Jet fuel density via GC × GC-FID
Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet...
Ausführliche Beschreibung
Autor*in: |
Vozka, Petr [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2019transfer abstract |
---|
Schlagwörter: |
---|
Umfang: |
9 |
---|
Übergeordnetes Werk: |
Enthalten in: Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias - Yang, Chaoqiang ELSEVIER, 2018, the science and technology of fuel and energy, New York, NY [u.a.] |
---|---|
Übergeordnetes Werk: |
volume:235 ; year:2019 ; day:1 ; month:01 ; pages:1052-1060 ; extent:9 |
Links: |
---|
DOI / URN: |
10.1016/j.fuel.2018.08.110 |
---|
Katalog-ID: |
ELV044314167 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | ELV044314167 | ||
003 | DE-627 | ||
005 | 20230626004943.0 | ||
007 | cr uuu---uuuuu | ||
008 | 181113s2019 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1016/j.fuel.2018.08.110 |2 doi | |
028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001213.pica |
035 | |a (DE-627)ELV044314167 | ||
035 | |a (ELSEVIER)S0016-2361(18)31481-9 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 530 |a 600 |a 670 |q VZ |
084 | |a 51.00 |2 bkl | ||
100 | 1 | |a Vozka, Petr |e verfasserin |4 aut | |
245 | 1 | 0 | |a Jet fuel density via GC × GC-FID |
264 | 1 | |c 2019transfer abstract | |
300 | |a 9 | ||
336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
337 | |a nicht spezifiziert |b z |2 rdamedia | ||
338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
520 | |a Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples. | ||
520 | |a Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples. | ||
650 | 7 | |a Alternative jet fuel |2 Elsevier | |
650 | 7 | |a Weighted average |2 Elsevier | |
650 | 7 | |a PLS |2 Elsevier | |
650 | 7 | |a GC × GC |2 Elsevier | |
650 | 7 | |a SVM |2 Elsevier | |
650 | 7 | |a Jet fuel |2 Elsevier | |
650 | 7 | |a Fuel density |2 Elsevier | |
700 | 1 | |a Modereger, Brent A. |4 oth | |
700 | 1 | |a Park, Anthony C. |4 oth | |
700 | 1 | |a Zhang, Wan Tang Jeff |4 oth | |
700 | 1 | |a Trice, Rodney W. |4 oth | |
700 | 1 | |a Kenttämaa, Hilkka I. |4 oth | |
700 | 1 | |a Kilaz, Gozdem |4 oth | |
773 | 0 | 8 | |i Enthalten in |n Elsevier |a Yang, Chaoqiang ELSEVIER |t Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias |d 2018 |d the science and technology of fuel and energy |g New York, NY [u.a.] |w (DE-627)ELV000307122 |
773 | 1 | 8 | |g volume:235 |g year:2019 |g day:1 |g month:01 |g pages:1052-1060 |g extent:9 |
856 | 4 | 0 | |u https://doi.org/10.1016/j.fuel.2018.08.110 |3 Volltext |
912 | |a GBV_USEFLAG_U | ||
912 | |a GBV_ELV | ||
912 | |a SYSFLAG_U | ||
936 | b | k | |a 51.00 |j Werkstoffkunde: Allgemeines |q VZ |
951 | |a AR | ||
952 | |d 235 |j 2019 |b 1 |c 0101 |h 1052-1060 |g 9 |
author_variant |
p v pv |
---|---|
matchkey_str |
vozkapetrmoderegerbrentaparkanthonyczhan:2019----:efedniyig |
hierarchy_sort_str |
2019transfer abstract |
bklnumber |
51.00 |
publishDate |
2019 |
allfields |
10.1016/j.fuel.2018.08.110 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001213.pica (DE-627)ELV044314167 (ELSEVIER)S0016-2361(18)31481-9 DE-627 ger DE-627 rakwb eng 530 600 670 VZ 51.00 bkl Vozka, Petr verfasserin aut Jet fuel density via GC × GC-FID 2019transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples. Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples. Alternative jet fuel Elsevier Weighted average Elsevier PLS Elsevier GC × GC Elsevier SVM Elsevier Jet fuel Elsevier Fuel density Elsevier Modereger, Brent A. oth Park, Anthony C. oth Zhang, Wan Tang Jeff oth Trice, Rodney W. oth Kenttämaa, Hilkka I. oth Kilaz, Gozdem oth Enthalten in Elsevier Yang, Chaoqiang ELSEVIER Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias 2018 the science and technology of fuel and energy New York, NY [u.a.] (DE-627)ELV000307122 volume:235 year:2019 day:1 month:01 pages:1052-1060 extent:9 https://doi.org/10.1016/j.fuel.2018.08.110 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 51.00 Werkstoffkunde: Allgemeines VZ AR 235 2019 1 0101 1052-1060 9 |
spelling |
10.1016/j.fuel.2018.08.110 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001213.pica (DE-627)ELV044314167 (ELSEVIER)S0016-2361(18)31481-9 DE-627 ger DE-627 rakwb eng 530 600 670 VZ 51.00 bkl Vozka, Petr verfasserin aut Jet fuel density via GC × GC-FID 2019transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples. Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples. Alternative jet fuel Elsevier Weighted average Elsevier PLS Elsevier GC × GC Elsevier SVM Elsevier Jet fuel Elsevier Fuel density Elsevier Modereger, Brent A. oth Park, Anthony C. oth Zhang, Wan Tang Jeff oth Trice, Rodney W. oth Kenttämaa, Hilkka I. oth Kilaz, Gozdem oth Enthalten in Elsevier Yang, Chaoqiang ELSEVIER Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias 2018 the science and technology of fuel and energy New York, NY [u.a.] (DE-627)ELV000307122 volume:235 year:2019 day:1 month:01 pages:1052-1060 extent:9 https://doi.org/10.1016/j.fuel.2018.08.110 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 51.00 Werkstoffkunde: Allgemeines VZ AR 235 2019 1 0101 1052-1060 9 |
