Estimation of gross calorific value of coals using artificial neural networks

The gross calorific value (GCV) is an important property defining the energy content and thereby efficiency of fuels, such as coals. There exist a number of correlations for estimating the GCV of a coal sample based upon its proximate and/or ultimate analyses. These correlations are mainly linear in...

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Bibliographic Details
Published in:Fuel (Guildford) 2007-02, Vol.86 (3), p.334-344
Main Authors: Patel, Shagufta U., Jeevan Kumar, B., Badhe, Yogesh P., Sharma, B.K., Saha, Sujan, Biswas, Subhasish, Chaudhury, Asim, Tambe, Sanjeev S., Kulkarni, Bhaskar D.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial neural network
Coal and derived products
Energy
Exact sciences and technology
Fuels
Gross calorific value (GCV)
Proximate and ultimate analyses
Structure, chemical and physical properties
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Summary:The gross calorific value (GCV) is an important property defining the energy content and thereby efficiency of fuels, such as coals. There exist a number of correlations for estimating the GCV of a coal sample based upon its proximate and/or ultimate analyses. These correlations are mainly linear in character although there are indications that the relationship between the GCV and a few constituents of the proximate and ultimate analyses could be nonlinear. Accordingly, in this paper a total of seven nonlinear models have been developed using the artificial neural networks (ANN) methodology for the estimation of GCV with a special focus on Indian coals. The comprehensive ANN model developed here uses all the major constituents of the proximate and ultimate analyses as inputs while the remaining six sub-models use different combinations of the constituents of the stated analyses. It has been found that the GCV prediction accuracy of all the models is excellent with the comprehensive model being the most accurate GCV predictor. Also, the performance of the ANN models has been found to be consistently better than that of their linear counterparts. Additionally, a sensitivity analysis of the comprehensive ANN model has been performed to identify the important model inputs, which significantly affect the GCV. The ANN-based modeling approach illustrated in this paper is sufficiently general and thus can be gainfully extended for estimating the GCV of a wide spectrum of solid, liquid and gaseous fuels.
ISSN:0016-2361
1873-7153
DOI:10.1016/j.fuel.2006.07.036