Study on the Co-gasification characteristics of biomass and municipal solid waste based on machine learning
文献类型：期刊论文
| 作者 | Qi, Jingwei1,2; Wang, Yijie3; Xu, Pengcheng2; Hu, Ming4; Huhe, Taoli1,5; Ling, Xiang1; Yuan, Haoran6; Chen, Yong1,6 |
| 刊名 | ENERGY
 |
| 出版日期 | 2024-03-01 |
| 卷号 | 290页码:12 |
| 关键词 | Machine learning Co-gasification Biomass and MSW SHAP analysis |
| ISSN号 | 0360-5442 |
| DOI | 10.1016/j.energy.2023.130178 |
| 通讯作者 | Yuan, Haoran(yuanhr@ms.giec.ac.cn) |
| 英文摘要 | Co-gasification of biomass and municipal solid waste (MSW) exhibits synergistic effects by improving the quality of syngas while reducing environmental pollution from MSW. In this study, Machine learning (ML) techniques were employed to investigate the co-gasification process of biomass and MSW. A comprehensive dataset was constructed using existing data, including different feedstock types and operating conditions, with 18 input features and 9 output features. Four advanced ML models were utilized to model and analyze the co-gasification process. By leveraging feedstock characteristics and operating parameters, key gasification parameters such as syngas composition, lower heating value (LHV) of syngas, tar yield, and carbon conversion efficiency were predicted. The results showed that all four models exhibited excellent predictive performance, with R2 values greater than 0.9 in both the training and testing stage. Specifically, Histogram-based gradient boosting regression (HGBR) exhibited the lowest root mean square error (RMSE) in predicting CO, while the gradient boosting regressor (GBR) achieved the best performance in H2 prediction with a RMSE of 1.6. The most influential input features for CO concentration were equivalence ratio (ER), oxygen content in biomass and hydrogen content in biomass. The key features affecting H2 concentration were steam/fuel and ER. |
| WOS研究方向 | Thermodynamics ; Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:001151018200001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://ir.giec.ac.cn/handle/344007/40759]  |
| 专题 | 中国科学院广州能源研究所 |
| 通讯作者 | Yuan, Haoran |
| 作者单位 | 1.Nanjing Tech Univ, Sch Mech & Power Engn, Nanjing 211816, Peoples R China 2.Everbright Environm Res Inst Nanjing Co Ltd, Nanjing 210000, Peoples R China 3.China Univ Petr, Beijing 102249, Peoples R China 4.Everbright Greentech Technol Serv Jiangsu Ltd, Nanjing 210000, Peoples R China 5.Changzhou Univ, Changzhou 213164, Peoples R China 6.Chinese Acad Sci, Guangzhou Inst Energy Convers, Guangzhou 510640, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qi, Jingwei,Wang, Yijie,Xu, Pengcheng,et al. Study on the Co-gasification characteristics of biomass and municipal solid waste based on machine learning[J]. ENERGY,2024,290:12. |
| APA | Qi, Jingwei.,Wang, Yijie.,Xu, Pengcheng.,Hu, Ming.,Huhe, Taoli.,...&Chen, Yong.(2024).Study on the Co-gasification characteristics of biomass and municipal solid waste based on machine learning.ENERGY,290,12. |
| MLA | Qi, Jingwei,et al."Study on the Co-gasification characteristics of biomass and municipal solid waste based on machine learning".ENERGY 290(2024):12. |
入库方式： OAI收割
来源：广州能源研究所
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