Gross Calorific Value of Indian Coals and its Correlation with Ash Content

##plugins.themes.academic_pro.article.sidebar##

Published Aug 23, 2023
https://doi.org/10.33686/pwj.v19i1.1124
Download
PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 19, Issue 1, June 2023

##plugins.themes.academic_pro.article.main##

V. Saravanan
Central Power Research Institute, Bengaluru – 560012, Karnataka
K. Subbiramani
Central Power Research Institute, Bengaluru – 560012, Karnataka
T. Mallikharjuna Rao
Central Power Research Institute, Bengaluru – 560012, Karnataka

Abstract

The Indian coals are a sub-bituminous variety, high in ash content and low in calorific value. The high ash content in Indian coals makes Indian coals more heterogeneous. The moisture and the ash content have a direct impact on the calorific value of the coals. In the present work the moisture, ash content and GCV have been analyzed for the coals obtained from various locations in India and the relationship between ash content and GCV was established on a dry basis. The variation in GCV to ash content was statistically quantified for the coals from each mine and overall mines.

##plugins.themes.academic_pro.article.details##

How to Cite
Saravanan, V. ., Subbiramani, K. ., & Rao, T. M. . (2023). Gross Calorific Value of Indian Coals and its Correlation with Ash Content. Power Research - A Journal of CPRI, 19(1), 69–73. https://doi.org/10.33686/pwj.v19i1.1124
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Goutal MCR. Acad Sci Paris. 1902; 135:477-9.
  2. Mazumdar BK. Coal systematics: Deductions from proximate analysis of coal Part I. Jour. Sci. Indian Res. 1954; 13B(12):857-63.
  3. Channiwala SA, Parikh PP. A unified correlation for estimating HHV of solid, liquid and gaseous fuels. Fuel. 2002; 81:1051-63. https://doi.org/10.1016/S0016- 2361(01)00131-4
  4. Parikh J, Channiwala SA, Ghosal GK. A correlation for calculating HHV from proximate analysis of solid fuels. Fuel. 2005; 84:487-94. https://doi.org/10.1016/j. fuel.2004.10.010
  5. Matin SS, Chelgani SC. Estimation of coal gross calorific valuebased on various analyses by random forest method. Fuel. 2016; 177:274-8. https://doi.org/10.1016/j. fuel.2016.03.031
  6. Majumder AK, Jain R, Banerjee P, Barnwal JP. Development of a new proximate analysis based correlation to predict calorific value of coal. Fuel. 2008; 87:3077-81. https://doi.org/10.1016/j.fuel.2008.04.008
  7. Chelgani SC, Makaremi S. Explaining the relationship between common coal analyses and Afghan coal parameters using statistical modeling methods. Fuel Processing Technology. 2013; 110:79-85. https://doi.org/10.1016/j. fuproc.2012.11.005
  8. Tan P, Zhang C, Xia J, Fang Q-Y, Chen G. Estimation of higher heating value of coal based on proximate analysis using support vector regression. Fuel Processing Technology. 2015; 138:298-304. https://doi.org/10.1016/j. fuproc.2015.06.013
  9. Wen X, Jian S, Wang J. Prediction models of calorific value of coal based on wavelet neural networks. Fuel. 2017; 199:512-22. https://doi.org/10.1016/j.fuel.2017.03.012
  10. Kumari P, Singh AK, Wood DA, Hazra B. Predictions of gross calorific value of indian coals from their moisture and ash content. Journal Geological Society of India. 2019; 93:437-442. https://doi.org/10.1007/s12594-019-1198-5