Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine

This work aims to investigate the application of a comprehensive nonlinear model-based predictive control strategy as a means to avoid unsafe or inappropriate operation of a gas turbine. Herein, the nonlinear model-based predictive control is employed to control compressor speed by varying the fuel...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Thiago S. Pires [verfasserIn] Manuel E. Cruz [verfasserIn] Marcelo J. Colaco [verfasserIn] Marco A. C. Alves [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Nonlinear model-based predictive control Gas turbine Process simulator Optimization Fuel consumption Load rejection Transient operation.

Übergeordnetes Werk:	In: Journal of Sustainable Development of Energy, Water and Environment Systems - SDEWES Centre, 2013, 6(2018), 4, Seite 770-783
Übergeordnetes Werk:	volume:6 ; year:2018 ; number:4 ; pages:770-783

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.13044/j.sdewes.d6.0220

Katalog-ID:	DOAJ018822967

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ018822967
003	DE-627
005	20230310103112.0
007	cr uuu---uuuuu
008	230226s2018 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.13044/j.sdewes.d6.0220 \|2 doi
035			\|a (DE-627)DOAJ018822967
035			\|a (DE-599)DOAJ376056b12e394a09b163557beb0e3d57
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a HD72-88
100	0		\|a Thiago S. Pires \|e verfasserin \|4 aut
245	1	0	\|a Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine
264		1	\|c 2018
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a This work aims to investigate the application of a comprehensive nonlinear model-based predictive control strategy as a means to avoid unsafe or inappropriate operation of a gas turbine. Herein, the nonlinear model-based predictive control is employed to control compressor speed by varying the fuel flow in the combustion chamber. The methodology complies with the gas turbine constraints explicitly in the optimization procedure and, therefore, the nonlinear model-based predictive control algorithm ensures that process constraints are not violated. The nonlinear dynamic behaviour of the gas turbine is modelled with the aid of a first principle process simulator, which solves the equations of state and the conservation equations of mass, energy and momentum. The optimization procedure is achieved through the implementation of an evolutionary algorithm. Three scenarios are simulated: fuel consumption optimization, load removal/addition and load rejection. The proposed control strategy is successfully applied to both transient and steady-state operational modes of the gas turbine.
650		4	\|a Nonlinear model-based predictive control
650		4	\|a Gas turbine
650		4	\|a Process simulator
650		4	\|a Optimization
650		4	\|a Fuel consumption
650		4	\|a Load rejection
650		4	\|a Transient operation.
653		0	\|a Technology
653		0	\|a T
653		0	\|a Economic growth, development, planning
700	0		\|a Manuel E. Cruz \|e verfasserin \|4 aut
700	0		\|a Marcelo J. Colaco \|e verfasserin \|4 aut
700	0		\|a Marco A. C. Alves \|e verfasserin \|4 aut
773	0	8	\|i In \|t Journal of Sustainable Development of Energy, Water and Environment Systems \|d SDEWES Centre, 2013 \|g 6(2018), 4, Seite 770-783 \|w (DE-627)766477541 \|w (DE-600)2730778-5 \|x 18489257 \|7 nnns
773	1	8	\|g volume:6 \|g year:2018 \|g number:4 \|g pages:770-783
856	4	0	\|u https://doi.org/10.13044/j.sdewes.d6.0220 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/376056b12e394a09b163557beb0e3d57 \|z kostenfrei
856	4	0	\|u http://www.sdewes.org/jsdewes/pid6.0220 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1848-9257 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
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_95
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 6 \|j 2018 \|e 4 \|h 770-783

