Total plant performance evaluation based on big data: Visualization analysis of TE process
The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assess...
Ausführliche Beschreibung
Autor*in: |
Li, Mengyao [verfasserIn] Du, Wenli [verfasserIn] Qian, Feng [verfasserIn] Zhong, Weiming [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2018 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
Enthalten in: Chinese journal of chemical engineering - [S.l.] : Elsevier Science, 1993, 26, Seite 1736-1749 |
---|---|
Übergeordnetes Werk: |
volume:26 ; pages:1736-1749 |
DOI / URN: |
10.1016/j.cjche.2018.06.009 |
---|
Katalog-ID: |
ELV000536687 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | ELV000536687 | ||
003 | DE-627 | ||
005 | 20230524155217.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230427s2018 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1016/j.cjche.2018.06.009 |2 doi | |
035 | |a (DE-627)ELV000536687 | ||
035 | |a (ELSEVIER)S1004-9541(17)31318-6 | ||
040 | |a DE-627 |b ger |c DE-627 |e rda | ||
041 | |a eng | ||
082 | 0 | 4 | |a 660 |q DE-600 |
084 | |a 6,25 |2 ssgn | ||
084 | |a ASIEN |q DE-1a |2 fid | ||
084 | |a 58.10 |2 bkl | ||
100 | 1 | |a Li, Mengyao |e verfasserin |4 aut | |
245 | 1 | 0 | |a Total plant performance evaluation based on big data: Visualization analysis of TE process |
264 | 1 | |c 2018 | |
336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classification-visualization method based on kernel principal component analysis (KPCA) and self-organizing map (SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram. | ||
650 | 4 | |a Performance evaluation | |
650 | 4 | |a Structured logic rules | |
650 | 4 | |a Hierarchical framework | |
650 | 4 | |a Multidimensional visualization | |
650 | 4 | |a KPCA–SOM | |
700 | 1 | |a Du, Wenli |e verfasserin |4 aut | |
700 | 1 | |a Qian, Feng |e verfasserin |4 aut | |
700 | 1 | |a Zhong, Weiming |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Chinese journal of chemical engineering |d [S.l.] : Elsevier Science, 1993 |g 26, Seite 1736-1749 |h Online-Ressource |w (DE-627)271598441 |w (DE-600)1480835-3 |w (DE-576)284926248 |x 2210-321X |7 nnns |
773 | 1 | 8 | |g volume:26 |g pages:1736-1749 |
912 | |a GBV_USEFLAG_U | ||
912 | |a SYSFLAG_U | ||
912 | |a GBV_ELV | ||
912 | |a FID-ASIEN | ||
912 | |a SSG-OLC-PHA | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_32 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_74 | ||
912 | |a GBV_ILN_90 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_100 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_150 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_224 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_702 | ||
912 | |a GBV_ILN_2001 | ||
912 | |a GBV_ILN_2003 | ||
912 | |a GBV_ILN_2004 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2006 | ||
912 | |a GBV_ILN_2008 | ||
912 | |a GBV_ILN_2011 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2015 | ||
912 | |a GBV_ILN_2020 | ||
912 | |a GBV_ILN_2021 | ||
912 | |a GBV_ILN_2025 | ||
912 | |a GBV_ILN_2026 | ||
912 | |a GBV_ILN_2027 | ||
912 | |a GBV_ILN_2034 | ||
912 | |a GBV_ILN_2038 | ||
912 | |a GBV_ILN_2044 | ||
912 | |a GBV_ILN_2048 | ||
912 | |a GBV_ILN_2049 | ||
912 | |a GBV_ILN_2050 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2056 | ||
912 | |a GBV_ILN_2059 | ||
912 | |a GBV_ILN_2061 | ||
912 | |a GBV_ILN_2064 | ||
912 | |a GBV_ILN_2065 | ||
912 | |a GBV_ILN_2068 | ||
912 | |a GBV_ILN_2088 | ||
912 | |a GBV_ILN_2111 | ||
912 | |a GBV_ILN_2112 | ||
912 | |a GBV_ILN_2113 | ||
912 | |a GBV_ILN_2118 | ||
912 | |a GBV_ILN_2122 | ||
912 | |a GBV_ILN_2129 | ||
912 | |a GBV_ILN_2143 | ||
912 | |a GBV_ILN_2147 | ||
912 | |a GBV_ILN_2148 | ||
912 | |a GBV_ILN_2152 | ||
912 | |a GBV_ILN_2153 | ||
912 | |a GBV_ILN_2190 | ||
912 | |a GBV_ILN_2232 | ||
912 | |a GBV_ILN_2336 | ||
912 | |a GBV_ILN_2470 | ||
912 | |a GBV_ILN_2507 | ||
912 | |a GBV_ILN_2522 | ||
912 | |a GBV_ILN_4035 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4046 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4242 | ||
912 | |a GBV_ILN_4251 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4326 | ||
912 | |a GBV_ILN_4333 | ||
912 | |a GBV_ILN_4334 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4393 | ||
936 | b | k | |a 58.10 |j Verfahrenstechnik: Allgemeines |
951 | |a AR | ||
952 | |d 26 |h 1736-1749 |
author_variant |
m l ml w d wd f q fq w z wz |
---|---|
matchkey_str |
article:2210321X:2018----::oapatefraceautobsdnidtvsaiai |
hierarchy_sort_str |
2018 |
bklnumber |
58.10 |
publishDate |
2018 |
allfields |
