Large-Scale Digital Geologic Map Databases and Reports of the North Coal District in Afghanistan

Hare, T. M.; Davis, Philip A.; Nigh, Devon; Skinner, J. A.; SanFilipo, John R.; Bolm, Karen S.; Fortezzo, C. M.; Galuszka, D.; Stettner, William R.; Sultani, Shafiqullah; Nader, Billal

doi:10.3133/ds317

Cited by 3 publications

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“…Afghanistan mainly acquires its required electricity from neighbors. For more than a century, one of the most significant impediments in Afghanistan has been the lack of adequate fuel for power generation (Hare et al 2008;SanFilipo 2005;Wnuk 2016) and around 70% of Afghans do not have a reliable access to electricity (Doebrich et al 2006;Hackley et al 2010;Hare et al 2008;Tewalt et al 2010;Wnuk 2016). However, some studies have been conducted to find the potential coal deposits, and it was reported that Afghanistan has potentially abundant coal resources (Jacob 1961;Siebdrat and Weippert 1963;Wood et al 1983).…”

Section: Introductionmentioning

confidence: 99%

“…This is obvious due to the fact that many coal deposits are not accessible as a result of a specific situation in the country. Thus, techniques that are not typically used for coal production assessments (indirect methods), can be considered as an initial step for the development (Doebrich et al 2006;Hare et al 2008Hare et al , 2008Tewalt et al 2010;Wnuk 2016).…”

Section: Introductionmentioning

confidence: 99%

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Prediction of specific gravity of Afghan coal based on conventional coal properties by stepwise regression and random forest

Chelgani

2019

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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Coal can be considered as the main fuel for electricity generation in Afghanistan. However, there is a quite limited data available about the overall quality, distribution, and character of coals in Afghanistan. Specific gravity (S.G) of coal as a key factor can be used for the estimation of potential tonnage production and be a fundamental parameter for the selection of coal washery process method. However, there is no investigation which comprehensively explores relationships between S.G and coal properties. In this investigation, the potential of S.G prediction based on conventional properties for Afghan coal samples was explored by stepwise regression and random forest. Pearson correlation (r) and variable importance measurement (VIM) of random forest (RF) were applied to select the most effective variables among conventional parameters for the S.G prediction. Results of VIM indicated that ash and carbon content of coal samples had the highest importance for the S.G prediction.Stepwise regression and RF models were developed based on these two coal variables. Testing the generated models indicated that S.G of Afghan coals can quite accurately predict by these models (R 2 > 0.90). Modeling outcomes showed that the highest S.G (S.G > 2) for Afghan coal occurred when ash was higher than 40% and carbon was lower than 30%.

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