Study of model construction of fuel production from waste plastic pyrolysis based on machine learning
The conversion of waste plastics into oil (aviation fuel) and syngas (carbon monoxide and hydrogen) through pyrolysis offers an efficient means of recycling and reusing these plastics. Factors such as feedstock types and working conditions have an important impact on pyrolysis products, which makes...
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Editorial Office of Energy Environmental Protection
2024-10-01
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| Series: | 能源环境保护 |
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| Online Access: | https://eep1987.com/en/article/5137 |
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| author | CHEN Sihan YUAN Zhilong WANG Ye SUN Yifei* |
| author_facet | CHEN Sihan YUAN Zhilong WANG Ye SUN Yifei* |
| author_sort | CHEN Sihan |
| collection | DOAJ |
| description | The conversion of waste plastics into oil (aviation fuel) and syngas (carbon monoxide and hydrogen) through pyrolysis offers an efficient means of recycling and reusing these plastics. Factors such as feedstock types and working conditions have an important impact on pyrolysis products, which makes the reaction mechanism of pyrolysis process more complex, so it is necessary to explore the reaction nature through a large number of experimental data, and the experimental cost is high. Machine learning has the advantages of large data processing volume and easy extraction of statistical laws, which can reduce costs and research difficulties. A machine-learning approach was applied to utilize data from non-catalytic and molecular sieve catalytic processes and to build a model for analyzing raw material pyrolysis. The Gradient Boosting Regression (GBR) algorithm has the best fitting performance for predicting oil yield (R^2=0.91, RMSE=7.78), while the adaptive boosting algorithm (AdaBoost) has the best fitting performance for predicting gas yield (R^2=0.83, RMSE=6.42), enabling accurate prediction of reaction conditions. It was found that optimal oil yield occurred at a heating rate of approximately 20 ℃/min and a temperature of 500 ℃ through importance ranking and single dependency analyses. Additionally, a dual dependency analysis of oil yield with reaction temperature, heating rate, and reaction time was conducted. This study quantified the effects of heating rate, pyrolysis temperature and other reaction conditions on the oil and gas yield of plastic pyrolysis, which provides a theoretical basis for the production practice of waste plastic recycling. |
| format | Article |
| id | doaj-art-2796df8d08c749eb89ddd40b70aee73d |
| institution | DOAJ |
| issn | 2097-4183 |
| language | zho |
| publishDate | 2024-10-01 |
| publisher | Editorial Office of Energy Environmental Protection |
| record_format | Article |
| series | 能源环境保护 |
| spelling | doaj-art-2796df8d08c749eb89ddd40b70aee73d2025-08-20T03:17:02ZzhoEditorial Office of Energy Environmental Protection能源环境保护2097-41832024-10-0138512713410.20078/j.eep.20240704Study of model construction of fuel production from waste plastic pyrolysis based on machine learningCHEN Sihan0YUAN Zhilong1WANG Ye2SUN Yifei*3School of Energy and Power Engineering, Beihang University, Beijing 102206, ChinaSchool of Energy and Power Engineering, Beihang University, Beijing 102206, ChinaSchool of Energy and Power Engineering, Beihang University, Beijing 102206, China1. School of Energy and Power Engineering, Beihang University, Beijing 102206, China; 2. School of Environmental Science and Engineering, Hainan University, Haikou 570228, China; 3. Research Center for Advanced Energy and Carbon Neutrality, Internation Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, ChinaThe conversion of waste plastics into oil (aviation fuel) and syngas (carbon monoxide and hydrogen) through pyrolysis offers an efficient means of recycling and reusing these plastics. Factors such as feedstock types and working conditions have an important impact on pyrolysis products, which makes the reaction mechanism of pyrolysis process more complex, so it is necessary to explore the reaction nature through a large number of experimental data, and the experimental cost is high. Machine learning has the advantages of large data processing volume and easy extraction of statistical laws, which can reduce costs and research difficulties. A machine-learning approach was applied to utilize data from non-catalytic and molecular sieve catalytic processes and to build a model for analyzing raw material pyrolysis. The Gradient Boosting Regression (GBR) algorithm has the best fitting performance for predicting oil yield (R^2=0.91, RMSE=7.78), while the adaptive boosting algorithm (AdaBoost) has the best fitting performance for predicting gas yield (R^2=0.83, RMSE=6.42), enabling accurate prediction of reaction conditions. It was found that optimal oil yield occurred at a heating rate of approximately 20 ℃/min and a temperature of 500 ℃ through importance ranking and single dependency analyses. Additionally, a dual dependency analysis of oil yield with reaction temperature, heating rate, and reaction time was conducted. This study quantified the effects of heating rate, pyrolysis temperature and other reaction conditions on the oil and gas yield of plastic pyrolysis, which provides a theoretical basis for the production practice of waste plastic recycling.https://eep1987.com/en/article/5137waste plastic pyrolysismolecular sieve catalystmachine learninggradient boostingdependency analysis |
| spellingShingle | CHEN Sihan YUAN Zhilong WANG Ye SUN Yifei* Study of model construction of fuel production from waste plastic pyrolysis based on machine learning 能源环境保护 waste plastic pyrolysis molecular sieve catalyst machine learning gradient boosting dependency analysis |
| title | Study of model construction of fuel production from waste plastic pyrolysis based on machine learning |
| title_full | Study of model construction of fuel production from waste plastic pyrolysis based on machine learning |
| title_fullStr | Study of model construction of fuel production from waste plastic pyrolysis based on machine learning |
| title_full_unstemmed | Study of model construction of fuel production from waste plastic pyrolysis based on machine learning |
| title_short | Study of model construction of fuel production from waste plastic pyrolysis based on machine learning |
| title_sort | study of model construction of fuel production from waste plastic pyrolysis based on machine learning |
| topic | waste plastic pyrolysis molecular sieve catalyst machine learning gradient boosting dependency analysis |
| url | https://eep1987.com/en/article/5137 |
| work_keys_str_mv | AT chensihan studyofmodelconstructionoffuelproductionfromwasteplasticpyrolysisbasedonmachinelearning AT yuanzhilong studyofmodelconstructionoffuelproductionfromwasteplasticpyrolysisbasedonmachinelearning AT wangye studyofmodelconstructionoffuelproductionfromwasteplasticpyrolysisbasedonmachinelearning AT sunyifei studyofmodelconstructionoffuelproductionfromwasteplasticpyrolysisbasedonmachinelearning |