The Impact of Fatty Acid Composition on Transportation Fuel Yields via the Non-Catalytic Cracking of Triacylglyceride Oils
Abstract A wide spectrum of triacylglyceride (TG) oils were decomposed in batch lab and continuous pilot-scale reactors to generate an extensive database, which was then used to construct a model to predict the detailed composition of products generated during non-catalytic cracking. The model was t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Seames, Wayne [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© AOCS 2017 |
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| Übergeordnetes Werk: |
Enthalten in: The journal of the American Oil Chemists' Society - Hoboken : Wiley, 1947, 94(2017), 3 vom: 05. Jan., Seite 497-509 |
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| Übergeordnetes Werk: |
volume:94 ; year:2017 ; number:3 ; day:05 ; month:01 ; pages:497-509 |
| Links: |
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| DOI / URN: |
10.1007/s11746-016-2944-0 |
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| Katalog-ID: |
SPR02268414X |
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| 100 | 1 | |a Seames, Wayne |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The Impact of Fatty Acid Composition on Transportation Fuel Yields via the Non-Catalytic Cracking of Triacylglyceride Oils |
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| 520 | |a Abstract A wide spectrum of triacylglyceride (TG) oils were decomposed in batch lab and continuous pilot-scale reactors to generate an extensive database, which was then used to construct a model to predict the detailed composition of products generated during non-catalytic cracking. The model was then coupled with additional simulated process steps to determine the yields of transportation products and other chemical co-products meeting specifications of their petroleum analogs as validated with laboratory testing. A statistical study was then performed to use the model to analyze the impact that changes in TG oil composition have upon target product yields. In this study, the model was used to simulate a viable suite of products for every TG oil analyzed. The model predicts minor differences in the ratio of products from various different fatty acid compositions. For example, it was found that stearic (C18:0), oleic (C18:1), and erucic (C22:1) acids show a positive effect on fuel yields. By contrast, palmitic (C16:0), linoleic (C18:2), and linolenic (C18:3) acids have negative impacts on fuel yields. From these results, a hypothetical “ideal” TG oil was constructed. This oil turns out to have a composition that is very close to the composition of high oleic sunflower oil. | ||
| 650 | 4 | |a Thermal cracking |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Triacylglyceride oil |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Renewable fuel |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Transportation fuel |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Simulation modeling |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Linnen, Michael |4 aut | |
| 700 | 1 | |a Sander, Blake |4 aut | |
| 700 | 1 | |a Wills, Robert |4 aut | |
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10.1007/s11746-016-2944-0 doi (DE-627)SPR02268414X (SPR)s11746-016-2944-0-e DE-627 ger DE-627 rakwb eng Seames, Wayne verfasserin aut The Impact of Fatty Acid Composition on Transportation Fuel Yields via the Non-Catalytic Cracking of Triacylglyceride Oils 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © AOCS 2017 Abstract A wide spectrum of triacylglyceride (TG) oils were decomposed in batch lab and continuous pilot-scale reactors to generate an extensive database, which was then used to construct a model to predict the detailed composition of products generated during non-catalytic cracking. The model was then coupled with additional simulated process steps to determine the yields of transportation products and other chemical co-products meeting specifications of their petroleum analogs as validated with laboratory testing. A statistical study was then performed to use the model to analyze the impact that changes in TG oil composition have upon target product yields. In this study, the model was used to simulate a viable suite of products for every TG oil analyzed. The model predicts minor differences in the ratio of products from various different fatty acid compositions. For example, it was found that stearic (C18:0), oleic (C18:1), and erucic (C22:1) acids show a positive effect on fuel yields. By contrast, palmitic (C16:0), linoleic (C18:2), and linolenic (C18:3) acids have negative impacts on fuel yields. From these results, a hypothetical “ideal” TG oil was constructed. This oil turns out to have a composition that is very close to the composition of high oleic sunflower oil. Thermal cracking (dpeaa)DE-He213 Triacylglyceride oil (dpeaa)DE-He213 Renewable fuel (dpeaa)DE-He213 Transportation fuel (dpeaa)DE-He213 Simulation modeling (dpeaa)DE-He213 Linnen, Michael aut Sander, Blake aut Wills, Robert aut Enthalten in The journal of the American Oil Chemists' Society Hoboken : Wiley, 1947 94(2017), 3 vom: 05. Jan., Seite 497-509 (DE-627)326644008 (DE-600)2041388-9 1558-9331 nnns volume:94 year:2017 number:3 day:05 month:01 pages:497-509 https://dx.doi.org/10.1007/s11746-016-2944-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 94 2017 3 05 01 497-509 |
