Improvement of HHV prediction model of biomass based on the ultimate analysis
This paper presents an improved prediction model for the higher heating value (HHV) of biomass based on the ultimate analysis by using the standard least squares method. This study intends for us to predict the HHV of biomass within a wide range of elemental distributions rather than the range of li...
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| Veröffentlicht in: | Journal of renewable and sustainable energy 2021-09, Vol.13 (5) |
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| container_title | Journal of renewable and sustainable energy |
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| creator | Kim, Se Ung Yun, Jong Sun Ri, Jong Sim Hong, Kwang Il Chon, Ok Sim Ri, Jin Hyok Ri, Jang Mi |
| description | This paper presents an improved prediction model for the higher heating value (HHV) of biomass based on the ultimate analysis by using the standard least squares method. This study intends for us to predict the HHV of biomass within a wide range of elemental distributions rather than the range of literature in order to create an optimal prediction model. To this end, many experimental data, comprising a wide range of biomass elements, regression models, and neural networks, are used for its comparative validation. As a result, the proposed prediction model, HHV = 2.8799 + 0.2965 * C + 0.4826 * H – 0.0187 * O demonstrates a better HHV prediction performance for biomass in a comparative validation of 250 samples presented in the literature, and the fitness model using a neural network shows a high fitness in the training, validation, and testing for 430 samples. |
| doi_str_mv | 10.1063/5.0059376 |
| format | Article |
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This study intends for us to predict the HHV of biomass within a wide range of elemental distributions rather than the range of literature in order to create an optimal prediction model. To this end, many experimental data, comprising a wide range of biomass elements, regression models, and neural networks, are used for its comparative validation. As a result, the proposed prediction model, HHV = 2.8799 + 0.2965 * C + 0.4826 * H – 0.0187 * O demonstrates a better HHV prediction performance for biomass in a comparative validation of 250 samples presented in the literature, and the fitness model using a neural network shows a high fitness in the training, validation, and testing for 430 samples.</description><identifier>ISSN: 1941-7012</identifier><identifier>EISSN: 1941-7012</identifier><identifier>DOI: 10.1063/5.0059376</identifier><identifier>CODEN: JRSEBH</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Biomass ; Calorific value ; Fitness ; Least squares method ; Neural networks ; Prediction models ; Regression models</subject><ispartof>Journal of renewable and sustainable energy, 2021-09, Vol.13 (5)</ispartof><rights>Author(s)</rights><rights>2021 Author(s). 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This study intends for us to predict the HHV of biomass within a wide range of elemental distributions rather than the range of literature in order to create an optimal prediction model. To this end, many experimental data, comprising a wide range of biomass elements, regression models, and neural networks, are used for its comparative validation. As a result, the proposed prediction model, HHV = 2.8799 + 0.2965 * C + 0.4826 * H – 0.0187 * O demonstrates a better HHV prediction performance for biomass in a comparative validation of 250 samples presented in the literature, and the fitness model using a neural network shows a high fitness in the training, validation, and testing for 430 samples.