Stabilization temperature prediction in carbon fiber production using empirical mode decomposition and long short-term memory network
Carbon fiber holds significant promise as a sustainable material with diverse applications. The production of carbon fiber involves a critical process known as oxidative stabilization. Optimizing this process necessitates accurate prediction of the stabilization temperature. However, predicting the...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Guo, Yuanjing [verfasserIn] Jiang, Shaofei [verfasserIn] Fu, Jiangen [verfasserIn] Yang, Youdong [verfasserIn] Bao, Yumei [verfasserIn] Jin, Xiaohang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of cleaner production - Amsterdam [u.a.] : Elsevier Science, 1993, 429 |
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| Übergeordnetes Werk: |
volume:429 |
| DOI / URN: |
10.1016/j.jclepro.2023.139345 |
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| Katalog-ID: |
ELV065697103 |
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| 245 | 1 | 0 | |a Stabilization temperature prediction in carbon fiber production using empirical mode decomposition and long short-term memory network |
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| 520 | |a Carbon fiber holds significant promise as a sustainable material with diverse applications. The production of carbon fiber involves a critical process known as oxidative stabilization. Optimizing this process necessitates accurate prediction of the stabilization temperature. However, predicting the stabilization temperature based on easily available heater power data encounters formidable challenges due to factors like nonlinearity, non-uniformity, time delay and time-varying effect. To address these issues, this study proposed an innovative method for predicting the stabilization temperature using empirical mode decomposition (EMD) and long short-term memory (LSTM) network. The distinctive aspect of this proposed method lies in the utilization of two EMD-based reconstruction approaches to preprocess the heater power training and testing data. This helps mitigate the nonlinearity between stabilization temperature and heater power output during the machine learning network training phase and alleviate the time-varying effect of heater power testing data relative to the training data during the network testing phase. Considering the hysteresis effect of heater power output on stabilization temperature, an LSTM network was integrated for accurate stabilization temperature prediction. The proposed EMD-LSTM network model was implemented for stabilization temperature predictions in six oxidation ovens of a carbon fiber production line. The average metrics for predicted results using this method are notably superior, with values of 0.0153 °C for root-mean-square error (RMSE), 0.3711 for relative RMSE (rRMSE), 0.0117 °C for mean absolute error (MAE), and 0.2838 for relative MAE (rMAE), surpassing the results obtained from the LSTM network model trained and tested using unreconstructed data. The comparative analysis further demonstrates the advantageous performance of the proposed EMD-LSTM network model compared to models based on five widely used machine learning networks. This study offers valuable insights for optimizing the oxidative stabilization process in carbon fiber production, thereby reducing waste heat and exhaust emissions, and promoting cleaner carbon fiber production. | ||
| 650 | 4 | |a Carbon fiber | |
| 650 | 4 | |a Stabilization temperature | |
| 650 | 4 | |a Prediction | |
| 650 | 4 | |a Empirical mode decomposition | |
| 650 | 4 | |a Long short-term memory network | |
| 700 | 1 | |a Jiang, Shaofei |e verfasserin |0 (orcid)0000-0003-4093-0192 |4 aut | |
| 700 | 1 | |a Fu, Jiangen |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Youdong |e verfasserin |4 aut | |
| 700 | 1 | |a Bao, Yumei |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, Xiaohang |e verfasserin |4 aut | |
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10.1016/j.jclepro.2023.139345 doi (DE-627)ELV065697103 (ELSEVIER)S0959-6526(23)03503-5 DE-627 ger DE-627 rda eng 690 330 VZ 43.35 bkl 85.35 bkl Guo, Yuanjing verfasserin (orcid)0000-0001-8899-4860 aut Stabilization temperature prediction in carbon fiber production using empirical mode decomposition and long short-term memory network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Carbon fiber holds significant promise as a sustainable material with diverse applications. The production of carbon fiber involves a critical process known as oxidative stabilization. Optimizing this process necessitates accurate prediction of the stabilization temperature. However, predicting the stabilization temperature based on easily available heater power data encounters formidable challenges due to factors like nonlinearity, non-uniformity, time delay and time-varying effect. To address these issues, this study proposed an innovative method for