Indexfelder

author_variant	t s p tsp m e c mec m j c mjc m a c a maca
matchkey_str	article:18489257:2018----::olnamdlrdcieotoapidornino
hierarchy_sort_str	2018
callnumber-subject-code	HD
publishDate	2018
allfields	10.13044/j.sdewes.d6.0220 doi (DE-627)DOAJ018822967 (DE-599)DOAJ376056b12e394a09b163557beb0e3d57 DE-627 ger DE-627 rakwb eng HD72-88 Thiago S. Pires verfasserin aut Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work aims to investigate the application of a comprehensive nonlinear model-based predictive control strategy as a means to avoid unsafe or inappropriate operation of a gas turbine. Herein, the nonlinear model-based predictive control is employed to control compressor speed by varying the fuel flow in the combustion chamber. The methodology complies with the gas turbine constraints explicitly in the optimization procedure and, therefore, the nonlinear model-based predictive control algorithm ensures that process constraints are not violated. The nonlinear dynamic behaviour of the gas turbine is modelled with the aid of a first principle process simulator, which solves the equations of state and the conservation equations of mass, energy and momentum. The optimization procedure is achieved through the implementation of an evolutionary algorithm. Three scenarios are simulated: fuel consumption optimization, load removal/addition and load rejection. The proposed control strategy is successfully applied to both transient and steady-state operational modes of the gas turbine. Nonlinear model-based predictive control Gas turbine Process simulator Optimization Fuel consumption Load rejection Transient operation. Technology T Economic growth, development, planning Manuel E. Cruz verfasserin aut Marcelo J. Colaco verfasserin aut Marco A. C. Alves verfasserin aut In Journal of Sustainable Development of Energy, Water and Environment Systems SDEWES Centre, 2013 6(2018), 4, Seite 770-783 (DE-627)766477541 (DE-600)2730778-5 18489257 nnns volume:6 year:2018 number:4 pages:770-783 https://doi.org/10.13044/j.sdewes.d6.0220 kostenfrei https://doaj.org/article/376056b12e394a09b163557beb0e3d57 kostenfrei http://www.sdewes.org/jsdewes/pid6.0220 kostenfrei https://doaj.org/toc/1848-9257 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 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_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 4 770-783
spelling	10.13044/j.sdewes.d6.0220 doi (DE-627)DOAJ018822967 (DE-599)DOAJ376056b12e394a09b163557beb0e3d57 DE-627 ger DE-627 rakwb eng HD72-88 Thiago S. Pires verfasserin aut Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work aims to investigate the application of a comprehensive nonlinear model-based predictive control strategy as a means to avoid unsafe or inappropriate operation of a gas turbine. Herein, the nonlinear model-based predictive control is employed to control compressor speed by varying the fuel flow in the combustion chamber. The methodology complies with the gas turbine constraints explicitly in the optimization procedure and, therefore, the nonlinear model-based predictive control algorithm ensures that process constraints are not violated. The nonlinear dynamic behaviour of the gas turbine is modelled with the aid of a first principle process simulator, which solves the equations of state and the conservation equations of mass, energy and momentum. The optimization procedure is achieved through the implementation of an evolutionary algorithm. Three scenarios are simulated: fuel consumption optimization, load removal/addition and load rejection. The proposed control strategy is successfully applied to both transient and steady-state operational modes of the gas turbine. Nonlinear model-based predictive control Gas turbine Process simulator Optimization Fuel consumption Load rejection Transient operation. Technology T Economic growth, development, planning Manuel E. Cruz verfasserin aut Marcelo J. Colaco verfasserin aut Marco A. C. Alves verfasserin aut In Journal of Sustainable Development of Energy, Water and Environment Systems SDEWES Centre, 2013 6(2018), 4, Seite 770-783 (DE-627)766477541 (DE-600)2730778-5 18489257 nnns volume:6 year:2018 number:4 pages:770-783 https://doi.org/10.13044/j.sdewes.d6.0220 kostenfrei https://doaj.org/article/376056b12e394a09b163557beb0e3d57 kostenfrei http://www.sdewes.org/jsdewes/pid6.0220 kostenfrei https://doaj.org/toc/1848-9257 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 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_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 4 770-783
allfields_unstemmed	10.13044/j.sdewes.d6.0220 doi (DE-627)DOAJ018822967 (DE-599)DOAJ376056b12e394a09b163557beb0e3d57 DE-627 ger DE-627 rakwb eng HD72-88 Thiago S. Pires verfasserin aut Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work aims to investigate the application of a comprehensive nonlinear model-based predictive control strategy as a means to avoid unsafe or inappropriate operation of a gas turbine. Herein, the nonlinear model-based predictive control is employed to control compressor speed by varying the fuel flow in the combustion chamber. The methodology complies with the gas turbine constraints explicitly in the optimization procedure and, therefore, the nonlinear model-based predictive control algorithm ensures that process constraints are not violated. The nonlinear dynamic behaviour of the gas turbine is modelled with the aid of a first principle process simulator, which solves the