10.1016/j.cjche.2018.06.009 doi (DE-627)ELV000536687 (ELSEVIER)S1004-9541(17)31318-6 DE-627 ger DE-627 rda eng 660 DE-600 6,25 ssgn ASIEN DE-1a fid 58.10 bkl Li, Mengyao verfasserin aut Total plant performance evaluation based on big data: Visualization analysis of TE process 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classification-visualization method based on kernel principal component analysis (KPCA) and self-organizing map (SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram. Performance evaluation Structured logic rules Hierarchical framework Multidimensional visualization KPCA–SOM Du, Wenli verfasserin aut Qian, Feng verfasserin aut Zhong, Weiming verfasserin aut Enthalten in Chinese journal of chemical engineering [S.l.] : Elsevier Science, 1993 26, Seite 1736-1749 Online-Ressource (DE-627)271598441 (DE-600)1480835-3 (DE-576)284926248 2210-321X nnns volume:26 pages:1736-1749 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-ASIEN 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2038 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 58.10 Verfahrenstechnik: Allgemeines AR 26 1736-1749 |
spelling |
10.1016/j.cjche.2018.06.009 doi (DE-627)ELV000536687 (ELSEVIER)S1004-9541(17)31318-6 DE-627 ger DE-627 rda eng 660 DE-600 6,25 ssgn ASIEN DE-1a fid 58.10 bkl Li, Mengyao verfasserin aut Total plant performance evaluation based on big data: Visualization analysis of TE process 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classification-visualization method based on kernel principal component analysis (KPCA) and self-organizing map (SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram. Performance evaluation Structured logic rules Hierarchical framework Multidimensional visualization KPCA–SOM Du, Wenli verfasserin aut Qian, Feng verfasserin aut Zhong, Weiming verfasserin aut Enthalten in Chinese journal of chemical engineering [S.l.] : Elsevier Science, 1993 26, Seite 1736-1749 Online-Ressource (DE-627)271598441 (DE-600)1480835-3 (DE-576)284926248 2210-321X nnns volume:26 pages:1736-1749 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-ASIEN 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2038 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 58.10 Verfahrenstechnik: Allgemeines AR 26 1736-1749 |
allfields_unstemmed |
10.1016/j.cjche.2018.06.009 doi (DE-627)ELV000536687 (ELSEVIER)S1004-9541(17)31318-6 DE-627 ger DE-627 rda eng 660 DE-600 6,25 ssgn ASIEN DE-1a fid 58.10 bkl Li, Mengyao verfasserin aut Total plant performance evaluation based on big data: Visualization analysis of TE process 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classification-visualization method based on kernel principal component analysis (KPCA) and self-organizing map (SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram. Performance evaluation Structured logic rules Hierarchical framework Multidimensional visualization KPCA–SOM Du, Wenli verfasserin aut Qian, Feng verfasserin aut Zhong, Weiming verfasserin aut Enthalten in Chinese journal of chemical engineering [S.l.] : Elsevier Science, 1993 26, Seite 1736-1749 Online-Ressource (DE-627)271598441 (DE-600)1480835-3 (DE-576)284926248 2210-321X nnns volume:26 pages:1736-1749 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-ASIEN 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2038 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 58.10 Verfahrenstechnik: Allgemeines AR 26 1736-1749 |
allfieldsGer |
10.1016/j.cjche.2018.06.009 doi (DE-627)ELV000536687 (ELSEVIER)S1004-9541(17)31318-6 DE-627 ger DE-627 rda eng 660 DE-600 6,25 ssgn ASIEN DE-1a fid 58.10 bkl Li, Mengyao verfasserin aut Total plant performance evaluation based on big data: Visualization analysis of TE process 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classification-visualization method based on kernel principal component analysis (KPCA) and self-organizing map (SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram. Performance evaluation Structured logic rules Hierarchical framework Multidimensional visualization KPCA–SOM Du, Wenli verfasserin aut Qian, Feng verfasserin aut Zhong, Weiming verfasserin aut Enthalten in Chinese journal of chemical engineering [S.l.] : Elsevier Science, 1993 26, Seite 1736-1749 Online-Ressource (DE-627)271598441 (DE-600)1480835-3 (DE-576)284926248 2210-321X nnns volume:26 pages:1736-1749 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-ASIEN 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2038 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 58.10 Verfahrenstechnik: Allgemeines AR 26 1736-1749 |
allfieldsSound |
10.1016/j.cjche.2018.06.009 doi (DE-627)ELV000536687 (ELSEVIER)S1004-9541(17)31318-6 DE-627 ger DE-627 rda eng 660 DE-600 6,25 ssgn ASIEN DE-1a fid 58.10 bkl Li, Mengyao verfasserin aut Total plant performance evaluation based on big data: Visualization analysis of TE process 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classification-visualization method based on kernel principal component analysis (KPCA) and self-organizing map (SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram. Performance evaluation Structured logic rules Hierarchical framework Multidimensional visualization KPCA–SOM Du, Wenli verfasserin aut Qian, Feng verfasserin aut Zhong, Weiming verfasserin aut Enthalten in Chinese journal of chemical engineering [S.l.] : Elsevier Science, 1993 26, Seite 1736-1749 Online-Ressource (DE-627)271598441 (DE-600)1480835-3 (DE-576)284926248 2210-321X nnns volume:26 pages:1736-1749 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-ASIEN 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2038 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 58.10 Verfahrenstechnik: Allgemeines AR 26 1736-1749 |
