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All Journal Multidiciplinary Output Research for Actual and International Issue (Morfai Journal)
B. Cahyaningsih
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LINEAR REGRESSION MODELING OF GROSS CALORIFIC VALUE BASED ON TOTAL MOISTURE AND ASH CONTENT IN COAL BLENDS Taufik Arief; A. M. Jannah; B. Cahyaningsih; Aisyah Minzikrina M.Rus
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 6 No. 2 (2026): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

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Abstract

Coal blending in stockpile management is widely implemented to meet market quality specifications; however, variations in coal quality parameters may significantly influence the gross calorific value (GCV). This study develops a linear regression-based predictive model to quantify the decline in GCV as a function of total moisture (TM) and ash content (AC) in multi-brand coal blending. A dataset derived from three mine-brand coals (MT-49, BB-51, and BTB-47) was used as a case study. GCV (ar) was treated as the dependent variable, while TM (ar) and AC (ar) were considered independent variables. The regression results reveal a strong negative linear relationship between TM and GCV, with a coefficient of determination (R²) of 0.6303, indicating that 63.03% of GCV variability is explained by changes in total moisture. In contrast, ash content shows a weaker relationship with GCV, with an R² of 0.1974 (19.74%). Quantitatively, an increase in TM by 1%, 2%, and 3% reduces GCV by 104.82, 209.64, and 314.46 kcal/kg (ar), respectively. Similarly, an increase in AC by 1%, 2%, and 3% decreases GCV by 94.60, 189.20, and 283.80 kcal/kg (ar), respectively. Blending simulations representing worst-case, moderate-case, and best-case scenarios were performed using the developed regression model to evaluate their impact on final calorific value (GCV). The moderate-case and best-case scenarios achieved the target specifications, confirming that moisture control plays a critical role in maintaining calorific value during coal stock blending. These findings demonstrate that a simple regression-based approach can serve as an effective decision-support tool for coal quality control and operational blending optimization.
Co-Authors A. M. Jannah Aisyah Minzikrina M.Rus Taufik Arief