</description><subject>Biomass</subject><subject>Calorific value</subject><subject>Fitness</subject><subject>Least squares method</subject><subject>Neural networks</subject><subject>Prediction models</subject><subject>Regression models</subject><issn>1941-7012</issn><issn>1941-7012</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kEFLAzEQhYMoWKsH_0HAk8LWJLvZNEcpaguKF_UasskEt-w2a5IW-u9NaVFB8DTDzMebNw-hS0omlNTlLZ8QwmUp6iM0orKihSCUHf_qT9FZjEtCakY4G6HnRT8Ev4EeVgl7h-fzdzwEsK1JrV_h3lvodvOm9b2OETc6gsV5kz4Ar7vU9joB1ivdbWMbz9GJ012Ei0Mdo7eH-9fZvHh6eVzM7p4KUzKRCulKZ_P5ygCAZc1UG0kbo5sKKslEWTMpGa-ZE8JKoUtDgQjqCOONdsaycoyu9rrZ--caYlJLvw7ZRFSMT4kQdVbJ1PWeMsHHGMCpIWS_YasoUbu0FFeHtDJ7s2ejaZPe_f4Nb3z4AdVg3X_wX-Uv-eB4JQ</recordid><startdate>202109</startdate><enddate>202109</enddate><creator>Kim, Se Ung</creator><creator>Yun, Jong Sun</creator><creator>Ri, Jong Sim</creator><creator>Hong, Kwang Il</creator><creator>Chon, Ok Sim</creator><creator>Ri, Jin Hyok</creator><creator>Ri, Jang Mi</creator><general>American Institute of Physics</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-2212-9986</orcidid><orcidid>https://orcid.org/0000-0003-2767-5043</orcidid><orcidid>https://orcid.org/0000-0003-4352-0201</orcidid><orcidid>https://orcid.org/0000-0002-8023-0919</orcidid><orcidid>https://orcid.org/0000-0003-2768-488X</orcidid><orcidid>https://orcid.org/0000-0001-7243-5204</orcidid></search><sort><creationdate>202109</creationdate><title>Improvement of HHV prediction model of biomass based on the ultimate analysis</title><author>Kim, Se Ung ; Yun, Jong Sun ; Ri, Jong Sim ; Hong, Kwang Il ; Chon, Ok Sim ; Ri, Jin Hyok ; Ri, Jang Mi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c327t-9f3fd0524ceeed2b8ac91bcab4e4927362992562f77d97a3c1e071f025bafcd23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Biomass</topic><topic>Calorific value</topic><topic>Fitness</topic><topic>Least squares method</topic><topic>Neural networks</topic><topic>Prediction models</topic><topic>Regression models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Se Ung</creatorcontrib><creatorcontrib>Yun, Jong Sun</creatorcontrib><creatorcontrib>Ri, Jong Sim</creatorcontrib><creatorcontrib>Hong, Kwang Il</creatorcontrib><creatorcontrib>Chon, Ok Sim</creatorcontrib><creatorcontrib>Ri, Jin Hyok</creatorcontrib><creatorcontrib>Ri, Jang Mi</creatorcontrib><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Journal of renewable and sustainable energy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Se Ung</au><au>Yun, Jong Sun</au><au>Ri, Jong Sim</au><au>Hong, Kwang Il</au><au>Chon, Ok Sim</au><au>Ri, Jin Hyok</au><au>Ri, Jang Mi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improvement of HHV prediction model of biomass based on the ultimate analysis</atitle><jtitle>Journal of renewable and sustainable energy</jtitle><date>2021-09</date><risdate>2021</risdate><volume>13</volume><issue>5</issue><issn>1941-7012</issn><eissn>1941-7012</eissn><coden>JRSEBH</coden><abstract>This paper presents an improved prediction model for the higher heating value (HHV) of biomass based on the ultimate analysis by using the standard least squares method. 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As a result, the proposed prediction model, HHV = 2.8799 + 0.2965 * C + 0.4826 * H – 0.0187 * O demonstrates a better HHV prediction performance for biomass in a comparative validation of 250 samples presented in the literature, and the fitness model using a neural network shows a high fitness in the training, validation, and testing for 430 samples.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0059376</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-2212-9986</orcidid><orcidid>https://orcid.org/0000-0003-2767-5043</orcidid><orcidid>https://orcid.org/0000-0003-4352-0201</orcidid><orcidid>https://orcid.org/0000-0002-8023-0919</orcidid><orcidid>https://orcid.org/0000-0003-2768-488X</orcidid><orcidid>https://orcid.org/0000-0001-7243-5204</orcidid></addata></record> |
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| identifier | ISSN: 1941-7012 |
| ispartof | Journal of renewable and sustainable energy, 2021-09, Vol.13 (5) |
| issn | 1941-7012 1941-7012 |
| language | eng |
| recordid | cdi_scitation_primary_10_1063_5_0059376 |
| source | AIP Journals Complete |
| subjects | Biomass Calorific value Fitness Least squares method Neural networks Prediction models Regression models |
| title | Improvement of HHV prediction model of biomass based on the ultimate analysis |
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