Prediction of macerals contents of Indian coals from proximate and ultimate analyses using artificial neural networks

• Published in: Fuel (Guildford) Vol. 89; no. 5; pp. 1101 - 1109
• Main Authors: Khandelwal, Manoj, Singh, T.N.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-05-2010; Elsevier
• Subjects: Applied sciences; Artificial neural network (ANN); Coal and derived products; Energy; Exact sciences and technology; Fuels; Macerals; Multi-variate regression analysis (MVRA); Proximate analysis; Structure, chemical and physical properties; Ultimate analysis; Asia; India; Artificial neural network (ANN); Ultimate analysis; Macerals; Proximate analysis; Multi-variate regression analysis (MVRA); Calorific value; Coal; Regression analysis; Neural network; Maceral
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2009.11.028

Coal, a prime source of energy needs in-depth study of its various parameters, such as proximate analysis, ultimate analysis, and its biological constituents (macerals). These properties manage the rank and calorific value of various coal varieties. Determination of the macerals in coal requires sophisticated microscopic instrumentation and expertise, unlike the other two properties mentioned above. In the present paper, an attempt has been made to predict the concentration of macerals of Indian coals using artificial neural network (ANN) by incorporating the proximate and ultimate analysis of coal. To investigate the appropriateness of this approach, the predictions by ANN are also compared with conventional multi-variate regression analysis (MVRA). For the prediction of macerals concentration, data sets have been taken from different coalfields of India for training and testing of the network. Network is trained by 149 datasets with 700 epochs, and tested and validated by 18 datasets. It was found that coefficient of determination between measured and predicted macerals by ANN was quite higher as well as mean absolute percentage error was very marginal as compared to MVRA prediction.