Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches
<p dir="ltr">Heat capacity is among the most well‐known thermal properties of cellulosic biomass samples. This study assembles a general machine learning model to estimate the heat capacity of the cellulosic biomass samples with different origins. Combining the uncertainty and rankin...
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2022
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| _version_ | 1864513548297699328 |
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| author | Mohsen Karimi (227621) |
| author2 | Marzieh Khosravi (21348587) Reza Fathollahi (21348590) Amith Khandakar (14151981) Behzad Vaferi (4724262) |
| author2_role | author author author author |
| author_facet | Mohsen Karimi (227621) Marzieh Khosravi (21348587) Reza Fathollahi (21348590) Amith Khandakar (14151981) Behzad Vaferi (4724262) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohsen Karimi (227621) Marzieh Khosravi (21348587) Reza Fathollahi (21348590) Amith Khandakar (14151981) Behzad Vaferi (4724262) |
| dc.date.none.fl_str_mv | 2022-04-13T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1002/ese3.1155 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Determination_of_the_heat_capacity_of_cellulosic_biosamples_employing_diverse_machine_learning_approaches/29046287 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Chemical engineering Materials engineering Information and computing sciences Machine learning cellulosic sample computational modeling heat capacity least‐squares support vector regression |
| dc.title.none.fl_str_mv | Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Heat capacity is among the most well‐known thermal properties of cellulosic biomass samples. This study assembles a general machine learning model to estimate the heat capacity of the cellulosic biomass samples with different origins. Combining the uncertainty and ranking analyses over 819 artificial intelligence models from seven different categories confirmed that the least‐squares support vector regression (LSSVR) with the Gaussian kernel function is the best estimator. This model is validated using 700 laboratory heat capacities of four cellulosic biomass samples in wide temperature ranges (absolute average relative deviation = 0.32%, mean square errors = 1.88 × 10−3, and R2 = 0.999991). The data validity investigation approved that only one out of 700 experimental data is an outlier. The LSSVR model considers the effect of the cellulosic samples' crystallinity, temperature, and sulfur and ash content on their heat capacity. The overall prediction accuracy of the LSSVR is more than 62% better than the achieved accuracy using the empirical correlation.</p><h2>Other Information</h2><p dir="ltr">Published in: Energy Science & Engineering<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1002/ese3.1155" target="_blank">https://dx.doi.org/10.1002/ese3.1155</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_309efd13680081995824b07c0d48c00b |
| identifier_str_mv | 10.1002/ese3.1155 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29046287 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approachesMohsen Karimi (227621)Marzieh Khosravi (21348587)Reza Fathollahi (21348590)Amith Khandakar (14151981)Behzad Vaferi (4724262)EngineeringChemical engineeringMaterials engineeringInformation and computing sciencesMachine learningcellulosic samplecomputational modelingheat capacityleast‐squares support vectorregression<p dir="ltr">Heat capacity is among the most well‐known thermal properties of cellulosic biomass samples. This study assembles a general machine learning model to estimate the heat capacity of the cellulosic biomass samples with different origins. Combining the uncertainty and ranking analyses over 819 artificial intelligence models from seven different categories confirmed that the least‐squares support vector regression (LSSVR) with the Gaussian kernel function is the best estimator. This model is validated using 700 laboratory heat capacities of four cellulosic biomass samples in wide temperature ranges (absolute average relative deviation = 0.32%, mean square errors = 1.88 × 10−3, and R2 = 0.999991). The data validity investigation approved that only one out of 700 experimental data is an outlier. The LSSVR model considers the effect of the cellulosic samples' crystallinity, temperature, and sulfur and ash content on their heat capacity. The overall prediction accuracy of the LSSVR is more than 62% better than the achieved accuracy using the empirical correlation.</p><h2>Other Information</h2><p dir="ltr">Published in: Energy Science & Engineering<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1002/ese3.1155" target="_blank">https://dx.doi.org/10.1002/ese3.1155</a></p>2022-04-13T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1002/ese3.1155https://figshare.com/articles/journal_contribution/Determination_of_the_heat_capacity_of_cellulosic_biosamples_employing_diverse_machine_learning_approaches/29046287CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290462872022-04-13T09:00:00Z |
| spellingShingle | Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches Mohsen Karimi (227621) Engineering Chemical engineering Materials engineering Information and computing sciences Machine learning cellulosic sample computational modeling heat capacity least‐squares support vector regression |
| status_str | publishedVersion |
| title | Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches |
| title_full | Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches |
| title_fullStr | Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches |
| title_full_unstemmed | Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches |
| title_short | Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches |
| title_sort | Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches |
| topic | Engineering Chemical engineering Materials engineering Information and computing sciences Machine learning cellulosic sample computational modeling heat capacity least‐squares support vector regression |