language |
English |
source |
Enthalten in Chinese journal of chemical engineering 26, Seite 1736-1749 volume:26 pages:1736-1749 |
sourceStr |
Enthalten in Chinese journal of chemical engineering 26, Seite 1736-1749 volume:26 pages:1736-1749 |
format_phy_str_mv |
Article |
bklname |
Verfahrenstechnik: Allgemeines |
institution |
findex.gbv.de |
topic_facet |
Performance evaluation Structured logic rules Hierarchical framework Multidimensional visualization KPCA–SOM |
dewey-raw |
660 |
isfreeaccess_bool |
false |
container_title |
Chinese journal of chemical engineering |
authorswithroles_txt_mv |
Li, Mengyao @@aut@@ Du, Wenli @@aut@@ Qian, Feng @@aut@@ Zhong, Weiming @@aut@@ |
publishDateDaySort_date |
2018-01-01T00:00:00Z |
hierarchy_top_id |
271598441 |
dewey-sort |
3660 |
id |
ELV000536687 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV000536687</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524155217.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230427s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.cjche.2018.06.009</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV000536687</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1004-9541(17)31318-6</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">660</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">6,25</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ASIEN</subfield><subfield code="q">DE-1a</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">58.10</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Mengyao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Total plant performance evaluation based on big data: Visualization analysis of TE process</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classification-visualization method based on kernel principal component analysis (KPCA) and self-organizing map (SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Performance evaluation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Structured logic rules</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hierarchical framework</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multidimensional visualization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">KPCA–SOM</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Du, Wenli</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qian, Feng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhong, Weiming</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Chinese journal of chemical engineering</subfield><subfield code="d">[S.l.] : Elsevier Science, 1993</subfield><subfield code="g">26, Seite 1736-1749</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)271598441</subfield><subfield code="w">(DE-600)1480835-3</subfield><subfield code="w">(DE-576)284926248</subfield><subfield code="x">2210-321X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:26</subfield><subfield code="g">pages:1736-1749</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-ASIEN</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">58.10</subfield><subfield code="j">Verfahrenstechnik: Allgemeines</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">26</subfield><subfield code="h">1736-1749</subfield></datafield></record></collection>
|
author |
Li, Mengyao |
spellingShingle |
Li, Mengyao ddc 660 ssgn 6,25 fid ASIEN bkl 58.10 misc Performance evaluation misc Structured logic rules misc Hierarchical framework misc Multidimensional visualization misc KPCA–SOM Total plant performance evaluation based on big data: Visualization analysis of TE process |
authorStr |
Li, Mengyao |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)271598441 |
format |
electronic Article |
dewey-ones |
660 - Chemical engineering |
delete_txt_mv |
keep |
author_role |
aut aut aut aut |
collection |
elsevier |
remote_str |
true |
illustrated |
Not Illustrated |
issn |
2210-321X |
topic_title |
660 DE-600 6,25 ssgn ASIEN DE-1a fid 58.10 bkl Total plant performance evaluation based on big data: Visualization analysis of TE process Performance evaluation Structured logic rules Hierarchical framework Multidimensional visualization KPCA–SOM |
topic |
ddc 660 ssgn 6,25 fid ASIEN bkl 58.10 misc Performance evaluation misc Structured logic rules misc Hierarchical framework misc Multidimensional visualization misc KPCA–SOM |
topic_unstemmed |
ddc 660 ssgn 6,25 fid ASIEN bkl 58.10 misc Performance evaluation misc Structured logic rules misc Hierarchical framework misc Multidimensional visualization misc KPCA–SOM |
topic_browse |
ddc 660 ssgn 6,25 fid ASIEN bkl 58.10 misc Performance evaluation misc Structured logic rules misc Hierarchical framework misc Multidimensional visualization misc KPCA–SOM |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Chinese journal of chemical engineering |
hierarchy_parent_id |
271598441 |
dewey-tens |
660 - Chemical engineering |
hierarchy_top_title |
