Adaptive Neuro-Fuzzy Inference System Prediction of Calorific Value Based on the Analysis of U.S. Coals

Abstract

Coal is a chemically and physically heterogeneous and combustible substance that consists of both organic and inorganic compounds. It currently is a major energy source worldwide, especially among many developing countries, and will continue to be so for many years (Miller, 2005).The chemical analysis of coal includes proximate and ultimate analyses. The proximate analysis gives the relative amounts of moisture, volatile matter, and ash, as well as the fixed carbon content of the coal. The ultimate or elemental analysis gives the amounts of carbon, hydrogen, nitrogen, sulfur, and oxygen in the coal (Miller, 2005). The measure of the amount of energy that a given quantity of coal will produce when burned is kown as calorific value or heating value. Heating value is a rank parameter and a complex function of the elemental composition of the coal, but it is also dependent on the maceral and mineral composition (Hower and Eble, 1996). It can be determined experimentally using a calorimeter. Many equations have been developed for the estimation of gross calorific value (GCV) based on proximate analysis and/or ultimate analysis (Mason and Gandhi, 1983; Mesroghli et al., 2009; Given et al., 1986; Parikh et al., 2005; Custer, 1951; Spooner, 1951; Mazumdar, 1954; Channiwala and Parikh, 2002; Majumder et al., 2008). Regression analyses and data for 775 U.S. coal samples (with less than 30% dry ash) were used by Mason and Gandhi (1983) to develop an empirical equation that estimates the calorific value (CV) of coal based on its C, H, S, and ash contents (all on dry basis). Their empirical equation, expressed in SI units, is: