A machine learning approach to improve ignition properties of high-ash Indian coals by solvent extraction and coal blending

Abstract

Indian coals are of poor quality, having high ash content and lower calorific value as compared to foreign coal reserves. To circumvent the problems of high ash content and low calorific value in coal, this study endeavours to explore two methods of improving ignition characteristics of Indian coals: solvent extraction and coal blending. The thermal decomposition behaviour of four Indian coals with and without solvent extraction were investigated through a thermogravimetric analyser under non-isothermal conditions. Kinetic parameters and activation energies of the coals were determined by using Arrhenius, Coats-Redfern, Doyle-Ozawa and Friedman methods. It was found that solvent extraction of the coal reduced the activation energy by approximately 50%. Pearson correlation analysis performed on the chemical composition and ignition properties of 16 typical Chinese coals and 48 of their blends showed that moisture, volatile matter, fixed carbon, calorific value, oxygen, and carbon content of coals were the most relevant factor to ignition temperature and activation energy. Accordingly, three-layer back-propagation neural network models were trained and tested and found to be reasonably accurate. The selected neural networks were used to predict the ignition characteristics of 17 Indian coals. 16 Indian coals were then hypothetically blended with 84 coals from various other countries to form 1344 blends, whose ignition characteristics were further predicted using the trained neural network models. 220 of these 1344 blends were found to have better ignition properties than the original Indian coals. The maximum reductions observed in ignition temperature and activation energy were 79.08 K and 179.38 kJ/mol respectively.