Diesel engine NOx emissions control: An advanced method for the O

In recent decades, the increasingly tight emissions regulations, along with the ever-increasing price of fuels and the request for more power from the engines, has pushed the world car industry to improve the performances of the applications of electronics, designed to control the internal combustio...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Mariani, F. [verfasserIn] Grimaldi, C.N. [verfasserIn] Battistoni, M. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Emissions Diesel engines Control Anfis model

Übergeordnetes Werk:	Enthalten in: Applied energy - Amsterdam [u.a.] : Elsevier Science, 1975, 113, Seite 576-588
Übergeordnetes Werk:	volume:113 ; pages:576-588

DOI / URN:	10.1016/j.apenergy.2013.07.067

Katalog-ID:	ELV001863339

Internformat


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245	1	0	\|a Diesel engine NOx emissions control: An advanced method for the O
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520			\|a In recent decades, the increasingly tight emissions regulations, along with the ever-increasing price of fuels and the request for more power from the engines, has pushed the world car industry to improve the performances of the applications of electronics, designed to control the internal combustion engines (ICE) and the pollutant emissions systems. At present, one of the main problems, in the development of diesel engines is represented by the achievement of an increasingly strict control on the systems used for the pollutant emission reduction. In particular, as far as NOx gas is concerned, EGR systems are mature and widely used, but increased efficiency in terms of emissions abatement, is necessary in order to determine as best possible the actual oxygen content in the charge at the engine intake manifold. The present work compares the ability of the ANN and Neuro-Fuzzy approach (ANFIS) to predict the volumetric oxygen concentration at the intake, using experimental data acquired on a compression ignition engine in transient operational conditions. In an off-line evaluation of results, both models show good predicting abilities; in particular the ANFIS model presents an absolute error value for the training and test phases respectively equal to 0.7 and 0.9 (as a percentage of 3.5% and 4.5%), while, the same evaluation obtained using the ANN-BP model provides 0.92 and 0.9 (as a percentage of 4.6% and 4.5%). The comparison shows that the ANFIS model produces more accurate solutions in less time, using linear rules that bind the input variables with the output. The linearity of the rules is a key feature to decrease the convergence time especially when the model is used for the on-board periodic training activity due to the aging of the engine.
650		4	\|a Emissions
650		4	\|a Diesel engines
650		4	\|a Control
650		4	\|a Anfis model
700	1		\|a Grimaldi, C.N. \|e verfasserin \|4 aut
700	1		\|a Battistoni, M. \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Applied energy \|d Amsterdam [u.a.] : Elsevier Science, 1975 \|g 113, Seite 576-588 \|h Online-Ressource \|w (DE-627)320406709 \|w (DE-600)2000772-3 \|w (DE-576)256140251 \|x 1872-9118 \|7 nnns
773	1	8	\|g volume:113 \|g pages:576-588
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912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_34
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_187
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
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_2010
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_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_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_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_4307
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 52.50 \|j Energietechnik: Allgemeines
951			\|a AR
952			\|d 113 \|h 576-588

