Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines
To improve the economy and meet the demand for transporting different oil products, multi-product pipelines are utilized to transport multi-products in sequence in the same pipelines. It is fundamentally important for operators in stations to know the accurate location of the head of each batch inte...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zheng, Jianqin [verfasserIn] Du, Jian [verfasserIn] Liang, Yongtu [verfasserIn] Wang, Bohong [verfasserIn] Li, Miao [verfasserIn] Liao, Qi [verfasserIn] Xu, Ning [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: Chemical engineering research and design - Amsterdam : Elsevier, 1983, 191, Seite 236-248 |
|---|---|
| Übergeordnetes Werk: |
volume:191 ; pages:236-248 |
| DOI / URN: |
10.1016/j.cherd.2022.12.036 |
|---|
| Katalog-ID: |
ELV06330189X |
|---|
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| 245 | 1 | 0 | |a Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines |
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| 520 | |a To improve the economy and meet the demand for transporting different oil products, multi-product pipelines are utilized to transport multi-products in sequence in the same pipelines. It is fundamentally important for operators in stations to know the accurate location of the head of each batch interface, to swing the valve at a station, and deliver oil products with minimal contamination. However, it is difficult to determine the location of the batch interface accurately, due to the complex hydrothermal conditions and mixed oil segment. In this paper, a hybrid intelligent framework is proposed to track the real-time batch interface of multi-product pipelines. The batch injection judgment module is applied to determine whether there is a new product batch injected in the pipeline. Applying the upstream and downstream flowrate, the volume calculation model is proposed to track the real-time location of each batch interface. Considering the deviation between the estimated location and the actual location of the batch interface, a self-learning modified model is proposed to compensate for the tracking errors. Taking a real-world multi-product pipeline network in China as an example, the accuracy and efficiency of the proposed model are verified. The results suggested that the hybrid intelligent framework outperforms other comparative methods, with minimal tracking errors being 3.79 min | ||
| 650 | 4 | |a Multi-product pipeline | |
| 650 | 4 | |a Batch tracking | |
| 650 | 4 | |a Hybrid intelligent framework | |
| 650 | 4 | |a Data-driven | |
| 650 | 4 | |a Modified model | |
| 700 | 1 | |a Du, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Yongtu |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Bohong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Miao |e verfasserin |4 aut | |
| 700 | 1 | |a Liao, Qi |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Ning |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Chemical engineering research and design |d Amsterdam : Elsevier, 1983 |g 191, Seite 236-248 |h Online-Ressource |w (DE-627)312841965 |w (DE-600)2008006-2 |w (DE-576)090893190 |x 1744-3563 |7 nnns |
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10.1016/j.cherd.2022.12.036 doi (DE-627)ELV06330189X (ELSEVIER)S0263-8762(22)00738-9 DE-627 ger DE-627 rda eng 540 660 VZ 58.10 bkl Zheng, Jianqin verfasserin aut Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To improve the economy and meet the demand for transporting different oil products, multi-product pipelines are utilized to transport multi-products in sequence in the same pipelines. It is fundamentally important for operators in stations to know the accurate location of the head of each batch interface, to swing the valve at a station, and deliver oil products with minimal contamination. However, it is difficult to determine the location of the batch interface accurately, due to the complex hydrothermal conditions and mixed oil segment. In this paper, a hybrid intelligent framework is proposed to track the real-time batch interface of multi-product pipelines. The batch injection judgment module is applied to determine whether there is a new product batch injected in the pipeline. Applying the upstream and downstream flowrate, the volume calculation model is proposed to track the real-time location of each batch interface. Considering the deviation between the estimated location and the actual location of the batch interface, a self-learning modified model is proposed to compensate for the tracking errors. Taking a real-world multi-product pipeline network in China as an example, the accuracy and efficiency of the proposed model are verified. The results suggested that the hybrid intelligent framework outperforms other comparative methods, with minimal tracking errors being 3.79 min Multi-product pipeline Batch tracking Hybrid intelligent framework Data-driven Modified model Du, Jian verfasserin aut Liang, Yongtu verfasserin aut Wang, Bohong verfasserin aut Li, Miao verfasserin aut Liao, Qi verfasserin aut Xu, Ning verfasserin aut Enthalten in Chemical engineering research and design Amsterdam : Elsevier, 1983 191, Seite 236-248 Online-Ressource (DE-627)312841965 (DE-600)2008006-2 (DE-576)090893190 1744-3563 nnns volume:191 pages:236-248 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 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_101 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_2008 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_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.10 Verfahrenstechnik: Allgemeines VZ AR 191 236-248 |
