The modeling of ethanol production by Kluyveromyces marxianus using whey as substrate in continuous A-Stat bioreactors

Abstract We investigated the kinetics of whey bioconversion into ethanol by Kluyveromyces marxianus in continuous bioreactors using the “accelerostat technique” (A-stat). Cultivations using free and Ca-alginate immobilized cells were evaluated using two different acceleration rates (a). The kinetic...
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Autor*in:	Gabardo, Sabrina [verfasserIn] Pereira, Gabriela Feix [verfasserIn] Rech, Rosane [verfasserIn] Ayub, Marco Antônio Záchia [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Bioprocess modeling Ethanol Continuous fermentation A-stat control Whey

Übergeordnetes Werk:	Enthalten in: Journal of industrial microbiology and biotechnology - Berlin : Springer, 1986, 42(2015), 9 vom: 02. Aug., Seite 1243-1253
Übergeordnetes Werk:	volume:42 ; year:2015 ; number:9 ; day:02 ; month:08 ; pages:1243-1253

Links:	Volltext

DOI / URN:	10.1007/s10295-015-1661-2

Katalog-ID:	SPR009380078

Internformat


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245	1	4	\|a The modeling of ethanol production by Kluyveromyces marxianus using whey as substrate in continuous A-Stat bioreactors
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520			\|a Abstract We investigated the kinetics of whey bioconversion into ethanol by Kluyveromyces marxianus in continuous bioreactors using the “accelerostat technique” (A-stat). Cultivations using free and Ca-alginate immobilized cells were evaluated using two different acceleration rates (a). The kinetic profiles of these systems were modeled using four different unstructured models, differing in the expressions for the specific growth (μ) and substrate consumption rates (rs), taking into account substrate limitation and product inhibition. Experimental data showed that the dilution rate (D) directly affected cell physiology and metabolism. The specific growth rate followed the dilution rate (μ≈D) for the lowest acceleration rate (a = 0.0015 $ h^{−2} $), condition in which the highest ethanol yield (0.52 g $ g^{−1} $) was obtained. The highest acceleration rate (a = 0.00667 $ h^{−2} $) led to a lower ethanol yield (0.40 g $ g^{−1} $) in the system where free cells were used, whereas with immobilized cells ethanol yields increased by 23 % (0.49 g $ g^{−1} $). Among the evaluated models, Monod and Levenspiel combined with Ghose and Tyagi models were found to be more appropriate for describing the kinetics of whey bioconversion into ethanol. These results may be useful in scaling up the process for ethanol production from whey.
650		4	\|a Bioprocess modeling \|7 (dpeaa)DE-He213
650		4	\|a Ethanol \|7 (dpeaa)DE-He213
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650		4	\|a A-stat control \|7 (dpeaa)DE-He213
650		4	\|a Whey \|7 (dpeaa)DE-He213
700	1		\|a Pereira, Gabriela Feix \|e verfasserin \|4 aut
700	1		\|a Rech, Rosane \|e verfasserin \|4 aut
700	1		\|a Ayub, Marco Antônio Záchia \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Journal of industrial microbiology and biotechnology \|d Berlin : Springer, 1986 \|g 42(2015), 9 vom: 02. Aug., Seite 1243-1253 \|w (DE-627)300589514 \|w (DE-600)1482484-X \|x 1476-5535 \|7 nnns
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912			\|a GBV_ILN_2061
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912			\|a GBV_ILN_2065
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936	b	k	\|a 42.30 \|q ASE
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952			\|d 42 \|j 2015 \|e 9 \|b 02 \|c 08 \|h 1243-1253