allfields_unstemmed |
10.1016/j.fuel.2018.08.110 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001213.pica (DE-627)ELV044314167 (ELSEVIER)S0016-2361(18)31481-9 DE-627 ger DE-627 rakwb eng 530 600 670 VZ 51.00 bkl Vozka, Petr verfasserin aut Jet fuel density via GC × GC-FID 2019transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples. Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples. Alternative jet fuel Elsevier Weighted average Elsevier PLS Elsevier GC × GC Elsevier SVM Elsevier Jet fuel Elsevier Fuel density Elsevier Modereger, Brent A. oth Park, Anthony C. oth Zhang, Wan Tang Jeff oth Trice, Rodney W. oth Kenttämaa, Hilkka I. oth Kilaz, Gozdem oth Enthalten in Elsevier Yang, Chaoqiang ELSEVIER Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias 2018 the science and technology of fuel and energy New York, NY [u.a.] (DE-627)ELV000307122 volume:235 year:2019 day:1 month:01 pages:1052-1060 extent:9 https://doi.org/10.1016/j.fuel.2018.08.110 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 51.00 Werkstoffkunde: Allgemeines VZ AR 235 2019 1 0101 1052-1060 9 |
allfieldsGer |
10.1016/j.fuel.2018.08.110 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001213.pica (DE-627)ELV044314167 (ELSEVIER)S0016-2361(18)31481-9 DE-627 ger DE-627 rakwb eng 530 600 670 VZ 51.00 bkl Vozka, Petr verfasserin aut Jet fuel density via GC × GC-FID 2019transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples. Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples. Alternative jet fuel Elsevier Weighted average Elsevier PLS Elsevier GC × GC Elsevier SVM Elsevier Jet fuel Elsevier Fuel density Elsevier Modereger, Brent A. oth Park, Anthony C. oth Zhang, Wan Tang Jeff oth Trice, Rodney W. oth Kenttämaa, Hilkka I. oth Kilaz, Gozdem oth Enthalten in Elsevier Yang, Chaoqiang ELSEVIER Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias 2018 the science and technology of fuel and energy New York, NY [u.a.] (DE-627)ELV000307122 volume:235 year:2019 day:1 month:01 pages:1052-1060 extent:9 https://doi.org/10.1016/j.fuel.2018.08.110 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 51.00 Werkstoffkunde: Allgemeines VZ AR 235 2019 1 0101 1052-1060 9 |
allfieldsSound |
10.1016/j.fuel.2018.08.110 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001213.pica (DE-627)ELV044314167 (ELSEVIER)S0016-2361(18)31481-9 DE-627 ger DE-627 rakwb eng 530 600 670 VZ 51.00 bkl Vozka, Petr verfasserin aut Jet fuel density via GC × GC-FID 2019transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples. Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples. Alternative jet fuel Elsevier Weighted average Elsevier PLS Elsevier GC × GC Elsevier SVM Elsevier Jet fuel Elsevier Fuel density Elsevier Modereger, Brent A. oth Park, Anthony C. oth Zhang, Wan Tang Jeff oth Trice, Rodney W. oth Kenttämaa, Hilkka I. oth Kilaz, Gozdem oth Enthalten in Elsevier Yang, Chaoqiang ELSEVIER Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias 2018 the science and technology of fuel and energy New York, NY [u.a.] (DE-627)ELV000307122 volume:235 year:2019 day:1 month:01 pages:1052-1060 extent:9 https://doi.org/10.1016/j.fuel.2018.08.110 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 51.00 Werkstoffkunde: Allgemeines VZ AR 235 2019 1 0101 1052-1060 9 |
language |
English |
source |
Enthalten in Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias New York, NY [u.a.] volume:235 year:2019 day:1 month:01 pages:1052-1060 extent:9 |
sourceStr |
Enthalten in Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias New York, NY [u.a.] volume:235 year:2019 day:1 month:01 pages:1052-1060 extent:9 |
format_phy_str_mv |
Article |
bklname |
Werkstoffkunde: Allgemeines |
institution |
findex.gbv.de |
topic_facet |
Alternative jet fuel Weighted average PLS GC × GC SVM Jet fuel Fuel density |
dewey-raw |
530 |
isfreeaccess_bool |
false |
container_title |
Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias |
authorswithroles_txt_mv |
Vozka, Petr @@aut@@ Modereger, Brent A. @@oth@@ Park, Anthony C. @@oth@@ Zhang, Wan Tang Jeff @@oth@@ Trice, Rodney W. @@oth@@ Kenttämaa, Hilkka I. @@oth@@ Kilaz, Gozdem @@oth@@ |
publishDateDaySort_date |
2019-01-01T00:00:00Z |
hierarchy_top_id |
ELV000307122 |
dewey-sort |
3530 |
id |
ELV044314167 |