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10.1007/s11746-016-2944-0 doi (DE-627)SPR02268414X (SPR)s11746-016-2944-0-e DE-627 ger DE-627 rakwb eng Seames, Wayne verfasserin aut The Impact of Fatty Acid Composition on Transportation Fuel Yields via the Non-Catalytic Cracking of Triacylglyceride Oils 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © AOCS 2017 Abstract A wide spectrum of triacylglyceride (TG) oils were decomposed in batch lab and continuous pilot-scale reactors to generate an extensive database, which was then used to construct a model to predict the detailed composition of products generated during non-catalytic cracking. The model was then coupled with additional simulated process steps to determine the yields of transportation products and other chemical co-products meeting specifications of their petroleum analogs as validated with laboratory testing. A statistical study was then performed to use the model to analyze the impact that changes in TG oil composition have upon target product yields. In this study, the model was used to simulate a viable suite of products for every TG oil analyzed. The model predicts minor differences in the ratio of products from various different fatty acid compositions. For example, it was found that stearic (C18:0), oleic (C18:1), and erucic (C22:1) acids show a positive effect on fuel yields. By contrast, palmitic (C16:0), linoleic (C18:2), and linolenic (C18:3) acids have negative impacts on fuel yields. From these results, a hypothetical “ideal” TG oil was constructed. This oil turns out to have a composition that is very close to the composition of high oleic sunflower oil. Thermal cracking (dpeaa)DE-He213 Triacylglyceride oil (dpeaa)DE-He213 Renewable fuel (dpeaa)DE-He213 Transportation fuel (dpeaa)DE-He213 Simulation modeling (dpeaa)DE-He213 Linnen, Michael aut Sander, Blake aut Wills, Robert aut Enthalten in The journal of the American Oil Chemists' Society Hoboken : Wiley, 1947 94(2017), 3 vom: 05. Jan., Seite 497-509 (DE-627)326644008 (DE-600)2041388-9 1558-9331 nnns volume:94 year:2017 number:3 day:05 month:01 pages:497-509 https://dx.doi.org/10.1007/s11746-016-2944-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 94 2017 3 05 01 497-509 |
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10.1007/s11746-016-2944-0 doi (DE-627)SPR02268414X (SPR)s11746-016-2944-0-e DE-627 ger DE-627 rakwb eng Seames, Wayne verfasserin aut The Impact of Fatty Acid Composition on Transportation Fuel Yields via the Non-Catalytic Cracking of Triacylglyceride Oils 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © AOCS 2017 Abstract A wide spectrum of triacylglyceride (TG) oils were decomposed in batch lab and continuous pilot-scale reactors to generate an extensive database, which was then used to construct a model to predict the detailed composition of products generated during non-catalytic cracking. The model was then coupled with additional simulated process steps to determine the yields of transportation products and other chemical co-products meeting specifications of their petroleum analogs as validated with laboratory testing. A statistical study was then performed to use the model to analyze the impact that changes in TG oil composition have upon target product yields. In this study, the model was used to simulate a viable suite of products for every TG oil analyzed. The model predicts minor differences in the ratio of products from various different fatty acid compositions. For example, it was found that stearic (C18:0), oleic (C18:1), and erucic (C22:1) acids show a positive effect on fuel yields. By contrast, palmitic (C16:0), linoleic (C18:2), and linolenic (C18:3) acids have negative impacts on fuel yields. From these results, a hypothetical “ideal” TG oil was constructed. This oil turns out to have a composition that is very close to the composition of high oleic sunflower oil. Thermal cracking (dpeaa)DE-He213 Triacylglyceride oil (dpeaa)DE-He213 Renewable fuel (dpeaa)DE-He213 Transportation fuel (dpeaa)DE-He213 Simulation modeling (dpeaa)DE-He213 Linnen, Michael aut Sander, Blake aut Wills, Robert aut Enthalten in The journal of the American Oil Chemists' Society Hoboken : Wiley, 1947 94(2017), 3 vom: 05. Jan., Seite 497-509 (DE-627)326644008 (DE-600)2041388-9 1558-9331 nnns volume:94 year:2017 number:3 day:05 month:01 pages:497-509 https://dx.doi.org/10.1007/s11746-016-2944-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 94 2017 3 05 01 497-509 |