| spelling |
10.1016/j.cherd.2022.12.036 doi (DE-627)ELV06330189X (ELSEVIER)S0263-8762(22)00738-9 DE-627 ger DE-627 rda eng 540 660 VZ 58.10 bkl Zheng, Jianqin verfasserin aut Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To improve the economy and meet the demand for transporting different oil products, multi-product pipelines are utilized to transport multi-products in sequence in the same pipelines. It is fundamentally important for operators in stations to know the accurate location of the head of each batch interface, to swing the valve at a station, and deliver oil products with minimal contamination. However, it is difficult to determine the location of the batch interface accurately, due to the complex hydrothermal conditions and mixed oil segment. In this paper, a hybrid intelligent framework is proposed to track the real-time batch interface of multi-product pipelines. The batch injection judgment module is applied to determine whether there is a new product batch injected in the pipeline. Applying the upstream and downstream flowrate, the volume calculation model is proposed to track the real-time location of each batch interface. Considering the deviation between the estimated location and the actual location of the batch interface, a self-learning modified model is proposed to compensate for the tracking errors. Taking a real-world multi-product pipeline network in China as an example, the accuracy and efficiency of the proposed model are verified. The results suggested that the hybrid intelligent framework outperforms other comparative methods, with minimal tracking errors being 3.79 min Multi-product pipeline Batch tracking Hybrid intelligent framework Data-driven Modified model Du, Jian verfasserin aut Liang, Yongtu verfasserin aut Wang, Bohong verfasserin aut Li, Miao verfasserin aut Liao, Qi verfasserin aut Xu, Ning verfasserin aut Enthalten in Chemical engineering research and design Amsterdam : Elsevier, 1983 191, Seite 236-248 Online-Ressource (DE-627)312841965 (DE-600)2008006-2 (DE-576)090893190 1744-3563 nnns volume:191 pages:236-248 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 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_101 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_2008 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_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.10 Verfahrenstechnik: Allgemeines VZ AR 191 236-248 |
| allfields_unstemmed |
10.1016/j.cherd.2022.12.036 doi (DE-627)ELV06330189X (ELSEVIER)S0263-8762(22)00738-9 DE-627 ger DE-627 rda eng 540 660 VZ 58.10 bkl Zheng, Jianqin verfasserin aut Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To improve the economy and meet the demand for transporting different oil products, multi-product pipelines are utilized to transport multi-products in sequence in the same pipelines. It is fundamentally important for operators in stations to know the accurate location of the head of each batch interface, to swing the valve at a station, and deliver oil products with minimal contamination. However, it is difficult to determine the location of the batch interface accurately, due to the complex hydrothermal conditions and mixed oil segment. In this paper, a hybrid intelligent framework is proposed to track the real-time batch interface of multi-product pipelines. The batch injection judgment module is applied to determine whether there is a new product batch injected in the pipeline. Applying the upstream and downstream flowrate, the volume calculation model is proposed to track the real-time location of each batch interface. Considering the deviation between the estimated location and the actual location of the batch interface, a self-learning modified model is proposed to compensate for the tracking errors. Taking a real-world multi-product pipeline network in China as an example, the accuracy and efficiency of the proposed model are verified. The results suggested that the hybrid intelligent framework outperforms other comparative methods, with minimal tracking errors being 3.79 min Multi-product pipeline Batch tracking Hybrid intelligent framework Data-driven Modified model Du, Jian verfasserin aut Liang, Yongtu verfasserin aut Wang, Bohong verfasserin aut Li, Miao verfasserin aut Liao, Qi verfasserin aut Xu, Ning verfasserin aut Enthalten in Chemical engineering research and design Amsterdam : Elsevier, 1983 191, Seite 236-248 Online-Ressource (DE-627)312841965 (DE-600)2008006-2 (DE-576)090893190 1744-3563 nnns volume:191 pages:236-248 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 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_101 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_2008 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_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.10 Verfahrenstechnik: Allgemeines VZ AR 191 236-248 |
