The reflectance distribution of coal (the reflectogram), which shows the heterogeneity of coal, can be represented by several reflectance bins. In this paper, in considering the heterogeneity of coal, coal is regarded as a mixture of particles. Each particle is characterized by reflectance and is treated as being homogeneous, without considering the intraparticle heterogeneity. The chemical compositions (volatile matter (VM), carbon content (C%), ratio of hydrogen to carbon (H/C)) of a single particle have been estimated from previous works and are used to derive the 13C NMR parameters of this particle. These parameters are input into the chemical percolation devolatilization (CPD) model to predict the high-temperature VM yields of this particle. The total VM yields of the coal can be obtained by a summation of the results of all of the particles. In the experimental portion, the sink−float technique has been used to separate a series of coals into different maceral-rich fractions, which exhibit very different reflectance distributions. The high-temperature VM yields of these samples have been obtained from drop tube furnace pyrolysis experiments at 1400 °C to validate the model. The results from pyrolysis experiments and the single-particle modeling approach show that, under high temperature, the inertinite-rich fraction produces less VM than does the vitrinite-rich fraction of the same coal. The inertinite-rich fraction has a lower Q factor, which is defined as the ratio of high-temperature VM yields and proximate VM, than its vitrinite counterpart. The high-temperature VM yields also have been estimated from bulk properties of coal samples from the CPD model. These estimations do not agree with the experimental data. The model could not be applied to those coals that showed abnormal reflectance features.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.