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Machine learning-driven prediction and optimization of pyrolysis oil and limonene production from waste tires

文献类型：期刊论文

作者Qi, Jingwei2,3; Xu, Pengcheng3; Hu, Ming4; Huhe, Taoli2,6; Ling, Xiang2; Yuan, Haoran5; Wang, Yijie1; Chen, Yong2,5
刊名JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS
出版日期2024
卷号177页码:13
关键词Waste tires Pyrolysis Machine learning Oil yield Limonene yield
ISSN号0165-2370
DOI10.1016/j.jaap.2023.106296
通讯作者Yuan, Haoran(yuanhr@ms.giec.ac.cn)
英文摘要The pyrolysis oil from waste tires possesses significant economic value and is a crucial factor in determining the industrial viability of tire pyrolysis processes. Limonene in pyrolysis oil is a significant component with extremely high industrial application value. In the process of tire pyrolysis, predicting the pyrolysis products through operating conditions and feedstock composition can effectively control industrial operations and enhance operational efficiency. However, there is currently a lack of robust prediction methods for pyrolysis oil and limonene yield. This study proposes the application of machine learning to predict the yield of tire pyrolysis oil and limonene. Artificial Neural Network (ANN) and Random Forest (RF) models were developed to create prediction models. In the statistical analysis, RF achieved optimal R2 median values of 0.83 and 0.64 for pyrolysis oil and limonene predictions during the testing stage.For the prediction of limonene yield, the best R2 and RMSE values in the testing and training stages were 0.844, 3.76, and 0.964, 1.91, respectively. For the prediction of pyrolysis oil yield, the corresponding values were 0.926, 4.1, and 0.985, 1.889, in the testing and training stages, respectively.Temperature was identified as the most critical operating condition affecting pyrolysis oil and limonene yields, with relative importance percentages of 20.6% and 12.2% for oil and limonene yields, respectively. The optimal operating parameter range conducive to limonene yield is as follows: temperature between 350 and 450 degrees C, residence time between 0 and 50 min, and tire particle size greater than 15 mm.This study lays the foundation for the production of oil and the extraction of limonene from tire pyrolysis.
WOS关键词TYRE PYROLYSIS ; LIQUID
WOS研究方向Chemistry ; Energy & Fuels ; Engineering
语种英语
WOS记录号WOS:001132969100001
出版者ELSEVIER
源URL[http://ir.giec.ac.cn/handle/344007/40490]  
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.China Univ Petr, Beijing 102249, Peoples R China
2.Nanjing Tech Univ, Sch Mech & Power Engn, Nanjing 211816, Peoples R China
3.Everbright Environm Res Inst Nanjing Co Ltd, Nanjing 210000, Peoples R China
4.Everbright Greentech Technol Serv Jiangsu Ltd, Nanjing 210000, Peoples R China
5.Chinese Acad Sci, Guangzhou Inst Energy Convers, Guangzhou 510640, Peoples R China
6.Changzhou Univ, Chanqzhou 213164, Peoples R China
推荐引用方式
GB/T 7714		 Qi, Jingwei,Xu, Pengcheng,Hu, Ming,et al. Machine learning-driven prediction and optimization of pyrolysis oil and limonene production from waste tires[J]. JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS,2024,177:13.
APA Qi, Jingwei.,Xu, Pengcheng.,Hu, Ming.,Huhe, Taoli.,Ling, Xiang.,...&Chen, Yong.(2024).Machine learning-driven prediction and optimization of pyrolysis oil and limonene production from waste tires.JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS,177,13.
MLA Qi, Jingwei,et al."Machine learning-driven prediction and optimization of pyrolysis oil and limonene production from waste tires".JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS 177(2024):13.

入库方式： OAI收割

来源：广州能源研究所

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