Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials
This study correlated biomass heat capacity (Cp) with the chemistry (sulfur and ash content), crystallinity index, and temperature of various samples. A five-parameter linear correlation predicted 576 biomass Cp samples from four different origins with the absolute average relative deviation (AARD%)...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/27/19/6540 |
| _version_ | 1797477863649181696 |
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| author | Reza Iranmanesh Afham Pourahmad Fardad Faress Sevil Tutunchian Mohammad Amin Ariana Hamed Sadeqi Saleh Hosseini Falah Alobaid Babak Aghel |
| author_facet | Reza Iranmanesh Afham Pourahmad Fardad Faress Sevil Tutunchian Mohammad Amin Ariana Hamed Sadeqi Saleh Hosseini Falah Alobaid Babak Aghel |
| author_sort | Reza Iranmanesh |
| collection | DOAJ |
| description | This study correlated biomass heat capacity (Cp) with the chemistry (sulfur and ash content), crystallinity index, and temperature of various samples. A five-parameter linear correlation predicted 576 biomass Cp samples from four different origins with the absolute average relative deviation (AARD%) of ~1.1%. The proportional reduction in error (REE) approved that ash and sulfur contents only enlarge the correlation and have little effect on the accuracy. Furthermore, the REE showed that the temperature effect on biomass heat capacity was stronger than on the crystallinity index. Consequently, a new three-parameter correlation utilizing crystallinity index and temperature was developed. This model was more straightforward than the five-parameter correlation and provided better predictions (AARD = 0.98%). The proposed three-parameter correlation predicted the heat capacity of four different biomass classes with residual errors between −0.02 to 0.02 J/g∙K. The literature related biomass Cp to temperature using quadratic and linear correlations, and ignored the effect of the chemistry of the samples. These quadratic and linear correlations predicted the biomass Cp of the available database with an AARD of 39.19% and 1.29%, respectively. Our proposed model was the first work incorporating sample chemistry in biomass Cp estimation. |
| first_indexed | 2024-03-09T21:24:46Z |
| format | Article |
| id | doaj.art-846f3c12cd2b4a7d838990c683887514 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-09T21:24:46Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-846f3c12cd2b4a7d838990c6838875142023-11-23T21:13:06ZengMDPI AGMolecules1420-30492022-10-012719654010.3390/molecules27196540Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of BiomaterialsReza Iranmanesh0Afham Pourahmad1Fardad Faress2Sevil Tutunchian3Mohammad Amin Ariana4Hamed Sadeqi5Saleh Hosseini6Falah Alobaid7Babak Aghel8Faculty of Civil Engineering, K.N. Toosi University of Technology, Tehran 158754416, IranDepartment of Polymer Engineering, Amirkabir University of Technology, Tehran 1591634311, IranDepartment of Business, Data Analysis, The University of Texas Rio Grande Valley (UTRGV), Edinburg, TX 78539, USAEnergy Institute, Energy Science and Technology Department, Istanbul Technical University, Istanbul 34469, TurkeyDepartment of Petroleum Engineering, Gachsaran Branch, Islamic Azad University, Gachsaran 6387675818, IranDepartment of Internet and Wide Network, Iran Industrial Training Center Branch, University of Applied Science and Technology, Tehran 1599665111, IranDepartment of Chemical Engineering, University of Larestan, Larestan 7431813115, IranInstitut Energiesysteme und Energietechnik, Technische Universität Darmstadt, Otto-Berndt-Straße 2, 64287 Darmstadt, GermanyInstitut Energiesysteme und Energietechnik, Technische Universität Darmstadt, Otto-Berndt-Straße 2, 64287 Darmstadt, GermanyThis study correlated biomass heat capacity (Cp) with the chemistry (sulfur and ash content), crystallinity index, and temperature of various samples. A five-parameter linear correlation predicted 576 biomass Cp samples from four different origins with the absolute average relative deviation (AARD%) of ~1.1%. The proportional reduction in error (REE) approved that ash and sulfur contents only enlarge the correlation and have little effect on the accuracy. Furthermore, the REE showed that the temperature effect on biomass heat capacity was stronger than on the crystallinity index. Consequently, a new three-parameter correlation utilizing crystallinity index and temperature was developed. This model was more straightforward than the five-parameter correlation and provided better predictions (AARD = 0.98%). The proposed three-parameter correlation predicted the heat capacity of four different biomass classes with residual errors between −0.02 to 0.02 J/g∙K. The literature related biomass Cp to temperature using quadratic and linear correlations, and ignored the effect of the chemistry of the samples. These quadratic and linear correlations predicted the biomass Cp of the available database with an AARD of 39.19% and 1.29%, respectively. Our proposed model was the first work incorporating sample chemistry in biomass Cp estimation.https://www.mdpi.com/1420-3049/27/19/6540biomass sampleheat capacityempirical correlationbiomass crystallinityfeature reduction |
| spellingShingle | Reza Iranmanesh Afham Pourahmad Fardad Faress Sevil Tutunchian Mohammad Amin Ariana Hamed Sadeqi Saleh Hosseini Falah Alobaid Babak Aghel Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials Molecules biomass sample heat capacity empirical correlation biomass crystallinity feature reduction |
| title | Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials |
| title_full | Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials |
| title_fullStr | Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials |
| title_full_unstemmed | Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials |
| title_short | Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials |
| title_sort | introducing a linear empirical correlation for predicting the mass heat capacity of biomaterials |
| topic | biomass sample heat capacity empirical correlation biomass crystallinity feature reduction |
| url | https://www.mdpi.com/1420-3049/27/19/6540 |
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