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">ELV044314167</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626004943.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">181113s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.fuel.2018.08.110</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001213.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV044314167</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0016-2361(18)31481-9</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="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="a">600</subfield><subfield code="a">670</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">51.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Vozka, Petr</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Jet fuel density via GC × GC-FID</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">9</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">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Alternative jet fuel</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Weighted average</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">PLS</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">GC × GC</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">SVM</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Jet fuel</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fuel density</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Modereger, Brent A.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Park, Anthony C.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Wan Tang Jeff</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Trice, Rodney W.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kenttämaa, Hilkka I.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kilaz, Gozdem</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Yang, Chaoqiang ELSEVIER</subfield><subfield code="t">Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias</subfield><subfield code="d">2018</subfield><subfield code="d">the science and technology of fuel and energy</subfield><subfield code="g">New York, NY [u.a.]</subfield><subfield code="w">(DE-627)ELV000307122</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:235</subfield><subfield code="g">year:2019</subfield><subfield code="g">day:1</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:1052-1060</subfield><subfield code="g">extent:9</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.fuel.2018.08.110</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">51.00</subfield><subfield code="j">Werkstoffkunde: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">235</subfield><subfield code="j">2019</subfield><subfield code="b">1</subfield><subfield code="c">0101</subfield><subfield code="h">1052-1060</subfield><subfield code="g">9</subfield></datafield></record></collection>
|
author |
Vozka, Petr |
spellingShingle |
Vozka, Petr ddc 530 bkl 51.00 Elsevier Alternative jet fuel Elsevier Weighted average Elsevier PLS Elsevier GC × GC Elsevier SVM Elsevier Jet fuel Elsevier Fuel density Jet fuel density via GC × GC-FID |
authorStr |
Vozka, Petr |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV000307122 |
format |
electronic Article |
dewey-ones |
530 - Physics 600 - Technology 670 - Manufacturing |
delete_txt_mv |
keep |
author_role |
aut |
collection |
elsevier |
remote_str |
true |
illustrated |
Not Illustrated |
topic_title |
530 600 670 VZ 51.00 bkl Jet fuel density via GC × GC-FID Alternative jet fuel Elsevier Weighted average Elsevier PLS Elsevier GC × GC Elsevier SVM Elsevier Jet fuel Elsevier Fuel density Elsevier |
topic |
ddc 530 bkl 51.00 Elsevier Alternative jet fuel Elsevier Weighted average Elsevier PLS Elsevier GC × GC Elsevier SVM Elsevier Jet fuel Elsevier Fuel density |
topic_unstemmed |
ddc 530 bkl 51.00 Elsevier Alternative jet fuel Elsevier Weighted average Elsevier PLS Elsevier GC × GC Elsevier SVM Elsevier Jet fuel Elsevier Fuel density |
topic_browse |
ddc 530 bkl 51.00 Elsevier Alternative jet fuel Elsevier Weighted average Elsevier PLS Elsevier GC × GC Elsevier SVM Elsevier Jet fuel Elsevier Fuel density |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
zu |
author2_variant |
b a m ba bam a c p ac acp w t j z wtj wtjz r w t rw rwt h i k hi hik g k gk |
hierarchy_parent_title |
Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias |
hierarchy_parent_id |
ELV000307122 |
dewey-tens |
530 - Physics 600 - Technology 670 - Manufacturing |
hierarchy_top_title |
Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)ELV000307122 |
title |
Jet fuel density via GC × GC-FID |
ctrlnum |
(DE-627)ELV044314167 (ELSEVIER)S0016-2361(18)31481-9 |
title_full |
Jet fuel density via GC × GC-FID |
author_sort |
Vozka, Petr |
journal |
Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias |
journalStr |
Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
500 - Science 600 - Technology |
recordtype |
marc |
publishDateSort |
2019 |
contenttype_str_mv |
zzz |
container_start_page |
1052 |
author_browse |
Vozka, Petr |
container_volume |
235 |
physical |
9 |
class |
530 600 670 VZ 51.00 bkl |
format_se |
Elektronische Aufsätze |
author-letter |
Vozka, Petr |
doi_str_mv |
10.1016/j.fuel.2018.08.110 |
dewey-full |
530 600 670 |
title_sort |
jet fuel density via gc × gc-fid |
title_auth |
Jet fuel density via GC × GC-FID |
abstract |
Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples. |
abstractGer |
Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples. |
abstract_unstemmed |
Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples. |
collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
title_short |