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10.1016/j.cherd.2022.12.036 doi (DE-627)ELV06330189X (ELSEVIER)S0263-8762(22)00738-9 DE-627 ger DE-627 rda eng 540 660 VZ 58.10 bkl Zheng, Jianqin verfasserin aut Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To improve the economy and meet the demand for transporting different oil products, multi-product pipelines are utilized to transport multi-products in sequence in the same pipelines. It is fundamentally important for operators in stations to know the accurate location of the head of each batch interface, to swing the valve at a station, and deliver oil products with minimal contamination. However, it is difficult to determine the location of the batch interface accurately, due to the complex hydrothermal conditions and mixed oil segment. In this paper, a hybrid intelligent framework is proposed to track the real-time batch interface of multi-product pipelines. The batch injection judgment module is applied to determine whether there is a new product batch injected in the pipeline. Applying the upstream and downstream flowrate, the volume calculation model is proposed to track the real-time location of each batch interface. Considering the deviation between the estimated location and the actual location of the batch interface, a self-learning modified model is proposed to compensate for the tracking errors. Taking a real-world multi-product pipeline network in China as an example, the accuracy and efficiency of the proposed model are verified. The results suggested that the hybrid intelligent framework outperforms other comparative methods, with minimal tracking errors being 3.79 min Multi-product pipeline Batch tracking Hybrid intelligent framework Data-driven Modified model Du, Jian verfasserin aut Liang, Yongtu verfasserin aut Wang, Bohong verfasserin aut Li, Miao verfasserin aut Liao, Qi verfasserin aut Xu, Ning verfasserin aut Enthalten in Chemical engineering research and design Amsterdam : Elsevier, 1983 191, Seite 236-248 Online-Ressource (DE-627)312841965 (DE-600)2008006-2 (DE-576)090893190 1744-3563 nnns volume:191 pages:236-248 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 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_101 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_2008 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_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.10 Verfahrenstechnik: Allgemeines VZ AR 191 236-248 |
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10.1016/j.cherd.2022.12.036 doi (DE-627)ELV06330189X (ELSEVIER)S0263-8762(22)00738-9 DE-627 ger DE-627 rda eng 540 660 VZ 58.10 bkl Zheng, Jianqin verfasserin aut Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To improve the economy and meet the demand for transporting different oil products, multi-product pipelines are utilized to transport multi-products in sequence in the same pipelines. It is fundamentally important for operators in stations to know the accurate location of the head of each batch interface, to swing the valve at a station, and deliver oil products with minimal contamination. However, it is difficult to determine the location of the batch interface accurately, due to the complex hydrothermal conditions and mixed oil segment. In this paper, a hybrid intelligent framework is proposed to track the real-time batch interface of multi-product pipelines. The batch injection judgment module is applied to determine whether there is a new product batch injected in the pipeline. Applying the upstream and downstream flowrate, the volume calculation model is proposed to track the real-time location of each batch interface. Considering the deviation between the estimated location and the actual location of the batch interface, a self-learning modified model is proposed to compensate for the tracking errors. Taking a real-world multi-product pipeline network in China as an example, the accuracy and efficiency of the proposed model are verified. The results suggested that the hybrid intelligent framework outperforms other comparative methods, with minimal tracking errors being 3.79 min Multi-product pipeline Batch tracking Hybrid intelligent framework Data-driven Modified model Du, Jian verfasserin aut Liang, Yongtu verfasserin aut Wang, Bohong verfasserin aut Li, Miao verfasserin aut Liao, Qi verfasserin aut Xu, Ning verfasserin aut Enthalten in Chemical engineering research and design Amsterdam : Elsevier, 1983 191, Seite 236-248 Online-Ressource (DE-627)312841965 (DE-600)2008006-2 (DE-576)090893190 1744-3563 nnns volume:191 pages:236-248 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 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_101 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_2008 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_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.10 Verfahrenstechnik: Allgemeines VZ AR 191 236-248 |
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Zheng, Jianqin @@aut@@ Du, Jian @@aut@@ Liang, Yongtu @@aut@@ Wang, Bohong @@aut@@ Li, Miao @@aut@@ Liao, Qi @@aut@@ Xu, Ning @@aut@@ |
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Zheng, Jianqin |
