Prediction models of calorific value of coal based on wavelet neural networks
New prediction models based on wavelet neural networks (WNNs) have been proposed to estimate the gross calorific value (GCV) of coals. The input sets for the prediction models are involved of the proximate and ultimate analysis components of coal and the oxide analyses of ash. The coal samples, whic...
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| Veröffentlicht in: | Fuel (Guildford) 2017-07, Vol.199, p.512-522 |
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| creator | Wen, Xiaoqiang Jian, Shuguang Wang, Jianguo |
| description | New prediction models based on wavelet neural networks (WNNs) have been proposed to estimate the gross calorific value (GCV) of coals. The input sets for the prediction models are involved of the proximate and ultimate analysis components of coal and the oxide analyses of ash. The coal samples, which have been employed to develop and verify the prediction models, are from United States Geological Survey (USGS) and China Huaneng Group. Some published methods have also been employed and redeveloped to make a comparison with the models proposed in this paper. The comparison reveals that the WNN models proposed here based on the proximate (ultimate) analysis components of coal, are consistently better than the published ones. The WNN models based on the oxide analyses of ash have higher accuracy in estimating the GCV of Chinese coals than US coals. Here we also analyze the possible reasons that could lead to the low estimated accuracy. |
| doi_str_mv | 10.1016/j.fuel.2017.03.012 |
| format | Article |
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The input sets for the prediction models are involved of the proximate and ultimate analysis components of coal and the oxide analyses of ash. The coal samples, which have been employed to develop and verify the prediction models, are from United States Geological Survey (USGS) and China Huaneng Group. Some published methods have also been employed and redeveloped to make a comparison with the models proposed in this paper. The comparison reveals that the WNN models proposed here based on the proximate (ultimate) analysis components of coal, are consistently better than the published ones. The WNN models based on the oxide analyses of ash have higher accuracy in estimating the GCV of Chinese coals than US coals. Here we also analyze the possible reasons that could lead to the low estimated accuracy.</description><identifier>ISSN: 0016-2361</identifier><identifier>EISSN: 1873-7153</identifier><identifier>DOI: 10.1016/j.fuel.2017.03.012</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Ashes ; Calorific value ; Coal ; Geological surveys ; Gross calorific value ; Mathematical models ; Neural networks ; Oxide analyses ; Prediction models ; Proximate (ultimate) analysis ; Wavelet analysis ; Wavelet neural networks</subject><ispartof>Fuel (Guildford), 2017-07, Vol.199, p.512-522</ispartof><rights>2017 Elsevier Ltd</rights><rights>Copyright Elsevier BV Jul 1, 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c328t-2fd5875462485a0766ac244bc522d58c79d1cb0725b4e3fbec6e5432dbaff673</citedby><cites>FETCH-LOGICAL-c328t-2fd5875462485a0766ac244bc522d58c79d1cb0725b4e3fbec6e5432dbaff673</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0016236117302764$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,65309</link.rule.ids></links><search><creatorcontrib>Wen, Xiaoqiang</creatorcontrib><creatorcontrib>Jian, Shuguang</creatorcontrib><creatorcontrib>Wang, Jianguo</creatorcontrib><title>Prediction models of calorific value of coal based on wavelet neural networks</title><title>Fuel (Guildford)</title><description>New prediction models based on wavelet neural networks (WNNs) have been proposed to estimate the gross calorific value (GCV) of coals. 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Here we also analyze the possible reasons that could lead to the low estimated accuracy.</description><subject>Ashes</subject><subject>Calorific value</subject><subject>Coal</subject><subject>Geological surveys</subject><subject>Gross calorific value</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Oxide analyses</subject><subject>Prediction models</subject><subject>Proximate (ultimate) analysis</subject><subject>Wavelet analysis</subject><subject>Wavelet neural networks</subject><issn>0016-2361</issn><issn>1873-7153</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWD_-gKcFz7tOvjZb8CLFL6joofeQzU4gdbupyW6L_97UevY08M7zzsBDyA2FigKt79aVm7CvGFBVAa-AshMyo43ipaKSn5IZZKpkvKbn5CKlNQCoRooZefuI2Hk7-jAUm9Bhn4rgCmv6EL3zttiZfsLfKJi-aE3Crsjo3uywx7EYcIo5H3Dch_iZrsiZM33C6795SVZPj6vFS7l8f35dPCxLy1kzlsx1slFS1Ew00oCqa2OZEK2VjOWNVfOO2hYUk61A7lq0NUrBWdca52rFL8nt8ew2hq8J06jXYYpD_qgZAG9qAXOeKXakbAwpRXR6G_3GxG9NQR-s6bU-WNMHaxq4ztZy6f5YyiZw5zHqZD0ONkuKaEfdBf9f_QedfHXA</recordid><startdate>20170701</startdate><enddate>20170701</enddate><creator>Wen, Xiaoqiang</creator><creator>Jian, Shuguang</creator><creator>Wang, Jianguo</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope></search><sort><creationdate>20170701</creationdate><title>Prediction models of calorific value of coal based on wavelet neural networks</title><author>Wen, Xiaoqiang ; Jian, Shuguang ; Wang, Jianguo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c328t-2fd5875462485a0766ac244bc522d58c79d1cb0725b4e3fbec6e5432dbaff673</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Ashes</topic><topic>Calorific value</topic><topic>Coal</topic><topic>Geological surveys</topic><topic>Gross calorific value</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Oxide analyses</topic><topic>Prediction models</topic><topic>Proximate (ultimate) analysis</topic><topic>Wavelet analysis</topic><topic>Wavelet neural networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wen, Xiaoqiang</creatorcontrib><creatorcontrib>Jian, Shuguang</creatorcontrib><creatorcontrib>Wang, Jianguo</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Fuel (Guildford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wen, Xiaoqiang</au><au>Jian, Shuguang</au><au>Wang, Jianguo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction models of calorific value of coal based on wavelet neural networks</atitle><jtitle>Fuel (Guildford)</jtitle><date>2017-07-01</date><risdate>2017</risdate><volume>199</volume><spage>512</spage><epage>522</epage><pages>512-522</pages><issn>0016-2361</issn><eissn>1873-7153</eissn><abstract>New prediction models based on wavelet neural networks (WNNs) have been proposed to estimate the gross calorific value (GCV) of coals. The input sets for the prediction models are involved of the proximate and ultimate analysis components of coal and the oxide analyses of ash. The coal samples, which have been employed to develop and verify the prediction models, are from United States Geological Survey (USGS) and China Huaneng Group. Some published methods have also been employed and redeveloped to make a comparison with the models proposed in this paper. The comparison reveals that the WNN models proposed here based on the proximate (ultimate) analysis components of coal, are consistently better than the published ones. The WNN models based on the oxide analyses of ash have higher accuracy in estimating the GCV of Chinese coals than US coals. Here we also analyze the possible reasons that could lead to the low estimated accuracy.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.fuel.2017.03.012</doi><tpages>11</tpages></addata></record> |
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| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Ashes Calorific value Coal Geological surveys Gross calorific value Mathematical models Neural networks Oxide analyses Prediction models Proximate (ultimate) analysis Wavelet analysis Wavelet neural networks |
| title | Prediction models of calorific value of coal based on wavelet neural networks |
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