TY - GEN
T1 - Coal it's elementary my dear Watson
AU - Mathews, Jonathan P.
AU - Krishnamoorthy, Vijayaragavan
AU - Louw, Enette
AU - Tchapda, Aime H.N.
AU - Castro-Marcano, Fidel
AU - Karri, Vamsi
AU - Alexis, Dennis A.
AU - Mitchell, Gareth D.
PY - 2011/12/1
Y1 - 2011/12/1
N2 - The spatial arrangement and abundance of the elements: C, H, N, O, S often correlate or directly influence a plethora of coal properties. For >90 years, attempts have utilized the ultimate (elemental) analysis of coal to predict a wide variety of properties such as: calorific value (higher heating value), volatile matter, vitrinite reflectance (mean maximum), Hardgrove grindability index, helium density, aromaticity, etc. While many relationships resulted in graphical plots that have utility even today, numerical values can also be directly calculated utilizing the correlations. These have the potential to allow rapid predictions and low-cost approaches to coal property determination. Here the many correlations addressing multiple coal properties were reviewed and where possible evaluated against the Pennsylvania State University Coal Sample Bank and Database for vitrinite-rich (>80% by point counting) United States Coals. Around 40 correlations were found. While some correlations, such as calorific value predictions are accurate over a wide range of compositions, others are challenged to capture the predictive value accurately, over a wide range, but may capture the trends.
AB - The spatial arrangement and abundance of the elements: C, H, N, O, S often correlate or directly influence a plethora of coal properties. For >90 years, attempts have utilized the ultimate (elemental) analysis of coal to predict a wide variety of properties such as: calorific value (higher heating value), volatile matter, vitrinite reflectance (mean maximum), Hardgrove grindability index, helium density, aromaticity, etc. While many relationships resulted in graphical plots that have utility even today, numerical values can also be directly calculated utilizing the correlations. These have the potential to allow rapid predictions and low-cost approaches to coal property determination. Here the many correlations addressing multiple coal properties were reviewed and where possible evaluated against the Pennsylvania State University Coal Sample Bank and Database for vitrinite-rich (>80% by point counting) United States Coals. Around 40 correlations were found. While some correlations, such as calorific value predictions are accurate over a wide range of compositions, others are challenged to capture the predictive value accurately, over a wide range, but may capture the trends.
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M3 - Conference contribution
AN - SCOPUS:84877626514
SN - 9781618393982
T3 - 28th Annual International Pittsburgh Coal Conference 2011, PCC 2011
SP - 1827
EP - 1841
BT - 28th Annual International Pittsburgh Coal Conference 2011, PCC 2011
T2 - 28th Annual International Pittsburgh Coal Conference 2011, PCC 2011
Y2 - 12 September 2011 through 15 September 2011
ER -