Volatile Evolution
The rate and amount of volatile release during the heating of coal is strongly influenced by the rank of the coal and the heating environment.  Volatile evolution directly impacts on flame stability, char morphology and NOx formation in combustion systems and the plastic properties of coal during cokemaking.
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.