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author_variant	f m fm c g cg m b mb
matchkey_str	article:18729118:2013----::islnieoeisosotoaavn
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publishDate	2013
allfields	10.1016/j.apenergy.2013.07.067 doi (DE-627)ELV001863339 (ELSEVIER)S0306-2619(13)00634-X DE-627 ger DE-627 rda eng 620 DE-600 52.50 bkl Mariani, F. verfasserin aut Diesel engine NOx emissions control: An advanced method for the O 2013 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent decades, the increasingly tight emissions regulations, along with the ever-increasing price of fuels and the request for more power from the engines, has pushed the world car industry to improve the performances of the applications of electronics, designed to control the internal combustion engines (ICE) and the pollutant emissions systems. At present, one of the main problems, in the development of diesel engines is represented by the achievement of an increasingly strict control on the systems used for the pollutant emission reduction. In particular, as far as NOx gas is concerned, EGR systems are mature and widely used, but increased efficiency in terms of emissions abatement, is necessary in order to determine as best possible the actual oxygen content in the charge at the engine intake manifold. The present work compares the ability of the ANN and Neuro-Fuzzy approach (ANFIS) to predict the volumetric oxygen concentration at the intake, using experimental data acquired on a compression ignition engine in transient operational conditions. In an off-line evaluation of results, both models show good predicting abilities; in particular the ANFIS model presents an absolute error value for the training and test phases respectively equal to 0.7 and 0.9 (as a percentage of 3.5% and 4.5%), while, the same evaluation obtained using the ANN-BP model provides 0.92 and 0.9 (as a percentage of 4.6% and 4.5%). The comparison shows that the ANFIS model produces more accurate solutions in less time, using linear rules that bind the input variables with the output. The linearity of the rules is a key feature to decrease the convergence time especially when the model is used for the on-board periodic training activity due to the aging of the engine. Emissions Diesel engines Control Anfis model Grimaldi, C.N. verfasserin aut Battistoni, M. verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 113, Seite 576-588 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:113 pages:576-588 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 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_187 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 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_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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.50 Energietechnik: Allgemeines AR 113 576-588
spelling	10.1016/j.apenergy.2013.07.067 doi (DE-627)ELV001863339 (ELSEVIER)S0306-2619(13)00634-X DE-627 ger DE-627 rda eng 620 DE-600 52.50 bkl Mariani, F. verfasserin aut Diesel engine NOx emissions control: An advanced method for the O 2013 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent decades, the increasingly tight emissions regulations, along with the ever-increasing price of fuels and the request for more power from the engines, has pushed the world car industry to improve the performances of the applications of electronics, designed to control the internal combustion engines (ICE) and the pollutant emissions systems. At present, one of the main problems, in the development of diesel engines is represented by the achievement of an increasingly strict control on the systems used for the pollutant emission reduction. In particular, as far as NOx gas is concerned, EGR systems are mature and widely used, but increased efficiency in terms of emissions abatement, is necessary in order to determine as best possible the actual oxygen content in the charge at the engine intake manifold. The present work compares the ability of the ANN and Neuro-Fuzzy approach (ANFIS) to predict the volumetric oxygen concentration at the intake, using experimental data acquired on a compression ignition engine in transient operational conditions. In an off-line evaluation of results, both models show good predicting abilities; in particular the ANFIS model presents an absolute error value for the training and test phases respectively equal to 0.7 and 0.9 (as a percentage of 3.5% and 4.5%), while, the same evaluation obtained using the ANN-BP model provides 0.92 and 0.9 (as a percentage of 4.6% and 4.5%). The comparison shows that the ANFIS model produces more accurate solutions in less time, using linear rules that bind the input variables with the output. The linearity of the rules is a key feature to decrease the convergence time especially when the model is used for the on-board periodic training activity due to the aging of the engine. Emissions Diesel engines Control Anfis model Grimaldi, C.N. verfasserin aut Battistoni, M. verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 113, Seite 576-588 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:113 pages:576-588 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 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_187 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 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_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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.50 Energietechnik: Allgemeines AR 113 576-588
allfields_unstemmed	10.1016/j.apenergy.2013.07.067 doi (DE-627)ELV001863339 (ELSEVIER)S0306-2619(13)00634-X DE-627 ger DE-627 rda eng 620 DE-600 52.50 bkl Mariani, F. verfasserin aut Diesel engine NOx emissions control: An advanced method for the O 2013 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent decades, the increasingly tight emissions regulations, along with the ever-increasing price of fuels and the request for more power from the engines, has pushed the world car industry to improve the performances of the applications of electronics, designed to control the internal combustion engines (ICE) and the pollutant emissions systems. At present, one of the main problems, in the development of diesel engines is represented by the achievement of an increasingly strict control on the systems used for the pollutant emission reduction. In particular, as far as NOx gas is concerned, EGR systems are mature and widely used, but increased efficiency in terms of emissions abatement, is necessary in order to determine as best possible the actual oxygen content in the charge at the engine intake manifold. The present work compares the ability of the ANN and Neuro-Fuzzy approach (ANFIS) to predict the volumetric oxygen concentration at the intake, using experimental data acquired on a compression ignition engine in transient operational conditions. In an off-line