Study on waste tire pyrolysis product characteristics based on machine learning
文献类型：期刊论文
| 作者 | Qi, Jingwei5,6; Zhang, Kaihong7; Hu, Ming1; Xu, Pengcheng6; Huhe, Taoli3,5; Ling, Xiang5; Yuan, Haoran2; Wang, Yijie4; Chen, Yong2,5 |
| 刊名 | JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING
 |
| 出版日期 | 2023-12-01 |
| 卷号 | 11期号:6页码:14 |
| 关键词 | Waste tire Pyrolysis Machine Learning Yield prediction |
| ISSN号 | 2213-2929 |
| DOI | 10.1016/j.jece.2023.111314 |
| 通讯作者 | Yuan, Haoran(yuanhr@ms.giec.ac.cn) |
| 英文摘要 | Tire pyrolysis is a highly complex thermochemical conversion process that transforms waste tires into high-value products such as pyrolysis oil, pyrolysis gas, and pyrolysis char. This process significantly mitigates the environmental issues caused by waste tires and reduces reliance on fossil resources. The physicochemical properties of tires and pyrolysis operation parameters have a significant impact on the yield of the three-phase products, thus affecting the industrial viability of tire pyrolysis to a large extent. Traditional prediction methods such as computational fluid dynamics and process simulation often fail to provide satisfactory results. However, data driven machine learning (ML) models have demonstrated their ability to handle complex nonlinear problems and offer more reliable predictions of pyrolysis products yield. This study employed a collected database of tire pyrolysis to develop tire pyrolysis product prediction models based on five ML models. These models were further optimized using Particle Swarm Optimization (PSO), and their prediction performances were quantitatively evaluated to identify the optimal model. Shapley analysis and one-way partial dependence analysis were conducted to explore the impact of input features on the output responses. Furthermore, an industrial-grade software was developed for accurate prediction of tire pyrolysis three-phase products yield. The results revealed that Gaussian process regression (GPR) and random forest regression (RFR), both optimized with PSO, demonstrated impressive prediction performance. Among them, the GPR model achieved the highest prediction accuracy with coefficient of determination (R2) values of 0.964, 0.924, and 0.86 for oil, char, and gas yields respectively, during the testing stage. |
| WOS关键词 | BIOMASS PYROLYSIS ; FLASH PYROLYSIS ; FUEL ; OPTIMIZATION ; GAS |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001101269800001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.giec.ac.cn/handle/344007/40260]  |
| 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.Everbright Greentech Technol Serv Jiangsu Ltd, Nanjing 210000, Peoples R China 2.Chinese Acad Sci, Guangzhou Inst Energy Convers, Guangzhou 510640, Peoples R China 3.Changzhou Univ, Changzhou 213164, Peoples R China 4.China Univ Petr, Beijing 102249, Peoples R China 5.Nanjing Tech Univ, Sch Mech & Power Engn, Nanjing 211816, Peoples R China 6.Everbright Environm Res Inst Nanjing Co Ltd, Nanjing 210000, Peoples R China 7.Hefei Univ Technol, Sch Automot & Transportat Engn, Hefei 230009, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qi, Jingwei,Zhang, Kaihong,Hu, Ming,et al. Study on waste tire pyrolysis product characteristics based on machine learning[J]. JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING,2023,11(6):14. |
| APA | Qi, Jingwei.,Zhang, Kaihong.,Hu, Ming.,Xu, Pengcheng.,Huhe, Taoli.,...&Chen, Yong.(2023).Study on waste tire pyrolysis product characteristics based on machine learning.JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING,11(6),14. |
| MLA | Qi, Jingwei,et al."Study on waste tire pyrolysis product characteristics based on machine learning".JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 11.6(2023):14. |
入库方式： OAI收割
来源：广州能源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。