Chinese journal of chemical engineering |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)271598441 (DE-600)1480835-3 (DE-576)284926248 |
title |
Total plant performance evaluation based on big data: Visualization analysis of TE process |
ctrlnum |
(DE-627)ELV000536687 (ELSEVIER)S1004-9541(17)31318-6 |
title_full |
Total plant performance evaluation based on big data: Visualization analysis of TE process |
author_sort |
Li, Mengyao |
journal |
Chinese journal of chemical engineering |
journalStr |
Chinese journal of chemical engineering |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
600 - Technology |
recordtype |
marc |
publishDateSort |
2018 |
contenttype_str_mv |
zzz |
container_start_page |
1736 |
author_browse |
Li, Mengyao Du, Wenli Qian, Feng Zhong, Weiming |
container_volume |
26 |
class |
660 DE-600 6,25 ssgn ASIEN DE-1a fid 58.10 bkl |
format_se |
Elektronische Aufsätze |
author-letter |
Li, Mengyao |
doi_str_mv |
10.1016/j.cjche.2018.06.009 |
dewey-full |
660 |
author2-role |
verfasserin |
title_sort |
total plant performance evaluation based on big data: visualization analysis of te process |
title_auth |
Total plant performance evaluation based on big data: Visualization analysis of TE process |
abstract |
The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classification-visualization method based on kernel principal component analysis (KPCA) and self-organizing map (SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram. |
abstractGer |
The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classification-visualization method based on kernel principal component analysis (KPCA) and self-organizing map (SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram. |
abstract_unstemmed |
The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classification-visualization method based on kernel principal component analysis (KPCA) and self-organizing map (SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram. |
collection_details |
GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-ASIEN 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2038 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 |
title_short |
Total plant performance evaluation based on big data: Visualization analysis of TE process |
remote_bool |
true |
author2 |
Du, Wenli Qian, Feng Zhong, Weiming |
author2Str |
Du, Wenli Qian, Feng Zhong, Weiming |
ppnlink |
271598441 |
mediatype_str_mv |
c |
isOA_txt |
false |
hochschulschrift_bool |
false |
doi_str |
10.1016/j.cjche.2018.06.009 |
up_date |
2024-07-06T18:18:30.130Z |
_version_ |
1803854712806572032 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV000536687</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524155217.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230427s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.cjche.2018.06.009</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV000536687</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1004-9541(17)31318-6</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">660</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">6,25</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ASIEN</subfield><subfield code="q">DE-1a</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">58.10</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Mengyao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Total plant performance evaluation based on big data: Visualization analysis of TE process</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classification-visualization method based on kernel principal component analysis (KPCA) and self-organizing map (SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Performance evaluation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Structured logic rules</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hierarchical framework</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multidimensional visualization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">KPCA–SOM</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Du, Wenli</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qian, Feng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhong, Weiming</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Chinese journal of chemical engineering</subfield><subfield code="d">[S.l.] : Elsevier Science, 1993</subfield><subfield code="g">26, Seite 1736-1749</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)271598441</subfield><subfield code="w">(DE-600)1480835-3</subfield><subfield code="w">(DE-576)284926248</subfield><subfield code="x">2210-321X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:26</subfield><subfield code="g">pages:1736-1749</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-ASIEN</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">58.10</subfield><subfield code="j">Verfahrenstechnik: Allgemeines</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">26</subfield><subfield code="h">1736-1749</subfield></datafield></record></collection>
|
score |
7.40088 |