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540 660 VZ 58.10 bkl Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines Multi-product pipeline Batch tracking Hybrid intelligent framework Data-driven Modified model |
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Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines |
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Zheng, Jianqin Du, Jian Liang, Yongtu Wang, Bohong Li, Miao Liao, Qi Xu, Ning |
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deeppipe: a hybrid intelligent framework for real-time batch tracking of multi-product pipelines |
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Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines |
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To improve the economy and meet the demand for transporting different oil products, multi-product pipelines are utilized to transport multi-products in sequence in the same pipelines. It is fundamentally important for operators in stations to know the accurate location of the head of each batch interface, to swing the valve at a station, and deliver oil products with minimal contamination. However, it is difficult to determine the location of the batch interface accurately, due to the complex hydrothermal conditions and mixed oil segment. In this paper, a hybrid intelligent framework is proposed to track the real-time batch interface of multi-product pipelines. The batch injection judgment module is applied to determine whether there is a new product batch injected in the pipeline. Applying the upstream and downstream flowrate, the volume calculation model is proposed to track the real-time location of each batch interface. Considering the deviation between the estimated location and the actual location of the batch interface, a self-learning modified model is proposed to compensate for the tracking errors. Taking a real-world multi-product pipeline network in China as an example, the accuracy and efficiency of the proposed model are verified. The results suggested that the hybrid intelligent framework outperforms other comparative methods, with minimal tracking errors being 3.79 min |
| abstractGer |
To improve the economy and meet the demand for transporting different oil products, multi-product pipelines are utilized to transport multi-products in sequence in the same pipelines. It is fundamentally important for operators in stations to know the accurate location of the head of each batch interface, to swing the valve at a station, and deliver oil products with minimal contamination. However, it is difficult to determine the location of the batch interface accurately, due to the complex hydrothermal conditions and mixed oil segment. In this paper, a hybrid intelligent framework is proposed to track the real-time batch interface of multi-product pipelines. The batch injection judgment module is applied to determine whether there is a new product batch injected in the pipeline. Applying the upstream and downstream flowrate, the volume calculation model is proposed to track the real-time location of each batch interface. Considering the deviation between the estimated location and the actual location of the batch interface, a self-learning modified model is proposed to compensate for the tracking errors. Taking a real-world multi-product pipeline network in China as an example, the accuracy and efficiency of the proposed model are verified. The results suggested that the hybrid intelligent framework outperforms other comparative methods, with minimal tracking errors being 3.79 min |
| abstract_unstemmed |
To improve the economy and meet the demand for transporting different oil products, multi-product pipelines are utilized to transport multi-products in sequence in the same pipelines. It is fundamentally important for operators in stations to know the accurate location of the head of each batch interface, to swing the valve at a station, and deliver oil products with minimal contamination. However, it is difficult to determine the location of the batch interface accurately, due to the complex hydrothermal conditions and mixed oil segment. In this paper, a hybrid intelligent framework is proposed to track the real-time batch interface of multi-product pipelines. The batch injection judgment module is applied to determine whether there is a new product batch injected in the pipeline. Applying the upstream and downstream flowrate, the volume calculation model is proposed to track the real-time location of each batch interface. Considering the deviation between the estimated location and the actual location of the batch interface, a self-learning modified model is proposed to compensate for the tracking errors. Taking a real-world multi-product pipeline network in China as an example, the accuracy and efficiency of the proposed model are verified. The results suggested that the hybrid intelligent framework outperforms other comparative methods, with minimal tracking errors being 3.79 min |
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Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines |
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