<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>B. Hart</dc:contributor>
  <dc:contributor>W.C. Grady</dc:contributor>
  <dc:contributor>J.C. Hower</dc:contributor>
  <dc:creator>S.C. Chelgani</dc:creator>
  <dc:date>2011</dc:date>
  <dc:description>&lt;p&gt;&lt;span&gt;The relationship between maceral content plus mineral matter and gross calorific value (GCV) for a wide range of West Virginia coal samples (from 6518 to 15330 BTU/lb; 15.16 to 35.66&amp;nbsp;MJ/kg) has been investigated by multivariable regression and adaptive neuro-fuzzy inference system (ANFIS). The stepwise least square mathematical method comparison between liptinite, vitrinite, plus mineral matter as input data sets with measured GCV reported a nonlinear correlation coefficient (&lt;/span&gt;&lt;i&gt;R&lt;/i&gt;&lt;span&gt;&amp;nbsp;&lt;/span&gt;&lt;sup&gt;2&lt;/sup&gt;&lt;span&gt;) of 0.83. Using the same data set the correlation between the predicted GCV from the ANFIS model and the actual GCV reported a&amp;nbsp;&lt;/span&gt;&lt;i&gt;R&lt;/i&gt;&lt;span&gt;&amp;nbsp;&lt;/span&gt;&lt;sup&gt;2&lt;/sup&gt;&lt;span&gt;&amp;nbsp;value of 0.96. It was determined that the GCV-based prediction methods, as used in this article, can provide a reasonable estimation of GCV.&lt;/span&gt;&lt;/p&gt;</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>10.1080/19392699.2010.527876</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Taylor &amp; Francis Online</dc:publisher>
  <dc:title>Study relationship between inorganic and organic coal analysis with gross calorific value by multiple regression and ANFIS</dc:title>
  <dc:type>article</dc:type>
</oai_dc:dc>