Calorific Value Prediction Model Using Structure Composition of Heat-Treated Lignocellulosic Biomass
This study aims to identify an equation for predicting the calorific value for heat-treated biomass using structural analysis. Different models were constructed using 129 samples of cellulose, hemicellulose, and lignin, and calorific values obtained from previous studies. These models were validated...
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| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/23/7896 |
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| author | Sunyong Park Seon Yeop Kim Ha Eun Kim Kwang Cheol Oh Seok Jun Kim La Hoon Cho Young Kwang Jeon DaeHyun Kim |
| author_facet | Sunyong Park Seon Yeop Kim Ha Eun Kim Kwang Cheol Oh Seok Jun Kim La Hoon Cho Young Kwang Jeon DaeHyun Kim |
| author_sort | Sunyong Park |
| collection | DOAJ |
| description | This study aims to identify an equation for predicting the calorific value for heat-treated biomass using structural analysis. Different models were constructed using 129 samples of cellulose, hemicellulose, and lignin, and calorific values obtained from previous studies. These models were validated using 41 additional datasets, and an optimal model was identified using its results and following performance metrics: the coefficient of determination (R<sup>2</sup>), mean absolute error (MAE), root-mean-squared error (RMSE), average absolute error (AAE), and average bias error (ABE). Finally, the model was verified using 25 additional data points. For the overall dataset, R<sup>2</sup> was ~0.52, and the RMSE range was 1.46–1.77. For woody biomass, the R<sup>2</sup> range was 0.78–0.83, and the RMSE range was 0.9626–1.2810. For herbaceous biomass, the R<sup>2</sup> range was 0.5251–0.6001, and the RMSE range was 1.1822–1.3957. The validation results showed similar or slightly poorer performances. The optimal model was then tested using the test data. For overall biomass and woody biomass, the performance metrics of the obtained model were superior to those in previous studies, whereas for herbaceous biomass, lower performance metrics were observed. The identified model demonstrated equal or superior performance compared to linear models. Further improvements are required based on a wider range of structural biomass data. |
| first_indexed | 2024-03-09T01:52:00Z |
| format | Article |
| id | doaj.art-90313be4591f49f8965cfddab43994c7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T01:52:00Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-90313be4591f49f8965cfddab43994c72023-12-08T15:15:11ZengMDPI AGEnergies1996-10732023-12-011623789610.3390/en16237896Calorific Value Prediction Model Using Structure Composition of Heat-Treated Lignocellulosic BiomassSunyong Park0Seon Yeop Kim1Ha Eun Kim2Kwang Cheol Oh3Seok Jun Kim4La Hoon Cho5Young Kwang Jeon6DaeHyun Kim7Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of KoreaDepartment of Biosystems Engineering, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of KoreaDepartment of Biosystems Engineering, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of KoreaAgriculture and Life Science Research Institute, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of KoreaDepartment of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of KoreaDepartment of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of KoreaDepartment of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of KoreaDepartment of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of KoreaThis study aims to identify an equation for predicting the calorific value for heat-treated biomass using structural analysis. Different models were constructed using 129 samples of cellulose, hemicellulose, and lignin, and calorific values obtained from previous studies. These models were validated using 41 additional datasets, and an optimal model was identified using its results and following performance metrics: the coefficient of determination (R<sup>2</sup>), mean absolute error (MAE), root-mean-squared error (RMSE), average absolute error (AAE), and average bias error (ABE). Finally, the model was verified using 25 additional data points. For the overall dataset, R<sup>2</sup> was ~0.52, and the RMSE range was 1.46–1.77. For woody biomass, the R<sup>2</sup> range was 0.78–0.83, and the RMSE range was 0.9626–1.2810. For herbaceous biomass, the R<sup>2</sup> range was 0.5251–0.6001, and the RMSE range was 1.1822–1.3957. The validation results showed similar or slightly poorer performances. The optimal model was then tested using the test data. For overall biomass and woody biomass, the performance metrics of the obtained model were superior to those in previous studies, whereas for herbaceous biomass, lower performance metrics were observed. The identified model demonstrated equal or superior performance compared to linear models. Further improvements are required based on a wider range of structural biomass data.https://www.mdpi.com/1996-1073/16/23/7896woody biomassherbaceous biomassprediction modelcalorific value |
| spellingShingle | Sunyong Park Seon Yeop Kim Ha Eun Kim Kwang Cheol Oh Seok Jun Kim La Hoon Cho Young Kwang Jeon DaeHyun Kim Calorific Value Prediction Model Using Structure Composition of Heat-Treated Lignocellulosic Biomass Energies woody biomass herbaceous biomass prediction model calorific value |
| title | Calorific Value Prediction Model Using Structure Composition of Heat-Treated Lignocellulosic Biomass |
| title_full | Calorific Value Prediction Model Using Structure Composition of Heat-Treated Lignocellulosic Biomass |
| title_fullStr | Calorific Value Prediction Model Using Structure Composition of Heat-Treated Lignocellulosic Biomass |
| title_full_unstemmed | Calorific Value Prediction Model Using Structure Composition of Heat-Treated Lignocellulosic Biomass |
| title_short | Calorific Value Prediction Model Using Structure Composition of Heat-Treated Lignocellulosic Biomass |
| title_sort | calorific value prediction model using structure composition of heat treated lignocellulosic biomass |
| topic | woody biomass herbaceous biomass prediction model calorific value |
| url | https://www.mdpi.com/1996-1073/16/23/7896 |
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