Jet fuel density via GC × GC-FID |
url |
https://doi.org/10.1016/j.fuel.2018.08.110 |
remote_bool |
true |
author2 |
Modereger, Brent A. Park, Anthony C. Zhang, Wan Tang Jeff Trice, Rodney W. Kenttämaa, Hilkka I. Kilaz, Gozdem |
author2Str |
Modereger, Brent A. Park, Anthony C. Zhang, Wan Tang Jeff Trice, Rodney W. Kenttämaa, Hilkka I. Kilaz, Gozdem |
ppnlink |
ELV000307122 |
mediatype_str_mv |
z |
isOA_txt |
false |
hochschulschrift_bool |
false |
author2_role |
oth oth oth oth oth oth |
doi_str |
10.1016/j.fuel.2018.08.110 |
up_date |
2024-07-06T21:09:44.424Z |
_version_ |
1803865486183628800 |
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">ELV044314167</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626004943.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">181113s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.fuel.2018.08.110</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001213.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV044314167</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0016-2361(18)31481-9</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="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="a">600</subfield><subfield code="a">670</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">51.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Vozka, Petr</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Jet fuel density via GC × GC-FID</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">9</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">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Aviation jet fuels contain over a thousand different hydrocarbons, making the prediction of their properties from chemical composition difficult. The density of a jet fuel at 15 °C is necessary for its certification. We present here an analytical approach for the determination of the density of jet fuels based on the chemical composition of the fuel determined via comprehensive two-dimensional gas chromatography with flame ionization detector (GC × GC-FID). The analysis was carried out using two-dimensional gas chromatography with electron ionization high-resolution time-of-flight mass spectrometry detection (GC × GC-TOF/MS) and flame ionization detection (GC × GC-FID). A detailed chemical composition analysis was performed on 50 samples, including petroleum-based aviation fuels and all approved alternative aviation fuel blending components. Fuel constituents were classified into seven hydrocarbon classes (n-paraffins, isoparaffins, monocycloparaffins, di- and tricycloparaffins, alkylbenzenes, cycloaromatic compounds, and alkylnaphthalenes) with the number of carbons in the range of 7–20. Several correlation algorithms and approaches were explored, including partial least squares regression (PLS) and support vector machines method (SVM), which yielded the most accurate results with mean absolute percentage errors of 0.1740% and 0.0984%, respectively. All used methods were validated utilizing uncalibrated validation samples.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Alternative jet fuel</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Weighted average</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">PLS</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">GC × GC</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">SVM</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Jet fuel</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fuel density</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Modereger, Brent A.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Park, Anthony C.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Wan Tang Jeff</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Trice, Rodney W.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kenttämaa, Hilkka I.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kilaz, Gozdem</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Yang, Chaoqiang ELSEVIER</subfield><subfield code="t">Achieving highly tunable negative permittivity in titanium nitride/polyimide nanocomposites via controlled DC bias</subfield><subfield code="d">2018</subfield><subfield code="d">the science and technology of fuel and energy</subfield><subfield code="g">New York, NY [u.a.]</subfield><subfield code="w">(DE-627)ELV000307122</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:235</subfield><subfield code="g">year:2019</subfield><subfield code="g">day:1</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:1052-1060</subfield><subfield code="g">extent:9</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.fuel.2018.08.110</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">51.00</subfield><subfield code="j">Werkstoffkunde: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">235</subfield><subfield code="j">2019</subfield><subfield code="b">1</subfield><subfield code="c">0101</subfield><subfield code="h">1052-1060</subfield><subfield code="g">9</subfield></datafield></record></collection>
|
score |
7.3997936 |