predicting the stabilization temperature using empirical mode decomposition (EMD) and long short-term memory (LSTM) network. The distinctive aspect of this proposed method lies in the utilization of two EMD-based reconstruction approaches to preprocess the heater power training and testing data. This helps mitigate the nonlinearity between stabilization temperature and heater power output during the machine learning network training phase and alleviate the time-varying effect of heater power testing data relative to the training data during the network testing phase. Considering the hysteresis effect of heater power output on stabilization temperature, an LSTM network was integrated for accurate stabilization temperature prediction. The proposed EMD-LSTM network model was implemented for stabilization temperature predictions in six oxidation ovens of a carbon fiber production line. The average metrics for predicted results using this method are notably superior, with values of 0.0153 °C for root-mean-square error (RMSE), 0.3711 for relative RMSE (rRMSE), 0.0117 °C for mean absolute error (MAE), and 0.2838 for relative MAE (rMAE), surpassing the results obtained from the LSTM network model trained and tested using unreconstructed data. The comparative analysis further demonstrates the advantageous performance of the proposed EMD-LSTM network model compared to models based on five widely used machine learning networks. This study offers valuable insights for optimizing the oxidative stabilization process in carbon fiber production, thereby reducing waste heat and exhaust emissions, and promoting cleaner carbon fiber production. Carbon fiber Stabilization temperature Prediction Empirical mode decomposition Long short-term memory network Jiang, Shaofei verfasserin (orcid)0000-0003-4093-0192 aut Fu, Jiangen verfasserin aut Yang, Youdong verfasserin aut Bao, Yumei verfasserin aut Jin, Xiaohang verfasserin aut Enthalten in Journal of cleaner production Amsterdam [u.a.] : Elsevier Science, 1993 429 Online-Ressource (DE-627)324655878 (DE-600)2029338-0 (DE-576)252613988 0959-6526 nnns volume:429 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.35 Umweltrichtlinien Umweltnormen VZ 85.35 Fertigung VZ AR 429 |
| spelling |
10.1016/j.jclepro.2023.139345 doi (DE-627)ELV065697103 (ELSEVIER)S0959-6526(23)03503-5 DE-627 ger DE-627 rda eng 690 330 VZ 43.35 bkl 85.35 bkl Guo, Yuanjing verfasserin (orcid)0000-0001-8899-4860 aut Stabilization temperature prediction in carbon fiber production using empirical mode decomposition and long short-term memory network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Carbon fiber holds significant promise as a sustainable material with diverse applications. The production of carbon fiber involves a critical process known as oxidative stabilization. Optimizing this process necessitates accurate prediction of the stabilization temperature. However, predicting the stabilization temperature based on easily available heater power data encounters formidable challenges due to factors like nonlinearity, non-uniformity, time delay and time-varying effect. To address these issues, this study proposed an innovative method for predicting the stabilization temperature using empirical mode decomposition (EMD) and long short-term memory (LSTM) network. The distinctive aspect of this proposed method lies in the utilization of two EMD-based reconstruction approaches to preprocess the heater power training and testing data. This helps mitigate the nonlinearity between stabilization temperature and heater power output during the machine learning network training phase and alleviate the time-varying effect of heater power testing data relative to the training data during the network testing phase. Considering the hysteresis effect of heater power output on stabilization temperature, an LSTM network was integrated for accurate stabilization temperature prediction. The proposed EMD-LSTM network model was implemented for stabilization temperature predictions in six oxidation ovens of a carbon fiber production line. The average metrics for predicted results using this method are notably superior, with values of 0.0153 °C for root-mean-square error (RMSE), 0.3711 for relative RMSE (rRMSE), 0.0117 °C for mean absolute error (MAE), and 0.2838 for relative MAE (rMAE), surpassing the results obtained from the LSTM network model trained and tested using unreconstructed data. The comparative analysis further demonstrates the advantageous performance of the proposed EMD-LSTM network model compared to models based on five widely used machine learning networks. This study offers valuable insights for optimizing the oxidative stabilization process in carbon fiber production, thereby reducing waste heat and exhaust emissions, and promoting cleaner carbon fiber production. Carbon fiber Stabilization temperature Prediction Empirical mode decomposition Long short-term memory network Jiang, Shaofei verfasserin (orcid)0000-0003-4093-0192 aut Fu, Jiangen verfasserin aut Yang, Youdong verfasserin aut Bao, Yumei verfasserin aut Jin, Xiaohang verfasserin aut Enthalten in Journal of cleaner production Amsterdam [u.a.] : Elsevier Science, 1993 429 Online-Ressource (DE-627)324655878 (DE-600)2029338-0 (DE-576)252613988 0959-6526 nnns volume:429 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.35 Umweltrichtlinien Umweltnormen VZ 85.35 Fertigung VZ AR 429 |
