Abstract: The experimental determination of proximate analysis data can be obtained easily using an ordinary muffle furnace whereas calorific value of solid fuels is a cost intensive process, as it requires spatial instrumentation and highly trained analyst to perform the experiments, compared to calorific value. Regression analysis and CIMFR formula methods have been introduced to simplify the task and also reduce the cost of analysis. An endeavor has been made in this present study to access the applicability of these correlations and regression method with a spatial emphasize on Indian coals. Correlation have been created using simple linear regression and multivariable linear Regression analysis based on proximate analysis of data sets.

15 samples were collected from different coal fields of India including the South Eastern Coalfields (SECL), Singareni Collieries Company Limited (SCCL), Central Coalfields limited (CCL), Mahanadi Coalfield Ltd. (MCL), Jindal Steel and Power Limited. The intrinsic properties were determined by carrying out proximate analysis and gross calorific value (GCV) by using bomb calorimeter. The results for intrinsic properties and the gross calorific value are given in table 1.

Correlation analysis was carried out to analyze the individual effect of moisture, volatile matter, ash and fixed carbon on the gross calorific value (GCV). It is observed that moisture, ash have adverse impact and reduce the gross calorific value whereas volatile matter, fixed carbon have positive impact and increase the gross calorific value (GCV).

Keywords: Coal Utilization; Energy Sources; Physical Properties; Ashes; Chemical Properties; Calorific Value; Combustion Temperature; Heat Measurement; Moisture Content; Volatility;

Downloads: | DOI: 10.17148/IARJSET.2025.12904