equations of state and the conservation equations of mass, energy and momentum. The optimization procedure is achieved through the implementation of an evolutionary algorithm. Three scenarios are simulated: fuel consumption optimization, load removal/addition and load rejection. The proposed control strategy is successfully applied to both transient and steady-state operational modes of the gas turbine. Nonlinear model-based predictive control Gas turbine Process simulator Optimization Fuel consumption Load rejection Transient operation. Technology T Economic growth, development, planning Manuel E. Cruz verfasserin aut Marcelo J. Colaco verfasserin aut Marco A. C. Alves verfasserin aut In Journal of Sustainable Development of Energy, Water and Environment Systems SDEWES Centre, 2013 6(2018), 4, Seite 770-783 (DE-627)766477541 (DE-600)2730778-5 18489257 nnns volume:6 year:2018 number:4 pages:770-783 https://doi.org/10.13044/j.sdewes.d6.0220 kostenfrei https://doaj.org/article/376056b12e394a09b163557beb0e3d57 kostenfrei http://www.sdewes.org/jsdewes/pid6.0220 kostenfrei https://doaj.org/toc/1848-9257 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 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_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 4 770-783
allfieldsGer	10.13044/j.sdewes.d6.0220 doi (DE-627)DOAJ018822967 (DE-599)DOAJ376056b12e394a09b163557beb0e3d57 DE-627 ger DE-627 rakwb eng HD72-88 Thiago S. Pires verfasserin aut Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work aims to investigate the application of a comprehensive nonlinear model-based predictive control strategy as a means to avoid unsafe or inappropriate operation of a gas turbine. Herein, the nonlinear model-based predictive control is employed to control compressor speed by varying the fuel flow in the combustion chamber. The methodology complies with the gas turbine constraints explicitly in the optimization procedure and, therefore, the nonlinear model-based predictive control algorithm ensures that process constraints are not violated. The nonlinear dynamic behaviour of the gas turbine is modelled with the aid of a first principle process simulator, which solves the equations of state and the conservation equations of mass, energy and momentum. The optimization procedure is achieved through the implementation of an evolutionary algorithm. Three scenarios are simulated: fuel consumption optimization, load removal/addition and load rejection. The proposed control strategy is successfully applied to both transient and steady-state operational modes of the gas turbine. Nonlinear model-based predictive control Gas turbine Process simulator Optimization Fuel consumption Load rejection Transient operation. Technology T Economic growth, development, planning Manuel E. Cruz verfasserin aut Marcelo J. Colaco verfasserin aut Marco A. C. Alves verfasserin aut In Journal of Sustainable Development of Energy, Water and Environment Systems SDEWES Centre, 2013 6(2018), 4, Seite 770-783 (DE-627)766477541 (DE-600)2730778-5 18489257 nnns volume:6 year:2018 number:4 pages:770-783 https://doi.org/10.13044/j.sdewes.d6.0220 kostenfrei https://doaj.org/article/376056b12e394a09b163557beb0e3d57 kostenfrei http://www.sdewes.org/jsdewes/pid6.0220 kostenfrei https://doaj.org/toc/1848-9257 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 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_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 4 770-783
allfieldsSound	10.13044/j.sdewes.d6.0220 doi (DE-627)DOAJ018822967 (DE-599)DOAJ376056b12e394a09b163557beb0e3d57 DE-627 ger DE-627 rakwb eng HD72-88 Thiago S. Pires verfasserin aut Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work aims to investigate the application of a comprehensive nonlinear model-based predictive control strategy as a means to avoid unsafe or inappropriate operation of a gas turbine. Herein, the nonlinear model-based predictive control is employed to control compressor speed by varying the fuel flow in the combustion chamber. The methodology complies with the gas turbine constraints explicitly in the optimization procedure and, therefore, the nonlinear model-based predictive control algorithm ensures that process constraints are not violated. The nonlinear dynamic behaviour of the gas turbine is modelled with the aid of a first principle process simulator, which solves the equations of state and the conservation equations of mass, energy and momentum. The optimization procedure is achieved through the implementation of an evolutionary algorithm. Three scenarios are simulated: fuel consumption optimization, load removal/addition and load rejection. The proposed control strategy is successfully applied to both transient and steady-state operational modes of the gas turbine. Nonlinear model-based predictive control Gas turbine Process simulator Optimization Fuel consumption Load rejection Transient operation. Technology T Economic growth, development, planning Manuel E. Cruz verfasserin aut Marcelo J. Colaco verfasserin aut Marco A. C. Alves verfasserin aut In Journal of Sustainable Development of Energy, Water and Environment Systems SDEWES Centre, 2013 6(2018), 4, Seite 770-783 (DE-627)766477541 (DE-600)2730778-5 18489257 nnns volume:6 year:2018 number:4 pages:770-783 https://doi.org/10.13044/j.sdewes.d6.0220 kostenfrei https://doaj.org/article/376056b12e394a09b163557beb0e3d57 kostenfrei http://www.sdewes.org/jsdewes/pid6.0220 kostenfrei https://doaj.org/toc/1848-9257 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 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_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 4 770-783