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10.1007/s11746-016-2944-0 doi (DE-627)SPR02268414X (SPR)s11746-016-2944-0-e DE-627 ger DE-627 rakwb eng Seames, Wayne verfasserin aut The Impact of Fatty Acid Composition on Transportation Fuel Yields via the Non-Catalytic Cracking of Triacylglyceride Oils 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © AOCS 2017 Abstract A wide spectrum of triacylglyceride (TG) oils were decomposed in batch lab and continuous pilot-scale reactors to generate an extensive database, which was then used to construct a model to predict the detailed composition of products generated during non-catalytic cracking. The model was then coupled with additional simulated process steps to determine the yields of transportation products and other chemical co-products meeting specifications of their petroleum analogs as validated with laboratory testing. A statistical study was then performed to use the model to analyze the impact that changes in TG oil composition have upon target product yields. In this study, the model was used to simulate a viable suite of products for every TG oil analyzed. The model predicts minor differences in the ratio of products from various different fatty acid compositions. For example, it was found that stearic (C18:0), oleic (C18:1), and erucic (C22:1) acids show a positive effect on fuel yields. By contrast, palmitic (C16:0), linoleic (C18:2), and linolenic (C18:3) acids have negative impacts on fuel yields. From these results, a hypothetical “ideal” TG oil was constructed. This oil turns out to have a composition that is very close to the composition of high oleic sunflower oil. Thermal cracking (dpeaa)DE-He213 Triacylglyceride oil (dpeaa)DE-He213 Renewable fuel (dpeaa)DE-He213 Transportation fuel (dpeaa)DE-He213 Simulation modeling (dpeaa)DE-He213 Linnen, Michael aut Sander, Blake aut Wills, Robert aut Enthalten in The journal of the American Oil Chemists' Society Hoboken : Wiley, 1947 94(2017), 3 vom: 05. Jan., Seite 497-509 (DE-627)326644008 (DE-600)2041388-9 1558-9331 nnns volume:94 year:2017 number:3 day:05 month:01 pages:497-509 https://dx.doi.org/10.1007/s11746-016-2944-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 94 2017 3 05 01 497-509 |
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10.1007/s11746-016-2944-0 doi (DE-627)SPR02268414X (SPR)s11746-016-2944-0-e DE-627 ger DE-627 rakwb eng Seames, Wayne verfasserin aut The Impact of Fatty Acid Composition on Transportation Fuel Yields via the Non-Catalytic Cracking of Triacylglyceride Oils 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © AOCS 2017 Abstract A wide spectrum of triacylglyceride (TG) oils were decomposed in batch lab and continuous pilot-scale reactors to generate an extensive database, which was then used to construct a model to predict the detailed composition of products generated during non-catalytic cracking. The model was then coupled with additional simulated process steps to determine the yields of transportation products and other chemical co-products meeting specifications of their petroleum analogs as validated with laboratory testing. A statistical study was then performed to use the model to analyze the impact that changes in TG oil composition have upon target product yields. In this study, the model was used to simulate a viable suite of products for every TG oil analyzed. The model predicts minor differences in the ratio of products from various different fatty acid compositions. For example, it was found that stearic (C18:0), oleic (C18:1), and erucic (C22:1) acids show a positive effect on fuel yields. By contrast, palmitic (C16:0), linoleic (C18:2), and linolenic (C18:3) acids have negative impacts on fuel yields. From these results, a hypothetical “ideal” TG oil was constructed. This oil turns out to have a composition that is very close to the composition of high oleic sunflower oil. Thermal cracking (dpeaa)DE-He213 Triacylglyceride oil (dpeaa)DE-He213 Renewable fuel (dpeaa)DE-He213 Transportation fuel (dpeaa)DE-He213 Simulation modeling (dpeaa)DE-He213 Linnen, Michael aut Sander, Blake aut Wills, Robert aut Enthalten in The journal of the American Oil Chemists' Society Hoboken : Wiley, 1947 94(2017), 3 vom: 05. Jan., Seite 497-509 (DE-627)326644008 (DE-600)2041388-9 1558-9331 nnns volume:94 year:2017 number:3 day:05 month:01 pages:497-509 https://dx.doi.org/10.1007/s11746-016-2944-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 94 2017 3 05 01 497-509 |
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Seames, Wayne |
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Seames, Wayne misc Thermal cracking misc Triacylglyceride oil misc Renewable fuel misc Transportation fuel misc Simulation modeling The Impact of Fatty Acid Composition on Transportation Fuel Yields via the Non-Catalytic Cracking of Triacylglyceride Oils |
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The Impact of Fatty Acid Composition on Transportation Fuel Yields via the Non-Catalytic Cracking of Triacylglyceride Oils Thermal cracking (dpeaa)DE-He213 Triacylglyceride oil (dpeaa)DE-He213 Renewable fuel (dpeaa)DE-He213 Transportation fuel (dpeaa)DE-He213 Simulation modeling (dpeaa)DE-He213 |