Indexfelder

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matchkey_str	article:14765535:2015----::hmdlnoehnlrdcinylyeoyemrinssnweasbtae
hierarchy_sort_str	2015
bklnumber	42.30 58.00
publishDate	2015
allfields	10.1007/s10295-015-1661-2 doi (DE-627)SPR009380078 (SPR)s10295-015-1661-2-e DE-627 ger DE-627 rakwb eng 570 ASE 42.30 bkl 58.00 bkl Gabardo, Sabrina verfasserin aut The modeling of ethanol production by Kluyveromyces marxianus using whey as substrate in continuous A-Stat bioreactors 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We investigated the kinetics of whey bioconversion into ethanol by Kluyveromyces marxianus in continuous bioreactors using the “accelerostat technique” (A-stat). Cultivations using free and Ca-alginate immobilized cells were evaluated using two different acceleration rates (a). The kinetic profiles of these systems were modeled using four different unstructured models, differing in the expressions for the specific growth (μ) and substrate consumption rates (rs), taking into account substrate limitation and product inhibition. Experimental data showed that the dilution rate (D) directly affected cell physiology and metabolism. The specific growth rate followed the dilution rate (μ≈D) for the lowest acceleration rate (a = 0.0015 $ h^{−2} $), condition in which the highest ethanol yield (0.52 g $ g^{−1} $) was obtained. The highest acceleration rate (a = 0.00667 $ h^{−2} $) led to a lower ethanol yield (0.40 g $ g^{−1} $) in the system where free cells were used, whereas with immobilized cells ethanol yields increased by 23 % (0.49 g $ g^{−1} $). Among the evaluated models, Monod and Levenspiel combined with Ghose and Tyagi models were found to be more appropriate for describing the kinetics of whey bioconversion into ethanol. These results may be useful in scaling up the process for ethanol production from whey. Bioprocess modeling (dpeaa)DE-He213 Ethanol (dpeaa)DE-He213 Continuous fermentation (dpeaa)DE-He213 A-stat control (dpeaa)DE-He213 Whey (dpeaa)DE-He213 Pereira, Gabriela Feix verfasserin aut Rech, Rosane verfasserin aut Ayub, Marco Antônio Záchia verfasserin aut Enthalten in Journal of industrial microbiology and biotechnology Berlin : Springer, 1986 42(2015), 9 vom: 02. Aug., Seite 1243-1253 (DE-627)300589514 (DE-600)1482484-X 1476-5535 nnns volume:42 year:2015 number:9 day:02 month:08 pages:1243-1253 https://dx.doi.org/10.1007/s10295-015-1661-2 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_101 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 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_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 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_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 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_2446 GBV_ILN_2470 GBV_ILN_2472 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_4246 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_4367 GBV_ILN_4393 GBV_ILN_4700 42.30 ASE 58.00 ASE AR 42 2015 9 02 08 1243-1253
spelling	10.1007/s10295-015-1661-2 doi (DE-627)SPR009380078 (SPR)s10295-015-1661-2-e DE-627 ger DE-627 rakwb eng 570 ASE 42.30 bkl 58.00 bkl Gabardo, Sabrina verfasserin aut The modeling of ethanol production by Kluyveromyces marxianus using whey as substrate in continuous A-Stat bioreactors 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We investigated the kinetics of whey bioconversion into ethanol by Kluyveromyces marxianus in continuous bioreactors using the “accelerostat technique” (A-stat). Cultivations using free and Ca-alginate immobilized cells were evaluated using two different acceleration rates (a). The kinetic profiles of these systems were modeled using four different unstructured models, differing in the expressions for the specific growth (μ) and substrate consumption rates (rs), taking into account substrate limitation and product inhibition. Experimental data showed that the dilution rate (D) directly affected cell physiology and metabolism. The specific growth rate followed the dilution rate (μ≈D) for the lowest acceleration rate (a = 0.0015 $ h^{−2} $), condition in which the highest ethanol yield (0.52 g $ g^{−1} $) was obtained. The highest acceleration rate (a = 0.00667 $ h^{−2} $) led to a lower ethanol yield (0.40 g $ g^{−1} $) in the system where free cells were used, whereas with immobilized cells ethanol yields increased by 23 % (0.49 g $ g^{−1} $). Among the evaluated models, Monod and Levenspiel combined with Ghose and Tyagi models were found to be more appropriate for describing the kinetics of whey bioconversion into ethanol. These results may be useful in scaling up the process for ethanol production from whey. Bioprocess modeling (dpeaa)DE-He213 Ethanol (dpeaa)DE-He213 Continuous fermentation (dpeaa)DE-He213 A-stat control (dpeaa)DE-He213 Whey (dpeaa)DE-He213 Pereira, Gabriela Feix verfasserin aut Rech, Rosane verfasserin aut Ayub, Marco Antônio Záchia verfasserin aut Enthalten in Journal of industrial microbiology and biotechnology Berlin : Springer, 1986 42(2015), 9 vom: 02. Aug., Seite 1243-1253 (DE-627)300589514 (DE-600)1482484-X 1476-5535 nnns volume:42 year:2015 number:9 day:02 month:08 pages:1243-1253 https://dx.doi.org/10.1007/s10295-015-1661-2 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_101 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 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_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 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_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 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_2446 GBV_ILN_2470 GBV_ILN_2472 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_4246 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_4367 GBV_ILN_4393 GBV_ILN_4700 42.30 ASE 58.00 ASE AR 42 2015 9 02 08 1243-1253