evaluation of results, both models show good predicting abilities; in particular the ANFIS model presents an absolute error value for the training and test phases respectively equal to 0.7 and 0.9 (as a percentage of 3.5% and 4.5%), while, the same evaluation obtained using the ANN-BP model provides 0.92 and 0.9 (as a percentage of 4.6% and 4.5%). The comparison shows that the ANFIS model produces more accurate solutions in less time, using linear rules that bind the input variables with the output. The linearity of the rules is a key feature to decrease the convergence time especially when the model is used for the on-board periodic training activity due to the aging of the engine. Emissions Diesel engines Control Anfis model Grimaldi, C.N. verfasserin aut Battistoni, M. verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 113, Seite 576-588 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:113 pages:576-588 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 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_187 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 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_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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.50 Energietechnik: Allgemeines AR 113 576-588
allfieldsGer	10.1016/j.apenergy.2013.07.067 doi (DE-627)ELV001863339 (ELSEVIER)S0306-2619(13)00634-X DE-627 ger DE-627 rda eng 620 DE-600 52.50 bkl Mariani, F. verfasserin aut Diesel engine NOx emissions control: An advanced method for the O 2013 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent decades, the increasingly tight emissions regulations, along with the ever-increasing price of fuels and the request for more power from the engines, has pushed the world car industry to improve the performances of the applications of electronics, designed to control the internal combustion engines (ICE) and the pollutant emissions systems. At present, one of the main problems, in the development of diesel engines is represented by the achievement of an increasingly strict control on the systems used for the pollutant emission reduction. In particular, as far as NOx gas is concerned, EGR systems are mature and widely used, but increased efficiency in terms of emissions abatement, is necessary in order to determine as best possible the actual oxygen content in the charge at the engine intake manifold. The present work compares the ability of the ANN and Neuro-Fuzzy approach (ANFIS) to predict the volumetric oxygen concentration at the intake, using experimental data acquired on a compression ignition engine in transient operational conditions. In an off-line evaluation of results, both models show good predicting abilities; in particular the ANFIS model presents an absolute error value for the training and test phases respectively equal to 0.7 and 0.9 (as a percentage of 3.5% and 4.5%), while, the same evaluation obtained using the ANN-BP model provides 0.92 and 0.9 (as a percentage of 4.6% and 4.5%). The comparison shows that the ANFIS model produces more accurate solutions in less time, using linear rules that bind the input variables with the output. The linearity of the rules is a key feature to decrease the convergence time especially when the model is used for the on-board periodic training activity due to the aging of the engine. Emissions Diesel engines Control Anfis model Grimaldi, C.N. verfasserin aut Battistoni, M. verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 113, Seite 576-588 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:113 pages:576-588 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 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_187 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 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_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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.50 Energietechnik: Allgemeines AR 113 576-588
allfieldsSound	10.1016/j.apenergy.2013.07.067 doi (DE-627)ELV001863339 (ELSEVIER)S0306-2619(13)00634-X DE-627 ger DE-627 rda eng 620 DE-600 52.50 bkl Mariani, F. verfasserin aut Diesel engine NOx emissions control: An advanced method for the O 2013 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent decades, the increasingly tight emissions regulations, along with the ever-increasing price of fuels and the request for more power from the engines, has pushed the world car industry to improve the performances of the applications of electronics, designed to control the internal combustion engines (ICE) and the pollutant emissions systems. At present, one of the main problems, in the development of diesel engines is represented by the achievement of an increasingly strict control on the systems used for the pollutant emission reduction. In particular, as far as NOx gas is concerned, EGR systems are mature and widely used, but increased efficiency in terms of emissions abatement, is necessary in order to determine as best possible the actual oxygen content in the charge at the engine intake manifold. The present work compares the ability of the ANN and Neuro-Fuzzy approach (ANFIS) to predict the volumetric oxygen concentration at the intake, using experimental data acquired on a compression ignition engine in transient operational conditions. In an off-line evaluation of results, both models show good predicting abilities; in particular the ANFIS model presents an absolute error value for the training and test phases respectively equal to 0.7 and 0.9 (as a percentage of 3.5% and 4.5%), while, the same evaluation obtained using the ANN-BP model provides 0.92 and 0.9 (as a percentage of 4.6% and 4.5%). The comparison shows that the ANFIS model produces more accurate solutions in less time, using linear rules that bind the input variables with the output. The linearity of the rules is a key feature to decrease the convergence time especially when the model is used for the on-board periodic training activity due to the aging of the engine. Emissions Diesel engines Control Anfis model Grimaldi, C.N. verfasserin aut Battistoni, M. verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 113, Seite 576-588 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:113 pages:576-588 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 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_187 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 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_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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.50 Energietechnik: Allgemeines AR 113 576-588
language	English
source	Enthalten in Applied energy 113, Seite 576-588 volume:113 pages:576-588
sourceStr	Enthalten in Applied energy 113, Seite 576-588 volume:113 pages:576-588
format_phy_str_mv	Article
bklname	Energietechnik: Allgemeines
institution	findex.gbv.de