language	English
source	In Journal of Sustainable Development of Energy, Water and Environment Systems 6(2018), 4, Seite 770-783 volume:6 year:2018 number:4 pages:770-783
sourceStr	In Journal of Sustainable Development of Energy, Water and Environment Systems 6(2018), 4, Seite 770-783 volume:6 year:2018 number:4 pages:770-783
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Nonlinear model-based predictive control Gas turbine Process simulator Optimization Fuel consumption Load rejection Transient operation. Technology T Economic growth, development, planning
isfreeaccess_bool	true
container_title	Journal of Sustainable Development of Energy, Water and Environment Systems
authorswithroles_txt_mv	Thiago S. Pires @@aut@@ Manuel E. Cruz @@aut@@ Marcelo J. Colaco @@aut@@ Marco A. C. Alves @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	766477541
id	DOAJ018822967
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">DOAJ018822967</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310103112.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2018 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.13044/j.sdewes.d6.0220</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ018822967</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ376056b12e394a09b163557beb0e3d57</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">HD72-88</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Thiago S. Pires</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</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">This work aims to investigate the application of a comprehensive nonlinear model-based predictive control strategy as a means to avoid unsafe or inappropriate operation of a gas turbine. Herein, the nonlinear model-based predictive control is employed to control compressor speed by varying the fuel flow in the combustion chamber. The methodology complies with the gas turbine constraints explicitly in the optimization procedure and, therefore, the nonlinear model-based predictive control algorithm ensures that process constraints are not violated. The nonlinear dynamic behaviour of the gas turbine is modelled with the aid of a first principle process simulator, which solves the equations of state and the conservation equations of mass, energy and momentum. The optimization procedure is achieved through the implementation of an evolutionary algorithm. Three scenarios are simulated: fuel consumption optimization, load removal/addition and load rejection. The proposed control strategy is successfully applied to both transient and steady-state operational modes of the gas turbine.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nonlinear model-based predictive control</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gas turbine</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Process simulator</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuel consumption</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Load rejection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Transient operation.</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Economic growth, development, planning</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Manuel E. Cruz</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Marcelo J. Colaco</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Marco A. C. Alves</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Journal of Sustainable Development of Energy, Water and Environment Systems</subfield><subfield code="d">SDEWES Centre, 2013</subfield><subfield code="g">6(2018), 4, Seite 770-783</subfield><subfield code="w">(DE-627)766477541</subfield><subfield code="w">(DE-600)2730778-5</subfield><subfield code="x">18489257</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:4</subfield><subfield code="g">pages:770-783</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.13044/j.sdewes.d6.0220</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/376056b12e394a09b163557beb0e3d57</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.sdewes.org/jsdewes/pid6.0220</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1848-9257</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</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_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</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_95</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_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</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_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</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_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_4249</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_4306</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_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">6</subfield><subfield code="j">2018</subfield><subfield code="e">4</subfield><subfield code="h">770-783</subfield></datafield></record></collection>
callnumber-first	H - Social Science
author	Thiago S. Pires
spellingShingle	Thiago S. Pires misc HD72-88 misc Nonlinear model-based predictive control misc Gas turbine misc Process simulator misc Optimization misc Fuel consumption misc Load rejection misc Transient operation. misc Technology misc T misc Economic growth, development, planning Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine
authorStr	Thiago S. Pires
ppnlink_with_tag_str_mv	@@773@@(DE-627)766477541
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	HD72-88
illustrated	Not Illustrated
issn	18489257
topic_title	HD72-88 Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine Nonlinear model-based predictive control Gas turbine Process simulator Optimization Fuel consumption Load rejection Transient operation
topic	misc HD72-88 misc Nonlinear model-based predictive control misc Gas turbine misc Process simulator misc Optimization misc Fuel consumption misc Load rejection misc Transient operation. misc Technology misc T misc Economic growth, development, planning
topic_unstemmed	misc HD72-88 misc Nonlinear model-based predictive control misc Gas turbine misc Process simulator misc Optimization misc Fuel consumption misc Load rejection misc Transient operation. misc Technology misc T misc Economic growth, development, planning
topic_browse	misc HD72-88 misc Nonlinear model-based predictive control misc Gas turbine misc Process simulator misc Optimization misc Fuel consumption misc Load rejection misc Transient operation. misc Technology misc T misc Economic growth, development, planning
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Journal of Sustainable Development of Energy, Water and Environment Systems
hierarchy_parent_id	766477541
hierarchy_top_title	Journal of Sustainable Development of Energy, Water and Environment Systems
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)766477541 (DE-600)2730778-5
title	Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine
ctrlnum	(DE-627)DOAJ018822967 (DE-599)DOAJ376056b12e394a09b163557beb0e3d57
title_full	Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine
author_sort	Thiago S. Pires
journal	Journal of Sustainable Development of Energy, Water and Environment Systems
journalStr	Journal of Sustainable Development of Energy, Water and Environment Systems
callnumber-first-code	H
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2018
contenttype_str_mv	txt
container_start_page	770
author_browse	Thiago S. Pires Manuel E. Cruz Marcelo J. Colaco Marco A. C. Alves
container_volume	6
class	HD72-88
format_se	Elektronische Aufsätze
author-letter	Thiago S. Pires
doi_str_mv	10.13044/j.sdewes.d6.0220
author2-role	verfasserin
title_sort	nonlinear model predictive control applied to transient operation of a gas turbine
callnumber	HD72-88
title_auth	Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine
abstract	This work aims to investigate the application of a comprehensive nonlinear model-based predictive control strategy as a means to avoid unsafe or inappropriate operation of a gas turbine. Herein, the nonlinear model-based predictive control is employed to control compressor speed by varying the fuel flow in the combustion chamber. The methodology complies with the gas turbine constraints explicitly in the optimization procedure and, therefore, the nonlinear model-based predictive control algorithm ensures that process constraints are not violated. The nonlinear dynamic behaviour of the gas turbine is modelled with the aid of a first principle process simulator, which solves the equations of state and the conservation equations of mass, energy and momentum. The optimization procedure is achieved through the implementation of an evolutionary algorithm. Three scenarios are simulated: fuel consumption optimization, load removal/addition and load rejection. The proposed control strategy is successfully applied to both transient and steady-state operational modes of the gas turbine.
abstractGer	This work aims to investigate the application of a comprehensive nonlinear model-based predictive control strategy as a means to avoid unsafe or inappropriate operation of a gas turbine. Herein, the nonlinear model-based predictive control is employed to control compressor speed by varying the fuel flow in the combustion chamber. The methodology complies with the gas turbine constraints explicitly in the optimization procedure and, therefore, the nonlinear model-based predictive control algorithm ensures that process constraints are not violated. The nonlinear dynamic behaviour of the gas turbine is modelled with the aid of a first principle process simulator, which solves the equations of state and the conservation equations of mass, energy and momentum. The optimization procedure is achieved through the implementation of an evolutionary algorithm. Three scenarios are simulated: fuel consumption optimization, load removal/addition and load rejection. The proposed control strategy is successfully applied to both transient and steady-state operational modes of the gas turbine.
abstract_unstemmed	This work aims to investigate the application of a comprehensive nonlinear model-based predictive control strategy as a means to avoid unsafe or inappropriate operation of a gas turbine. Herein, the nonlinear model-based predictive control is employed to control compressor speed by varying the fuel flow in the combustion chamber. The methodology complies with the gas turbine constraints explicitly in the optimization procedure and, therefore, the nonlinear model-based predictive control algorithm ensures that process constraints are not violated. The nonlinear dynamic behaviour of the gas turbine is modelled with the aid of a first principle process simulator, which solves the equations of state and the conservation equations of mass, energy and momentum. The optimization procedure is achieved through the implementation of an evolutionary algorithm. Three scenarios are simulated: fuel consumption optimization, load removal/addition and load rejection. The proposed control strategy is successfully applied to both transient and steady-state operational modes of the gas turbine.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 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_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700
