001     877455
005     20240712112908.0
024 7 _ |a 10.1002/aic.16721
|2 doi
024 7 _ |a 0001-1541
|2 ISSN
024 7 _ |a 1547-5905
|2 ISSN
024 7 _ |a 2128/25037
|2 Handle
024 7 _ |a WOS:000478196000001
|2 WOS
037 _ _ |a FZJ-2020-02208
082 _ _ |a 660
100 1 _ |a Caspari, Adrian
|0 P:(DE-HGF)0
|b 0
245 _ _ |a A flexible air separation process: 2. Optimal operation using economic model predictive control
260 _ _ |a Hoboken, NJ
|c 2019
|b Wiley
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1591705181_25233
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a The penetration of renewable electricity promises an economic advantage for flexible operation of energy‐intense processes. One way to achieve flexible operation is economic model predictive control (eNMPC), where an economic dynamic optimization problem is directly solved at controller level taking into account a process model and operational constraints. We apply eNMPC in silico to an air separation process with an integrated liquefier and liquid‐assist operation. We use a mechanistic dynamic model as both controller model and plant surrogate. We conduct a closed‐loop case study over a time horizon of 2 days with historical electricity prices and input disturbances. We solve the dynamic optimization problems in DyOS. Compared to the optimal steady‐state operation, the eNMPC operating strategy gives a significant improvement of 14%. We further show that the eNMPC enables economic improvements similar to an idealized quasistationary scheduling. While the eNMPC provides control profiles qualitatively similar to those obtained from deterministic global optimization of quasistationary scheduling, the eNMPC satisfies the product purity constraints all the time whereas the quasistationary scheduling sometimes fails to do so. The eNMPC applies local optimization methods and achieves profiles similar to the scheduling solved using deterministic global optimization methods over the complete closed‐loop simulation time horizon.
536 _ _ |a 899 - ohne Topic (POF3-899)
|0 G:(DE-HGF)POF3-899
|c POF3-899
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Offermanns, Christoph
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Schäfer, Pascal
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Mhamdi, Adel
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Mitsos, Alexander
|0 P:(DE-Juel1)172025
|b 4
|e Corresponding author
|u fzj
773 _ _ |a 10.1002/aic.16721
|g Vol. 65, no. 11
|0 PERI:(DE-600)2020333-0
|n 11
|p e16721
|t AIChE journal
|v 65
|y 2019
|x 1547-5905
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/877455/files/aic.16721.pdf
856 4 _ |y OpenAccess
|x pdfa
|u https://juser.fz-juelich.de/record/877455/files/aic.16721.pdf?subformat=pdfa
909 C O |o oai:juser.fz-juelich.de:877455
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a RWTH Aachen
|0 I:(DE-588b)36225-6
|k RWTH
|b 0
|6 P:(DE-HGF)0
910 1 _ |a RWTH Aachen
|0 I:(DE-588b)36225-6
|k RWTH
|b 1
|6 P:(DE-HGF)0
910 1 _ |a RWTH Aachen
|0 I:(DE-588b)36225-6
|k RWTH
|b 2
|6 P:(DE-HGF)0
910 1 _ |a RWTH Aachen
|0 I:(DE-588b)36225-6
|k RWTH
|b 3
|6 P:(DE-HGF)0
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)172025
910 1 _ |a RWTH Aachen
|0 I:(DE-588b)36225-6
|k RWTH
|b 4
|6 P:(DE-Juel1)172025
913 1 _ |a DE-HGF
|b Programmungebundene Forschung
|l ohne Programm
|1 G:(DE-HGF)POF3-890
|0 G:(DE-HGF)POF3-899
|2 G:(DE-HGF)POF3-800
|v ohne Topic
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
914 1 _ |y 2020
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2020-02-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2020-02-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1160
|2 StatID
|b Current Contents - Engineering, Computing and Technology
|d 2020-02-26
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b AICHE J : 2018
|d 2020-02-26
915 _ _ |a DEAL Wiley
|0 StatID:(DE-HGF)3001
|2 StatID
|d 2020-02-26
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2020-02-26
915 _ _ |a WoS
|0 StatID:(DE-HGF)0110
|2 StatID
|b Science Citation Index
|d 2020-02-26
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
|d 2020-02-26
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2020-02-26
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2020-02-26
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2020-02-26
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2020-02-26
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IEK-10-20170217
|k IEK-10
|l Modellierung von Energiesystemen
|x 0
980 1 _ |a FullTexts
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)IEK-10-20170217
981 _ _ |a I:(DE-Juel1)ICE-1-20170217


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21