Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network
•A novel deep learning model is established for predicting combustion stability.•Automatic generation of combustion stability label is achieved.•Quantitative and qualitative evaluation of combustion stability are presented.•Generalization and robustness of the model are verified. Combustion instabil...
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| Vydáno v: | Applied energy Ročník 259; s. 114159 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.02.2020
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| Témata: | |
| ISSN: | 0306-2619, 1872-9118 |
| On-line přístup: | Získat plný text |
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| Abstract | •A novel deep learning model is established for predicting combustion stability.•Automatic generation of combustion stability label is achieved.•Quantitative and qualitative evaluation of combustion stability are presented.•Generalization and robustness of the model are verified.
Combustion instability is a well-known problem in the combustion processes and closely linked to lower combustion efficiency and higher pollutant emissions. Therefore, it is important to monitor combustion stability for optimizing efficiency and maintaining furnace safety. However, it is difficult to establish a robust monitoring model with high precision through traditional data-driven methods, where prior knowledge of labeled data is required. This study proposes a novel approach for combustion stability monitoring through stacked sparse autoencoder based deep neural network. The proposed stacked sparse autoencoder is firstly utilized to extract flame representative features from the unlabeled images, and an improved loss function is used to enhance the training efficiency. The extracted features are then used to identify the classification label and stability index through clustering and statistical analysis. Classification and regression models incorporating the stacked sparse autoencoder are established for the qualitative and quantitative characterization of combustion stability. Experiments were carried out on a gas combustor to establish and evaluate the proposed models. It has been found that the classification model provides an F1-score of 0.99, whilst the R-squared of 0.98 is achieved through the regression model. Results obtained from the experiments demonstrated that the stacked sparse autoencoder model is capable of extracting flame representative features automatically without having manual interference. The results also show that the proposed model provides a higher prediction accuracy in comparison to the traditional data-driven methods and also demonstrates as a promising tool for monitoring the combustion stability accurately. |
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| AbstractList | •A novel deep learning model is established for predicting combustion stability.•Automatic generation of combustion stability label is achieved.•Quantitative and qualitative evaluation of combustion stability are presented.•Generalization and robustness of the model are verified.
Combustion instability is a well-known problem in the combustion processes and closely linked to lower combustion efficiency and higher pollutant emissions. Therefore, it is important to monitor combustion stability for optimizing efficiency and maintaining furnace safety. However, it is difficult to establish a robust monitoring model with high precision through traditional data-driven methods, where prior knowledge of labeled data is required. This study proposes a novel approach for combustion stability monitoring through stacked sparse autoencoder based deep neural network. The proposed stacked sparse autoencoder is firstly utilized to extract flame representative features from the unlabeled images, and an improved loss function is used to enhance the training efficiency. The extracted features are then used to identify the classification label and stability index through clustering and statistical analysis. Classification and regression models incorporating the stacked sparse autoencoder are established for the qualitative and quantitative characterization of combustion stability. Experiments were carried out on a gas combustor to establish and evaluate the proposed models. It has been found that the classification model provides an F1-score of 0.99, whilst the R-squared of 0.98 is achieved through the regression model. Results obtained from the experiments demonstrated that the stacked sparse autoencoder model is capable of extracting flame representative features automatically without having manual interference. The results also show that the proposed model provides a higher prediction accuracy in comparison to the traditional data-driven methods and also demonstrates as a promising tool for monitoring the combustion stability accurately. Combustion instability is a well-known problem in the combustion processes and closely linked to lower combustion efficiency and higher pollutant emissions. Therefore, it is important to monitor combustion stability for optimizing efficiency and maintaining furnace safety. However, it is difficult to establish a robust monitoring model with high precision through traditional data-driven methods, where prior knowledge of labeled data is required. This study proposes a novel approach for combustion stability monitoring through stacked sparse autoencoder based deep neural network. The proposed stacked sparse autoencoder is firstly utilized to extract flame representative features from the unlabeled images, and an improved loss function is used to enhance the training efficiency. The extracted features are then used to identify the classification label and stability index through clustering and statistical analysis. Classification and regression models incorporating the stacked sparse autoencoder are established for the qualitative and quantitative characterization of combustion stability. Experiments were carried out on a gas combustor to establish and evaluate the proposed models. It has been found that the classification model provides an F₁-score of 0.99, whilst the R-squared of 0.98 is achieved through the regression model. Results obtained from the experiments demonstrated that the stacked sparse autoencoder model is capable of extracting flame representative features automatically without having manual interference. The results also show that the proposed model provides a higher prediction accuracy in comparison to the traditional data-driven methods and also demonstrates as a promising tool for monitoring the combustion stability accurately. |
| ArticleNumber | 114159 |
| Author | Hossain, Md. Moinul Xu, Chuanlong Li, Jian Han, Zhezhe Wang, Yuwei |
| Author_xml | – sequence: 1 givenname: Zhezhe surname: Han fullname: Han, Zhezhe organization: Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China – sequence: 2 givenname: Md. Moinul surname: Hossain fullname: Hossain, Md. Moinul organization: School of Engineering and Digital Arts, University of Kent, Canterbury, Kent CT2 7NT, UK – sequence: 3 givenname: Yuwei surname: Wang fullname: Wang, Yuwei organization: China Energy Jianbi Power Plant, Zhenjiang 212006, China – sequence: 4 givenname: Jian surname: Li fullname: Li, Jian organization: Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China – sequence: 5 givenname: Chuanlong surname: Xu fullname: Xu, Chuanlong email: chuanlongxu@seu.edu.cn organization: Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China |
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| Keywords | Innovative loss function Stacked sparse autoencoder Combustion stability Flame imaging Gaussian mixture model |
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| Snippet | •A novel deep learning model is established for predicting combustion stability.•Automatic generation of combustion stability label is achieved.•Quantitative... Combustion instability is a well-known problem in the combustion processes and closely linked to lower combustion efficiency and higher pollutant emissions.... |
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| SubjectTerms | combustion combustion efficiency Combustion stability emissions Flame imaging furnaces Gaussian mixture model image analysis Innovative loss function monitoring pollutants prediction regression analysis Stacked sparse autoencoder |
| Title | Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network |
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