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The Impact of Fatty Acid Composition on Transportation Fuel Yields via the Non-Catalytic Cracking of Triacylglyceride Oils |
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impact of fatty acid composition on transportation fuel yields via the non-catalytic cracking of triacylglyceride oils |
| title_auth |
The Impact of Fatty Acid Composition on Transportation Fuel Yields via the Non-Catalytic Cracking of Triacylglyceride Oils |
| abstract |
Abstract A wide spectrum of triacylglyceride (TG) oils were decomposed in batch lab and continuous pilot-scale reactors to generate an extensive database, which was then used to construct a model to predict the detailed composition of products generated during non-catalytic cracking. The model was then coupled with additional simulated process steps to determine the yields of transportation products and other chemical co-products meeting specifications of their petroleum analogs as validated with laboratory testing. A statistical study was then performed to use the model to analyze the impact that changes in TG oil composition have upon target product yields. In this study, the model was used to simulate a viable suite of products for every TG oil analyzed. The model predicts minor differences in the ratio of products from various different fatty acid compositions. For example, it was found that stearic (C18:0), oleic (C18:1), and erucic (C22:1) acids show a positive effect on fuel yields. By contrast, palmitic (C16:0), linoleic (C18:2), and linolenic (C18:3) acids have negative impacts on fuel yields. From these results, a hypothetical “ideal” TG oil was constructed. This oil turns out to have a composition that is very close to the composition of high oleic sunflower oil. © AOCS 2017 |
| abstractGer |
Abstract A wide spectrum of triacylglyceride (TG) oils were decomposed in batch lab and continuous pilot-scale reactors to generate an extensive database, which was then used to construct a model to predict the detailed composition of products generated during non-catalytic cracking. The model was then coupled with additional simulated process steps to determine the yields of transportation products and other chemical co-products meeting specifications of their petroleum analogs as validated with laboratory testing. A statistical study was then performed to use the model to analyze the impact that changes in TG oil composition have upon target product yields. In this study, the model was used to simulate a viable suite of products for every TG oil analyzed. The model predicts minor differences in the ratio of products from various different fatty acid compositions. For example, it was found that stearic (C18:0), oleic (C18:1), and erucic (C22:1) acids show a positive effect on fuel yields. By contrast, palmitic (C16:0), linoleic (C18:2), and linolenic (C18:3) acids have negative impacts on fuel yields. From these results, a hypothetical “ideal” TG oil was constructed. This oil turns out to have a composition that is very close to the composition of high oleic sunflower oil. © AOCS 2017 |
| abstract_unstemmed |
Abstract A wide spectrum of triacylglyceride (TG) oils were decomposed in batch lab and continuous pilot-scale reactors to generate an extensive database, which was then used to construct a model to predict the detailed composition of products generated during non-catalytic cracking. The model was then coupled with additional simulated process steps to determine the yields of transportation products and other chemical co-products meeting specifications of their petroleum analogs as validated with laboratory testing. A statistical study was then performed to use the model to analyze the impact that changes in TG oil composition have upon target product yields. In this study, the model was used to simulate a viable suite of products for every TG oil analyzed. The model predicts minor differences in the ratio of products from various different fatty acid compositions. For example, it was found that stearic (C18:0), oleic (C18:1), and erucic (C22:1) acids show a positive effect on fuel yields. By contrast, palmitic (C16:0), linoleic (C18:2), and linolenic (C18:3) acids have negative impacts on fuel yields. From these results, a hypothetical “ideal” TG oil was constructed. This oil turns out to have a composition that is very close to the composition of high oleic sunflower oil. © AOCS 2017 |
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The Impact of Fatty Acid Composition on Transportation Fuel Yields via the Non-Catalytic Cracking of Triacylglyceride Oils |
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https://dx.doi.org/10.1007/s11746-016-2944-0 |
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Linnen, Michael Sander, Blake Wills, Robert |
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10.1007/s11746-016-2944-0 |
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2024-07-03T14:15:17.482Z |
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