| allfields_unstemmed |
10.1016/j.jclepro.2023.139345 doi (DE-627)ELV065697103 (ELSEVIER)S0959-6526(23)03503-5 DE-627 ger DE-627 rda eng 690 330 VZ 43.35 bkl 85.35 bkl Guo, Yuanjing verfasserin (orcid)0000-0001-8899-4860 aut Stabilization temperature prediction in carbon fiber production using empirical mode decomposition and long short-term memory network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Carbon fiber holds significant promise as a sustainable material with diverse applications. The production of carbon fiber involves a critical process known as oxidative stabilization. Optimizing this process necessitates accurate prediction of the stabilization temperature. However, predicting the stabilization temperature based on easily available heater power data encounters formidable challenges due to factors like nonlinearity, non-uniformity, time delay and time-varying effect. To address these issues, this study proposed an innovative method for predicting the stabilization temperature using empirical mode decomposition (EMD) and long short-term memory (LSTM) network. The distinctive aspect of this proposed method lies in the utilization of two EMD-based reconstruction approaches to preprocess the heater power training and testing data. This helps mitigate the nonlinearity between stabilization temperature and heater power output during the machine learning network training phase and alleviate the time-varying effect of heater power testing data relative to the training data during the network testing phase. Considering the hysteresis effect of heater power output on stabilization temperature, an LSTM network was integrated for accurate stabilization temperature prediction. The proposed EMD-LSTM network model was implemented for stabilization temperature predictions in six oxidation ovens of a carbon fiber production line. The average metrics for predicted results using this method are notably superior, with values of 0.0153 °C for root-mean-square error (RMSE), 0.3711 for relative RMSE (rRMSE), 0.0117 °C for mean absolute error (MAE), and 0.2838 for relative MAE (rMAE), surpassing the results obtained from the LSTM network model trained and tested using unreconstructed data. The comparative analysis further demonstrates the advantageous performance of the proposed EMD-LSTM network model compared to models based on five widely used machine learning networks. This study offers valuable insights for optimizing the oxidative stabilization process in carbon fiber production, thereby reducing waste heat and exhaust emissions, and promoting cleaner carbon fiber production. Carbon fiber Stabilization temperature Prediction Empirical mode decomposition Long short-term memory network Jiang, Shaofei verfasserin (orcid)0000-0003-4093-0192 aut Fu, Jiangen verfasserin aut Yang, Youdong verfasserin aut Bao, Yumei verfasserin aut Jin, Xiaohang verfasserin aut Enthalten in Journal of cleaner production Amsterdam [u.a.] : Elsevier Science, 1993 429 Online-Ressource (DE-627)324655878 (DE-600)2029338-0 (DE-576)252613988 0959-6526 nnns volume:429 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.35 Umweltrichtlinien Umweltnormen VZ 85.35 Fertigung VZ AR 429 |
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10.1016/j.jclepro.2023.139345 doi (DE-627)ELV065697103 (ELSEVIER)S0959-6526(23)03503-5 DE-627 ger DE-627 rda eng 690 330 VZ 43.35 bkl 85.35 bkl Guo, Yuanjing verfasserin (orcid)0000-0001-8899-4860 aut Stabilization temperature prediction in carbon fiber production using empirical mode decomposition and long short-term memory network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Carbon fiber holds significant promise as a sustainable material with diverse applications. The production of carbon fiber involves a critical process known as oxidative stabilization. Optimizing this process necessitates accurate prediction of the stabilization temperature. However, predicting the stabilization temperature based on easily available heater power data encounters formidable challenges due to factors like nonlinearity, non-uniformity, time delay and time-varying effect. To address these issues, this study proposed an innovative method for predicting the stabilization temperature using empirical mode decomposition (EMD) and long short-term memory (LSTM) network. The distinctive aspect of this proposed method lies in the utilization of two EMD-based reconstruction approaches to preprocess the heater power training and testing data. This helps mitigate the nonlinearity between stabilization