Explaining the relationship between common coal analyses and Afghan coal parameters using statistical modeling methods

被引：39
作者：
Chelgani, S. Chehreh [1 ]
Makaremi, S. [2 ]
机构：
[1] Univ Western Ontario, London, ON N6G 0J3, Canada
[2] Univ Western Ontario, Biomed Engn Grad Program, London, ON N6A 5B9, Canada
来源：
FUEL PROCESSING TECHNOLOGY | 2013年 / 110卷
关键词：
Afghanistan; Hardgrove grindingability index; Gross calorific value; Ash fusion temperature; Regression; ANFIS; ASH FUSION TEMPERATURES; ARTIFICIAL NEURAL-NETWORK; GROSS CALORIFIC VALUE; PROXIMATE ANALYSIS; KENTUCKY COALS; PREDICTION; GRINDABILITY; PETROGRAPHY; REGRESSION; ALGORITHM;
D O I：
10.1016/j.fuproc.2012.11.005
中图分类号：
O69 [应用化学];
学科分类号：
081704 ;
摘要：
This study investigates the effects of proximate, ultimate and elemental analysis for Afghan coal samples on Hardgrove grindability index (HGI), Gross calorific value (GCV), and Ash fusion temperatures (AFTs) by using multivariable regression (MR) and Adaptive neuro-fuzzy inference system (ANFIS) to increase information about the properties of the Afghan coal. Statistical modeling (MR, and ANFIS) indicated that coal parameters (HGI, GCV, AFTs) can be predicted with high accuracy, where GCV, AFTs, and HGI were estimated by R-2 = 0.99, 0.95, and 0.94, respectively. The small difference between the estimated parameters and their actual values shows that these accurate results can be also applied to estimate coal properties in other coal resources of Afghanistan. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码：79 / 85
页数：7
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