container_issue	4
title_short	Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine
url	https://doi.org/10.13044/j.sdewes.d6.0220 https://doaj.org/article/376056b12e394a09b163557beb0e3d57 http://www.sdewes.org/jsdewes/pid6.0220 https://doaj.org/toc/1848-9257
remote_bool	true
author2	Manuel E. Cruz Marcelo J. Colaco Marco A. C. Alves
author2Str	Manuel E. Cruz Marcelo J. Colaco Marco A. C. Alves
ppnlink	766477541
callnumber-subject	HD - Industries, Land Use, Labor
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.13044/j.sdewes.d6.0220
callnumber-a	HD72-88
up_date	2024-07-03T20:12:10.925Z
_version_	1803590074057621504
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">DOAJ018822967</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310103112.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2018 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.13044/j.sdewes.d6.0220</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ018822967</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ376056b12e394a09b163557beb0e3d57</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">HD72-88</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Thiago S. Pires</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</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">This work aims to investigate the application of a comprehensive nonlinear model-based predictive control strategy as a means to avoid unsafe or inappropriate operation of a gas turbine. Herein, the nonlinear model-based predictive control is employed to control compressor speed by varying the fuel flow in the combustion chamber. The methodology complies with the gas turbine constraints explicitly in the optimization procedure and, therefore, the nonlinear model-based predictive control algorithm ensures that process constraints are not violated. The nonlinear dynamic behaviour of the gas turbine is modelled with the aid of a first principle process simulator, which solves the equations of state and the conservation equations of mass, energy and momentum. The optimization procedure is achieved through the implementation of an evolutionary algorithm. Three scenarios are simulated: fuel consumption optimization, load removal/addition and load rejection. The proposed control strategy is successfully applied to both transient and steady-state operational modes of the gas turbine.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nonlinear model-based predictive control</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gas turbine</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Process simulator</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuel consumption</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Load rejection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Transient operation.</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Economic growth, development, planning</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Manuel E. Cruz</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Marcelo J. Colaco</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Marco A. C. Alves</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Journal of Sustainable Development of Energy, Water and Environment Systems</subfield><subfield code="d">SDEWES Centre, 2013</subfield><subfield code="g">6(2018), 4, Seite 770-783</subfield><subfield code="w">(DE-627)766477541</subfield><subfield code="w">(DE-600)2730778-5</subfield><subfield code="x">18489257</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:4</subfield><subfield code="g">pages:770-783</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.13044/j.sdewes.d6.0220</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/376056b12e394a09b163557beb0e3d57</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.sdewes.org/jsdewes/pid6.0220</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1848-9257</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</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_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</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_95</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_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</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_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</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_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_4249</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_4306</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_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">6</subfield><subfield code="j">2018</subfield><subfield code="e">4</subfield><subfield code="h">770-783</subfield></datafield></record></collection>
score	7.4022093

Nicht das Richtige dabei?

Schreiben Sie uns!

Nonlinear Model Predictive Control applied to Transient Operation of a Gas Turbine

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?