temperature and heater power output during the machine learning network training phase and alleviate the time-varying effect of heater power testing data relative to the training data during the network testing phase. Considering the hysteresis effect of heater power output on stabilization temperature, an LSTM network was integrated for accurate stabilization temperature prediction. The proposed EMD-LSTM network model was implemented for stabilization temperature predictions in six oxidation ovens of a carbon fiber production line. The average metrics for predicted results using this method are notably superior, with values of 0.0153 °C for root-mean-square error (RMSE), 0.3711 for relative RMSE (rRMSE), 0.0117 °C for mean absolute error (MAE), and 0.2838 for relative MAE (rMAE), surpassing the results obtained from the LSTM network model trained and tested using unreconstructed data. The comparative analysis further demonstrates the advantageous performance of the proposed EMD-LSTM network model compared to models based on five widely used machine learning networks. This study offers valuable insights for optimizing the oxidative stabilization process in carbon fiber production, thereby reducing waste heat and exhaust emissions, and promoting cleaner carbon fiber production. Carbon fiber Stabilization temperature Prediction Empirical mode decomposition Long short-term memory network Jiang, Shaofei verfasserin (orcid)0000-0003-4093-0192 aut Fu, Jiangen verfasserin aut Yang, Youdong verfasserin aut Bao, Yumei verfasserin aut Jin, Xiaohang verfasserin aut Enthalten in Journal of cleaner production Amsterdam [u.a.] : Elsevier Science, 1993 429 Online-Ressource (DE-627)324655878 (DE-600)2029338-0 (DE-576)252613988 0959-6526 nnns volume:429 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.35 Umweltrichtlinien Umweltnormen VZ 85.35 Fertigung VZ AR 429 |
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10.1016/j.jclepro.2023.139345 doi (DE-627)ELV065697103 (ELSEVIER)S0959-6526(23)03503-5 DE-627 ger DE-627 rda eng 690 330 VZ 43.35 bkl 85.35 bkl Guo, Yuanjing verfasserin (orcid)0000-0001-8899-4860 aut Stabilization temperature prediction in carbon fiber production using empirical mode decomposition and long short-term memory network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Carbon fiber holds significant promise as a sustainable material with diverse applications. The production of carbon fiber involves a critical process known as oxidative stabilization. Optimizing this process necessitates accurate prediction of the stabilization temperature. However, predicting the stabilization temperature based on easily available heater power data encounters formidable challenges due to factors like nonlinearity, non-uniformity, time delay and time-varying effect. To address these issues, this study proposed an innovative method for predicting the stabilization temperature using empirical mode decomposition (EMD) and long short-term memory (LSTM) network. The distinctive aspect of this proposed method lies in the utilization of two EMD-based reconstruction approaches to preprocess the heater power training and testing data. This helps mitigate the nonlinearity between stabilization temperature and heater power output during the machine learning network training phase and alleviate the time-varying effect of heater power testing data relative to the training data during the network testing phase. Considering the hysteresis effect of heater power output on stabilization temperature, an LSTM network was integrated for accurate stabilization temperature prediction. The proposed EMD-LSTM network model was implemented for stabilization temperature predictions in six oxidation ovens of a carbon fiber production line. The average metrics for predicted results using this method are notably superior, with values of 0.0153 °C for root-mean-square error (RMSE), 0.3711 for relative RMSE (rRMSE), 0.0117 °C for mean absolute error (MAE), and 0.2838 for relative MAE (rMAE), surpassing the results obtained from the LSTM network model trained and tested using unreconstructed data. The comparative analysis further demonstrates the advantageous performance of the proposed EMD-LSTM network model compared to models based on five widely used machine learning networks. This study offers valuable insights for optimizing the oxidative stabilization process in carbon fiber production, thereby reducing waste heat and exhaust emissions, and promoting cleaner carbon fiber production. Carbon fiber Stabilization temperature Prediction Empirical mode decomposition Long short-term memory network Jiang, Shaofei verfasserin (orcid)0000-0003-4093-0192 aut Fu, Jiangen verfasserin aut Yang, Youdong verfasserin aut Bao, Yumei verfasserin aut Jin, Xiaohang verfasserin aut Enthalten in Journal of cleaner production Amsterdam [u.a.] : Elsevier Science, 1993 429 Online-Ressource (DE-627)324655878 (DE-600)2029338-0 (DE-576)252613988 0959-6526 nnns volume:429 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.35 Umweltrichtlinien Umweltnormen VZ 85.35 Fertigung VZ AR 429 |
