A machine learning model to predict the pyrolytic kinetics of different types of feedstocks
An in-depth knowledge of pyrolytic kinetics is vital for understanding the thermal decomposition process. Numerous experimental studies have investigated the kinetic performance of the pyrolysis of different raw materials. An accurate prediction of pyrolysis kinetics could substantially reduce the efforts of researchers and decrease the cost of experiments. In this work, a model to predict the mean values of model-free activation energies of pyrolysis for five types of feedstocks was successfully constructed using the random forest machine learning method. The coefficient of determination of the fitting result reached a value as high as 0.9964, which indicates significant potential for making a quick initial pyrolytic kinetic estimation using machine learning methods. Specifically, from the results of a partial dependence analysis of the lignocellulose-type feedstock, the atomic ratios of H/C and O/C were found to have negative correlations with the pyrolytic activation energies. However, the effect of the ash content on the activation energy strongly depended on the organic component species present in the lignocellulose feedstocks. This work confirms the possibility of predicting model-free pyrolytic activation energies by utilizing machine learning methods, which can improve the efficiency and understanding of the kinetic analysis of pyrolysis for biomass and fossil investigations.
Main Authors: | , , , , , , , , , , |
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Format: | Article/Letter to editor biblioteca |
Language: | English |
Subjects: | Kinetics, Machine learning, Prediction, Pyrolysis, Random forest, |
Online Access: | https://research.wur.nl/en/publications/a-machine-learning-model-to-predict-the-pyrolytic-kinetics-of-dif |
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dig-wur-nl-wurpubs-5967622024-10-02 Wang, Shule Shi, Ziyi Jin, Yanghao Zaini, Ilman Nuran Li, Yan Tang, Chuchu Mu, Wangzhong Wen, Yuming Jiang, Jianchun Jönsson, Pär Göran Yang, Weihong Article/Letter to editor Energy Conversion and Management 260 (2022) ISSN: 0196-8904 A machine learning model to predict the pyrolytic kinetics of different types of feedstocks 2022 An in-depth knowledge of pyrolytic kinetics is vital for understanding the thermal decomposition process. Numerous experimental studies have investigated the kinetic performance of the pyrolysis of different raw materials. An accurate prediction of pyrolysis kinetics could substantially reduce the efforts of researchers and decrease the cost of experiments. In this work, a model to predict the mean values of model-free activation energies of pyrolysis for five types of feedstocks was successfully constructed using the random forest machine learning method. The coefficient of determination of the fitting result reached a value as high as 0.9964, which indicates significant potential for making a quick initial pyrolytic kinetic estimation using machine learning methods. Specifically, from the results of a partial dependence analysis of the lignocellulose-type feedstock, the atomic ratios of H/C and O/C were found to have negative correlations with the pyrolytic activation energies. However, the effect of the ash content on the activation energy strongly depended on the organic component species present in the lignocellulose feedstocks. This work confirms the possibility of predicting model-free pyrolytic activation energies by utilizing machine learning methods, which can improve the efficiency and understanding of the kinetic analysis of pyrolysis for biomass and fossil investigations. en application/pdf https://research.wur.nl/en/publications/a-machine-learning-model-to-predict-the-pyrolytic-kinetics-of-dif 10.1016/j.enconman.2022.115613 https://edepot.wur.nl/568917 Kinetics Machine learning Prediction Pyrolysis Random forest https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ Wageningen University & Research |
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Kinetics Machine learning Prediction Pyrolysis Random forest Kinetics Machine learning Prediction Pyrolysis Random forest |
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Kinetics Machine learning Prediction Pyrolysis Random forest Kinetics Machine learning Prediction Pyrolysis Random forest Wang, Shule Shi, Ziyi Jin, Yanghao Zaini, Ilman Nuran Li, Yan Tang, Chuchu Mu, Wangzhong Wen, Yuming Jiang, Jianchun Jönsson, Pär Göran Yang, Weihong A machine learning model to predict the pyrolytic kinetics of different types of feedstocks |
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An in-depth knowledge of pyrolytic kinetics is vital for understanding the thermal decomposition process. Numerous experimental studies have investigated the kinetic performance of the pyrolysis of different raw materials. An accurate prediction of pyrolysis kinetics could substantially reduce the efforts of researchers and decrease the cost of experiments. In this work, a model to predict the mean values of model-free activation energies of pyrolysis for five types of feedstocks was successfully constructed using the random forest machine learning method. The coefficient of determination of the fitting result reached a value as high as 0.9964, which indicates significant potential for making a quick initial pyrolytic kinetic estimation using machine learning methods. Specifically, from the results of a partial dependence analysis of the lignocellulose-type feedstock, the atomic ratios of H/C and O/C were found to have negative correlations with the pyrolytic activation energies. However, the effect of the ash content on the activation energy strongly depended on the organic component species present in the lignocellulose feedstocks. This work confirms the possibility of predicting model-free pyrolytic activation energies by utilizing machine learning methods, which can improve the efficiency and understanding of the kinetic analysis of pyrolysis for biomass and fossil investigations. |
format |
Article/Letter to editor |
topic_facet |
Kinetics Machine learning Prediction Pyrolysis Random forest |
author |
Wang, Shule Shi, Ziyi Jin, Yanghao Zaini, Ilman Nuran Li, Yan Tang, Chuchu Mu, Wangzhong Wen, Yuming Jiang, Jianchun Jönsson, Pär Göran Yang, Weihong |
author_facet |
Wang, Shule Shi, Ziyi Jin, Yanghao Zaini, Ilman Nuran Li, Yan Tang, Chuchu Mu, Wangzhong Wen, Yuming Jiang, Jianchun Jönsson, Pär Göran Yang, Weihong |
author_sort |
Wang, Shule |
title |
A machine learning model to predict the pyrolytic kinetics of different types of feedstocks |
title_short |
A machine learning model to predict the pyrolytic kinetics of different types of feedstocks |
title_full |
A machine learning model to predict the pyrolytic kinetics of different types of feedstocks |
title_fullStr |
A machine learning model to predict the pyrolytic kinetics of different types of feedstocks |
title_full_unstemmed |
A machine learning model to predict the pyrolytic kinetics of different types of feedstocks |
title_sort |
machine learning model to predict the pyrolytic kinetics of different types of feedstocks |
url |
https://research.wur.nl/en/publications/a-machine-learning-model-to-predict-the-pyrolytic-kinetics-of-dif |
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