Prediction of Pyrolysis Kinetics of Biomass: New Insights from Artificial Intelligence-Based Modeling
The present work introduces a quantitative structure-property relationship (QSPR)-based stochastic gradient boosting (SGB) decision tree framework for simulating and capturing of the thermal decomposition kinetics of biomass considering effective parameters of the ultimate analysis (such as carbon,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Chemical Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/6491745 |
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| _version_ | 1850109658227802112 |
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| author | Lei Dong RanRan Wang PeiDe Liu Saeed Sarvazizi |
| author_facet | Lei Dong RanRan Wang PeiDe Liu Saeed Sarvazizi |
| author_sort | Lei Dong |
| collection | DOAJ |
| description | The present work introduces a quantitative structure-property relationship (QSPR)-based stochastic gradient boosting (SGB) decision tree framework for simulating and capturing of the thermal decomposition kinetics of biomass considering effective parameters of the ultimate analysis (such as carbon, hydrogen, oxygen, nitrogen, and sulfur content) and process heating rate. Through a total of 149 pyrolysis kinetics, this study developed an artificial model and subjected it to training and testing phases. The proposed model was validated using error analysis, sensitivity, regression, and outlier detection. The coefficient of determination (R2) and mean relative error (%MRE) were calculated to be 0.993 and 4.354%, respectively, suggesting good performance in the estimation of the pyrolysis kinetic parameters. Also, the sensitivity results indicated the process heating rate to have the strongest effect on the model output with a relevancy factor of 0.43. Eventually, the proposed model showed superior performance compared to earlier frameworks. |
| format | Article |
| id | doaj-art-482014af53d64b14a2488cfcda5b8e58 |
| institution | OA Journals |
| issn | 1687-8078 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Chemical Engineering |
| spelling | doaj-art-482014af53d64b14a2488cfcda5b8e582025-08-20T02:38:01ZengWileyInternational Journal of Chemical Engineering1687-80782022-01-01202210.1155/2022/6491745Prediction of Pyrolysis Kinetics of Biomass: New Insights from Artificial Intelligence-Based ModelingLei Dong0RanRan Wang1PeiDe Liu2Saeed Sarvazizi3Department of Mechanical EngineeringAviation Service DepartmentYantai Lutong Precision Technology Co.,Ltd.Department of Petroleum EngineeringThe present work introduces a quantitative structure-property relationship (QSPR)-based stochastic gradient boosting (SGB) decision tree framework for simulating and capturing of the thermal decomposition kinetics of biomass considering effective parameters of the ultimate analysis (such as carbon, hydrogen, oxygen, nitrogen, and sulfur content) and process heating rate. Through a total of 149 pyrolysis kinetics, this study developed an artificial model and subjected it to training and testing phases. The proposed model was validated using error analysis, sensitivity, regression, and outlier detection. The coefficient of determination (R2) and mean relative error (%MRE) were calculated to be 0.993 and 4.354%, respectively, suggesting good performance in the estimation of the pyrolysis kinetic parameters. Also, the sensitivity results indicated the process heating rate to have the strongest effect on the model output with a relevancy factor of 0.43. Eventually, the proposed model showed superior performance compared to earlier frameworks.http://dx.doi.org/10.1155/2022/6491745 |
| spellingShingle | Lei Dong RanRan Wang PeiDe Liu Saeed Sarvazizi Prediction of Pyrolysis Kinetics of Biomass: New Insights from Artificial Intelligence-Based Modeling International Journal of Chemical Engineering |
| title | Prediction of Pyrolysis Kinetics of Biomass: New Insights from Artificial Intelligence-Based Modeling |
| title_full | Prediction of Pyrolysis Kinetics of Biomass: New Insights from Artificial Intelligence-Based Modeling |
| title_fullStr | Prediction of Pyrolysis Kinetics of Biomass: New Insights from Artificial Intelligence-Based Modeling |
| title_full_unstemmed | Prediction of Pyrolysis Kinetics of Biomass: New Insights from Artificial Intelligence-Based Modeling |
| title_short | Prediction of Pyrolysis Kinetics of Biomass: New Insights from Artificial Intelligence-Based Modeling |
| title_sort | prediction of pyrolysis kinetics of biomass new insights from artificial intelligence based modeling |
| url | http://dx.doi.org/10.1155/2022/6491745 |
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