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Guo, Yuanjing @@aut@@ Jiang, Shaofei @@aut@@ Fu, Jiangen @@aut@@ Yang, Youdong @@aut@@ Bao, Yumei @@aut@@ Jin, Xiaohang @@aut@@ |
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The production of carbon fiber involves a critical process known as oxidative stabilization. Optimizing this process necessitates accurate prediction of the stabilization temperature. However, predicting the stabilization temperature based on easily available heater power data encounters formidable challenges due to factors like nonlinearity, non-uniformity, time delay and time-varying effect. To address these issues, this study proposed an innovative method for predicting the stabilization temperature using empirical mode decomposition (EMD) and long short-term memory (LSTM) network. The distinctive aspect of this proposed method lies in the utilization of two EMD-based reconstruction approaches to preprocess the heater power training and testing data. This helps mitigate the nonlinearity between stabilization temperature and heater power output during the machine learning network training phase and alleviate the time-varying effect of heater power testing data relative to the training data during the network testing phase. Considering the hysteresis effect of heater power output on stabilization temperature, an LSTM network was integrated for accurate stabilization temperature prediction. The proposed EMD-LSTM network model was implemented for stabilization temperature predictions in six oxidation ovens of a carbon fiber production line. The average metrics for predicted results using this method are notably superior, with values of 0.0153 °C for root-mean-square error (RMSE), 0.3711 for relative RMSE (rRMSE), 0.0117 °C for mean absolute error (MAE), and 0.2838 for relative MAE (rMAE), surpassing the results obtained from the LSTM network model trained and tested using unreconstructed data. 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Guo, Yuanjing |
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Guo, Yuanjing ddc 690 bkl 43.35 bkl 85.35 misc Carbon fiber misc Stabilization temperature misc Prediction misc Empirical mode decomposition misc Long short-term memory network Stabilization temperature prediction in carbon fiber production using empirical mode decomposition and long short-term memory network |
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690 330 VZ 43.35 bkl 85.35 bkl Stabilization temperature prediction in carbon fiber production using empirical mode decomposition and long short-term memory network Carbon fiber Stabilization temperature Prediction Empirical mode decomposition Long short-term memory network |
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stabilization temperature prediction in carbon fiber production using empirical mode decomposition and long short-term memory network |
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Stabilization temperature prediction in carbon fiber production using empirical mode decomposition and long short-term memory network |
| abstract |
Carbon fiber holds significant promise as a sustainable material with diverse applications. The production of carbon fiber involves a critical process known as oxidative stabilization. Optimizing this process necessitates accurate prediction of the stabilization temperature. However, predicting the stabilization temperature based on easily available heater power data encounters formidable challenges due to factors like nonlinearity, non-uniformity, time delay and time-varying effect. To address these issues, this study proposed an innovative method for predicting the stabilization temperature using empirical mode decomposition (EMD) and long short-term memory (LSTM) network. The distinctive aspect of this proposed method lies in the utilization of two EMD-based reconstruction approaches to preprocess the heater power training and testing data. This helps mitigate the nonlinearity between stabilization temperature and heater power output during the machine learning network training phase and alleviate the time-varying effect of heater power testing data relative to the training data during the network testing phase. Considering the hysteresis effect of heater power output on stabilization temperature, an LSTM network was integrated for accurate stabilization temperature prediction. The proposed EMD-LSTM network model was implemented for stabilization temperature predictions in six oxidation ovens of a carbon fiber production line. The average metrics for predicted results using this method are notably superior, with values of 0.0153 °C for root-mean-square error (RMSE), 0.3711 for relative RMSE (rRMSE), 0.0117 °C for mean absolute error (MAE), and 0.2838 for relative MAE (rMAE), surpassing the results obtained from the LSTM network model trained and tested using unreconstructed data. The comparative analysis further demonstrates the advantageous performance of the proposed EMD-LSTM network model compared to models based on five widely used machine learning networks. This study offers valuable insights for optimizing the oxidative stabilization process in carbon fiber production, thereby reducing waste heat and exhaust emissions, and promoting cleaner carbon fiber production. |
