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All Journal Multidiciplinary Output Research for Actual and International Issue (Morfai Journal)
Taufik Arief
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EXPERIMENTAL-BASED OPTIMIZATION MODELING OF COAL BLENDING USING PROXIMATE ANALYSIS TO MEET MT-47 POWER PLANT FUEL STANDARDS Taufik Arief; Eddy Ibrahim; IE. Frasetyo; Maulana Yusuf
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 6 No. 3 (2026): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

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Abstract

This study aims to evaluate the feasibility of using blended coal as fuel for steam power plants (PLTU) by examining the quality of MT-47 coal from PT Bukit Asam through proximate analysis and statistical trend analysis. High-calorie coal (A) and low-calorie coal (B) were blended at ratios of 30:70, 40:60, 50:50, and 60:40, and then tested in the laboratory to determine the intrinsic moisture (IM), ash content, volatile matter (VM), and fixed carbon (FC) content. The test results showed that the IM value ranged from 24.78–27.93%, ash from 4.52–5.79%, VM from 36.05–39.21%, and FC from 30.22–31.50% (adb). Regarding the quality of MT-47, only the ash parameters at the 30:70 and 40:60 ratios meet the standard limits, while the IM, VM, and FC at all ratios do not comply with the MT-47 specifications. Linear regression analysis and Pearson correlation coefficients show a very strong linear relationship between the blending ratio and all proximate parameters, with high correlation values (|r| > 0.95) and coefficients of determination above 95%, thus indicating a consistent trend of quality changes due to variations in the blending ratio. Overall, the 30:70 ratio is the composition closest to the quality of MT-47, although this coal mixture still requires further quality improvement processes to be suitable for direct use as PLTU fuel.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.20288117

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 B. Cahyaningsih Eddy Ibrahim IE. Frasetyo Maulana Yusuf