogen decreased by 5%. For a medium volatile coal (24 % daf), the
volatile yield only increased by 2% (Q factor of 1.45), the volatile nitrogen increased also by 2%
and the char nitrogen decreased by 2%.
[1] Solomon P., et el 1993, Fuel 72:469.
[2] Niksa S., 1996, “Assess coal quality impacts on your personal computer”, 1996 International
AFRF Symposium.
[3] Fletcher T., 1992, Energy & Fuels, 6:414.
[4] Fletcher T., 1999, “User’s manual for the CPD Model”, Brigham Young University, , 1999.
[5] Fletcher T., Kerstein A.R., Pugmire R.J., Solum M., Grant D.M., 1999, “A chemical percolation
model for devolatilization : summary”, Brigham Young University, , 1999.
[6] Jones J.M., Patterson P.M., Pourkashanian M., Williams A., Arenillas A., Rubiera F., Pis J.J.,
1999b, “Modelling NOx formation in coal particle combustion at high temperature: an investigation
of the devolatilisation kinetic factors”, Fuel 78, 1999.
[7] Niksa S., Muzio L., Fang T., Hurt R., Sun J., Mehta A., Stalling J., 1999, “Assess coal quality
impacts on NOx and LOI with EPRI’s NOx-LOI predictor”, Coal Quality Evaluation Tools, EPRI
1999.