topic_facet	Emissions Diesel engines Control Anfis model
dewey-raw	620
isfreeaccess_bool	false
container_title	Applied energy
authorswithroles_txt_mv	Mariani, F. @@aut@@ Grimaldi, C.N. @@aut@@ Battistoni, M. @@aut@@
publishDateDaySort_date	2013-01-01T00:00:00Z
hierarchy_top_id	320406709
dewey-sort	3620
id	ELV001863339
language_de	englisch
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author	Mariani, F.
spellingShingle	Mariani, F. ddc 620 bkl 52.50 misc Emissions misc Diesel engines misc Control misc Anfis model Diesel engine NOx emissions control: An advanced method for the O
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topic_title	620 DE-600 52.50 bkl Diesel engine NOx emissions control: An advanced method for the O Emissions Diesel engines Control Anfis model
topic	ddc 620 bkl 52.50 misc Emissions misc Diesel engines misc Control misc Anfis model
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title	Diesel engine NOx emissions control: An advanced method for the O
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title_full	Diesel engine NOx emissions control: An advanced method for the O
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title_sort	diesel engine nox emissions control: an advanced method for the o
title_auth	Diesel engine NOx emissions control: An advanced method for the O
abstract	In recent decades, the increasingly tight emissions regulations, along with the ever-increasing price of fuels and the request for more power from the engines, has pushed the world car industry to improve the performances of the applications of electronics, designed to control the internal combustion engines (ICE) and the pollutant emissions systems. At present, one of the main problems, in the development of diesel engines is represented by the achievement of an increasingly strict control on the systems used for the pollutant emission reduction. In particular, as far as NOx gas is concerned, EGR systems are mature and widely used, but increased efficiency in terms of emissions abatement, is necessary in order to determine as best possible the actual oxygen content in the charge at the engine intake manifold. The present work compares the ability of the ANN and Neuro-Fuzzy approach (ANFIS) to predict the volumetric oxygen concentration at the intake, using experimental data acquired on a compression ignition engine in transient operational conditions. In an off-line evaluation of results, both models show good predicting abilities; in particular the ANFIS model presents an absolute error value for the training and test phases respectively equal to 0.7 and 0.9 (as a percentage of 3.5% and 4.5%), while, the same evaluation obtained using the ANN-BP model provides 0.92 and 0.9 (as a percentage of 4.6% and 4.5%). The comparison shows that the ANFIS model produces more accurate solutions in less time, using linear rules that bind the input variables with the output. The linearity of the rules is a key feature to decrease the convergence time especially when the model is used for the on-board periodic training activity due to the aging of the engine.
abstractGer	In recent decades, the increasingly tight emissions regulations, along with the ever-increasing price of fuels and the request for more power from the engines, has pushed the world car industry to improve the performances of the applications of electronics, designed to control the internal combustion engines (ICE) and the pollutant emissions systems. At present, one of the main problems, in the development of diesel engines is represented by the achievement of an increasingly strict control on the systems used for the pollutant emission reduction. In particular, as far as NOx gas is concerned, EGR systems are mature and widely used, but increased efficiency in terms of emissions abatement, is necessary in order to determine as best possible the actual oxygen content in the charge at the engine intake manifold. The present work compares the ability of the ANN and Neuro-Fuzzy approach (ANFIS) to predict the volumetric oxygen concentration at the intake, using experimental data acquired on a compression ignition engine in transient operational conditions. In an off-line evaluation of results, both models show good predicting abilities; in particular the ANFIS model presents an absolute error value for the training and test phases respectively equal to 0.7 and 0.9 (as a percentage of 3.5% and 4.5%), while, the same evaluation obtained using the ANN-BP model provides 0.92 and 0.9 (as a percentage of 4.6% and 4.5%). The comparison shows that the ANFIS model produces more accurate solutions in less time, using linear rules that bind the input variables with the output. The linearity of the rules is a key feature to decrease the convergence time especially when the model is used for the on-board periodic training activity due to the aging of the engine.
abstract_unstemmed	In recent decades, the increasingly tight emissions regulations, along with the ever-increasing price of fuels and the request for more power from the engines, has pushed the world car industry to improve the performances of the applications of electronics, designed to control the internal combustion engines (ICE) and the pollutant emissions systems. At present, one of the main problems, in the development of diesel engines is represented by the achievement of an increasingly strict control on the systems used for the pollutant emission reduction. In particular, as far as NOx gas is concerned, EGR systems are mature and widely used, but increased efficiency in terms of emissions abatement, is necessary in order to determine as best possible the actual oxygen content in the charge at the engine intake manifold. The present work compares the ability of the ANN and Neuro-Fuzzy approach (ANFIS) to predict the volumetric oxygen concentration at the intake, using experimental data acquired on a compression ignition engine in transient operational conditions. In an off-line evaluation of results, both models show good predicting abilities; in particular the ANFIS model presents an absolute error value for the training and test phases respectively equal to 0.7 and 0.9 (as a percentage of 3.5% and 4.5%), while, the same evaluation obtained using the ANN-BP model provides 0.92 and 0.9 (as a percentage of 4.6% and 4.5%). The comparison shows that the ANFIS model produces more accurate solutions in less time, using linear rules that bind the input variables with the output. The linearity of the rules is a key feature to decrease the convergence time especially when the model is used for the on-board periodic training activity due to the aging of the engine.
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title_short	Diesel engine NOx emissions control: An advanced method for the O
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up_date	2024-07-06T22:49:18.988Z
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