Study on biomass and polymer catalytic co-pyrolysis product characteristics using machine learning and shapley additive explanations (SHAP)
文献类型：期刊论文
| 作者 | Qi, Jingwei1,2; Wang, Yijie3; Xu, Pengcheng2; Huhe, Taoli1,4; Ling, Xiang1; Yuan, Haoran5; Chen, Yong1,5; Li, Jiadong6 |
| 刊名 | FUEL
 |
| 出版日期 | 2025-01-15 |
| 卷号 | 380页码:13 |
| 关键词 | Machine learning Catalytic co-pyrolysis Biomass and polymer Shapley additive explanations |
| ISSN号 | 0016-2361 |
| DOI | 10.1016/j.fuel.2024.133165 |
| 通讯作者 | Qi, Jingwei(qijw@ebchinaintl.com.cn) ; Li, Jiadong(jd.li@upc.edu.cn) |
| 英文摘要 | Biomass and polymer catalytic co-pyrolysis can convert waste into higher-quality fuels, thereby reducing the use of fossil fuels to some extent. However, this process is an extremely complex thermochemical conversion, influenced by numerous factors such as feedstock properties, operational variables, and catalyst. Currently, experimental methods require substantial time and resource investment. Machine learning (ML) can fit and match input and output features based on existing data, achieving extremely high accuracy in the co-pyrolysis process. This study applies advanced ML models to study the biomass and polymer catalytic co-pyrolysis process, with a focus on the yield of pyrolysis products and the variations of the oxygen-containing components in the pyrolysis oil. The best-performing model is used for feature analysis of the correlation between inputs and outputs, based on game theory SHAP analysis. The results indicate a significant negative correlation between the polymer addition ratio and the generation of oxygen-containing components during the co-pyrolysis process. The addition of catalysts promotes the generation of pyrolysis gas during co-pyrolysis but suppresses the yield of pyrolysis oil. Additionally, catalysts significantly inhibit the formation of oxygenates in the pyrolysis oil. The XGBR model shows the highest performance in predicting pyrolysis oil yield, achieving R-2 values of 0.98 during training phase and 0.91 during testing phase. The GBR model performs well in predicting the oxygenate composition of pyrolysis oil from small datasets. |
| WOS关键词 | POLYPROPYLENE ; PLASTICS ; WASTE |
| WOS研究方向 | Energy & Fuels ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001318908000001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.giec.ac.cn/handle/344007/42948]  |
| 专题 | 中国科学院广州能源研究所 |
| 通讯作者 | Qi, Jingwei; Li, Jiadong |
| 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.Changzhou Univ, Changzhou 213164, Peoples R China 5.Chinese Acad Sci, Guangzhou Inst Energy Convers, Guangzhou 510640, Peoples R China 6.China Univ Petr East China, Qingdao 266580, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qi, Jingwei,Wang, Yijie,Xu, Pengcheng,et al. Study on biomass and polymer catalytic co-pyrolysis product characteristics using machine learning and shapley additive explanations (SHAP)[J]. FUEL,2025,380:13. |
| APA | Qi, Jingwei.,Wang, Yijie.,Xu, Pengcheng.,Huhe, Taoli.,Ling, Xiang.,...&Li, Jiadong.(2025).Study on biomass and polymer catalytic co-pyrolysis product characteristics using machine learning and shapley additive explanations (SHAP).FUEL,380,13. |
| MLA | Qi, Jingwei,et al."Study on biomass and polymer catalytic co-pyrolysis product characteristics using machine learning and shapley additive explanations (SHAP)".FUEL 380(2025):13. |
入库方式： OAI收割
来源：广州能源研究所
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