Optimizing biochar yield and composition prediction with ensemble machine learning models for sustainable production
Biochar production from organic waste can reduce fossil fuel reliance and combat climate change, but current models are computationally demanding and have limited accuracy. The study creates four machine learning models using multiple linear regression, decision trees, Adaboost regressors, and baggi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Ain Shams Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447924005902 |
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| author | Jingguo Gou Ghayas Haider Sajid Mohanad Muayad Sabri Mohammed El-Meligy Khalil El Hindi Nashwan Adnan OTHMAN |
| author_facet | Jingguo Gou Ghayas Haider Sajid Mohanad Muayad Sabri Mohammed El-Meligy Khalil El Hindi Nashwan Adnan OTHMAN |
| author_sort | Jingguo Gou |
| collection | DOAJ |
| description | Biochar production from organic waste can reduce fossil fuel reliance and combat climate change, but current models are computationally demanding and have limited accuracy. The study creates four machine learning models using multiple linear regression, decision trees, Adaboost regressors, and bagging regressors, trained on a dataset of pyrolysis tests. The results show that the data-driven models have significantly higher predictive accuracy than existing models, with an R2 of up to 0.96. The Bagging Regressor (BR) demonstrated superior efficacy compared over the MLR, AR, and DT models across all eight output parameters, with R2 values of 0.94, 0.93, 0.93, 0.94, 0.95, 0.90, 0.92, and 0.96 for Biochar Yield, Fixed Carbon, Volatile Matter, Ash, and ultimate composition parameters (C, H, O, and N), respectively. The study developed a data-driven model to predict Biochar yield and compositions, enhancing production processes and promoting sustainable farming practices. |
| format | Article |
| id | doaj-art-303a9c32dbfa41bab221c4f04af71c5a |
| institution | Kabale University |
| issn | 2090-4479 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj-art-303a9c32dbfa41bab221c4f04af71c5a2025-01-17T04:49:25ZengElsevierAin Shams Engineering Journal2090-44792025-01-01161103209Optimizing biochar yield and composition prediction with ensemble machine learning models for sustainable productionJingguo Gou0Ghayas Haider Sajid1Mohanad Muayad Sabri2Mohammed El-Meligy3Khalil El Hindi4Nashwan Adnan OTHMAN5School of Architecture and Surveying Engineering , Shanxi Datong University, Datong 037003,China; Corresponding authors.Department of Environmental Sciences, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan; Corresponding authors.Peter the Great Saint Petersburg Polytechnic University, Saint Petersburg, RussiaJadara University Research Center, Jadara University, PO Box 733 Irbid, Jordan; Applied Science Research Center, Applied Science Private University, Amman, JordanDepartment of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaDepartment of Computer Engineering, College of Engineering, Knowledge University, Erbil 44001, Iraq; Department of Computer Engineering, Al-Kitab University, Altun Kupri, IraqBiochar production from organic waste can reduce fossil fuel reliance and combat climate change, but current models are computationally demanding and have limited accuracy. The study creates four machine learning models using multiple linear regression, decision trees, Adaboost regressors, and bagging regressors, trained on a dataset of pyrolysis tests. The results show that the data-driven models have significantly higher predictive accuracy than existing models, with an R2 of up to 0.96. The Bagging Regressor (BR) demonstrated superior efficacy compared over the MLR, AR, and DT models across all eight output parameters, with R2 values of 0.94, 0.93, 0.93, 0.94, 0.95, 0.90, 0.92, and 0.96 for Biochar Yield, Fixed Carbon, Volatile Matter, Ash, and ultimate composition parameters (C, H, O, and N), respectively. The study developed a data-driven model to predict Biochar yield and compositions, enhancing production processes and promoting sustainable farming practices.http://www.sciencedirect.com/science/article/pii/S2090447924005902BiocharYieldCompositionPyrolysisData-driven modeling |
| spellingShingle | Jingguo Gou Ghayas Haider Sajid Mohanad Muayad Sabri Mohammed El-Meligy Khalil El Hindi Nashwan Adnan OTHMAN Optimizing biochar yield and composition prediction with ensemble machine learning models for sustainable production Ain Shams Engineering Journal Biochar Yield Composition Pyrolysis Data-driven modeling |
| title | Optimizing biochar yield and composition prediction with ensemble machine learning models for sustainable production |
| title_full | Optimizing biochar yield and composition prediction with ensemble machine learning models for sustainable production |
| title_fullStr | Optimizing biochar yield and composition prediction with ensemble machine learning models for sustainable production |
| title_full_unstemmed | Optimizing biochar yield and composition prediction with ensemble machine learning models for sustainable production |
| title_short | Optimizing biochar yield and composition prediction with ensemble machine learning models for sustainable production |
| title_sort | optimizing biochar yield and composition prediction with ensemble machine learning models for sustainable production |
| topic | Biochar Yield Composition Pyrolysis Data-driven modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2090447924005902 |
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