| abstractGer |
Carbon fiber holds significant promise as a sustainable material with diverse applications. The production of carbon fiber involves a critical process known as oxidative stabilization. Optimizing this process necessitates accurate prediction of the stabilization temperature. However, predicting the stabilization temperature based on easily available heater power data encounters formidable challenges due to factors like nonlinearity, non-uniformity, time delay and time-varying effect. To address these issues, this study proposed an innovative method for predicting the stabilization temperature using empirical mode decomposition (EMD) and long short-term memory (LSTM) network. The distinctive aspect of this proposed method lies in the utilization of two EMD-based reconstruction approaches to preprocess the heater power training and testing data. This helps mitigate the nonlinearity between stabilization temperature and heater power output during the machine learning network training phase and alleviate the time-varying effect of heater power testing data relative to the training data during the network testing phase. Considering the hysteresis effect of heater power output on stabilization temperature, an LSTM network was integrated for accurate stabilization temperature prediction. The proposed EMD-LSTM network model was implemented for stabilization temperature predictions in six oxidation ovens of a carbon fiber production line. The average metrics for predicted results using this method are notably superior, with values of 0.0153 °C for root-mean-square error (RMSE), 0.3711 for relative RMSE (rRMSE), 0.0117 °C for mean absolute error (MAE), and 0.2838 for relative MAE (rMAE), surpassing the results obtained from the LSTM network model trained and tested using unreconstructed data. The comparative analysis further demonstrates the advantageous performance of the proposed EMD-LSTM network model compared to models based on five widely used machine learning networks. This study offers valuable insights for optimizing the oxidative stabilization process in carbon fiber production, thereby reducing waste heat and exhaust emissions, and promoting cleaner carbon fiber production. |
| abstract_unstemmed |
Carbon fiber holds significant promise as a sustainable material with diverse applications. The production of carbon fiber involves a critical process known as oxidative stabilization. Optimizing this process necessitates accurate prediction of the stabilization temperature. However, predicting the stabilization temperature based on easily available heater power data encounters formidable challenges due to factors like nonlinearity, non-uniformity, time delay and time-varying effect. To address these issues, this study proposed an innovative method for predicting the stabilization temperature using empirical mode decomposition (EMD) and long short-term memory (LSTM) network. The distinctive aspect of this proposed method lies in the utilization of two EMD-based reconstruction approaches to preprocess the heater power training and testing data. This helps mitigate the nonlinearity between stabilization temperature and heater power output during the machine learning network training phase and alleviate the time-varying effect of heater power testing data relative to the training data during the network testing phase. Considering the hysteresis effect of heater power output on stabilization temperature, an LSTM network was integrated for accurate stabilization temperature prediction. The proposed EMD-LSTM network model was implemented for stabilization temperature predictions in six oxidation ovens of a carbon fiber production line. The average metrics for predicted results using this method are notably superior, with values of 0.0153 °C for root-mean-square error (RMSE), 0.3711 for relative RMSE (rRMSE), 0.0117 °C for mean absolute error (MAE), and 0.2838 for relative MAE (rMAE), surpassing the results obtained from the LSTM network model trained and tested using unreconstructed data. The comparative analysis further demonstrates the advantageous performance of the proposed EMD-LSTM network model compared to models based on five widely used machine learning networks. This study offers valuable insights for optimizing the oxidative stabilization process in carbon fiber production, thereby reducing waste heat and exhaust emissions, and promoting cleaner carbon fiber production. |
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| score |
7.402276 |