allfields_unstemmed	10.1007/s10295-015-1661-2 doi (DE-627)SPR009380078 (SPR)s10295-015-1661-2-e DE-627 ger DE-627 rakwb eng 570 ASE 42.30 bkl 58.00 bkl Gabardo, Sabrina verfasserin aut The modeling of ethanol production by Kluyveromyces marxianus using whey as substrate in continuous A-Stat bioreactors 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We investigated the kinetics of whey bioconversion into ethanol by Kluyveromyces marxianus in continuous bioreactors using the “accelerostat technique” (A-stat). Cultivations using free and Ca-alginate immobilized cells were evaluated using two different acceleration rates (a). The kinetic profiles of these systems were modeled using four different unstructured models, differing in the expressions for the specific growth (μ) and substrate consumption rates (rs), taking into account substrate limitation and product inhibition. Experimental data showed that the dilution rate (D) directly affected cell physiology and metabolism. The specific growth rate followed the dilution rate (μ≈D) for the lowest acceleration rate (a = 0.0015 $ h^{−2} $), condition in which the highest ethanol yield (0.52 g $ g^{−1} $) was obtained. The highest acceleration rate (a = 0.00667 $ h^{−2} $) led to a lower ethanol yield (0.40 g $ g^{−1} $) in the system where free cells were used, whereas with immobilized cells ethanol yields increased by 23 % (0.49 g $ g^{−1} $). Among the evaluated models, Monod and Levenspiel combined with Ghose and Tyagi models were found to be more appropriate for describing the kinetics of whey bioconversion into ethanol. These results may be useful in scaling up the process for ethanol production from whey. Bioprocess modeling (dpeaa)DE-He213 Ethanol (dpeaa)DE-He213 Continuous fermentation (dpeaa)DE-He213 A-stat control (dpeaa)DE-He213 Whey (dpeaa)DE-He213 Pereira, Gabriela Feix verfasserin aut Rech, Rosane verfasserin aut Ayub, Marco Antônio Záchia verfasserin aut Enthalten in Journal of industrial microbiology and biotechnology Berlin : Springer, 1986 42(2015), 9 vom: 02. Aug., Seite 1243-1253 (DE-627)300589514 (DE-600)1482484-X 1476-5535 nnns volume:42 year:2015 number:9 day:02 month:08 pages:1243-1253 https://dx.doi.org/10.1007/s10295-015-1661-2 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_101 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 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_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 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_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 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_2446 GBV_ILN_2470 GBV_ILN_2472 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_4246 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_4367 GBV_ILN_4393 GBV_ILN_4700 42.30 ASE 58.00 ASE AR 42 2015 9 02 08 1243-1253
allfieldsGer	10.1007/s10295-015-1661-2 doi (DE-627)SPR009380078 (SPR)s10295-015-1661-2-e DE-627 ger DE-627 rakwb eng 570 ASE 42.30 bkl 58.00 bkl Gabardo, Sabrina verfasserin aut The modeling of ethanol production by Kluyveromyces marxianus using whey as substrate in continuous A-Stat bioreactors 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We investigated the kinetics of whey bioconversion into ethanol by Kluyveromyces marxianus in continuous bioreactors using the “accelerostat technique” (A-stat). Cultivations using free and Ca-alginate immobilized cells were evaluated using two different acceleration rates (a). The kinetic profiles of these systems were modeled using four different unstructured models, differing in the expressions for the specific growth (μ) and substrate consumption rates (rs), taking into account substrate limitation and product inhibition. Experimental data showed that the dilution rate (D) directly affected cell physiology and metabolism. The specific growth rate followed the dilution rate (μ≈D) for the lowest acceleration rate (a = 0.0015 $ h^{−2} $), condition in which the highest ethanol yield (0.52 g $ g^{−1} $) was obtained. The highest acceleration rate (a = 0.00667 $ h^{−2} $) led to a lower ethanol yield (0.40 g $ g^{−1} $) in the system where free cells were used, whereas with immobilized cells ethanol yields increased by 23 % (0.49 g $ g^{−1} $). Among the evaluated models, Monod and Levenspiel combined with Ghose and Tyagi models were found to be more appropriate for describing the kinetics of whey bioconversion into ethanol. These results may be useful in scaling up the process for ethanol production from whey. Bioprocess modeling (dpeaa)DE-He213 Ethanol (dpeaa)DE-He213 Continuous fermentation (dpeaa)DE-He213 A-stat control (dpeaa)DE-He213 Whey (dpeaa)DE-He213 Pereira, Gabriela Feix verfasserin aut Rech, Rosane verfasserin aut Ayub, Marco Antônio Záchia verfasserin aut Enthalten in Journal of industrial microbiology and biotechnology Berlin : Springer, 1986 42(2015), 9 vom: 02. Aug., Seite 1243-1253 (DE-627)300589514 (DE-600)1482484-X 1476-5535 nnns volume:42 year:2015 number:9 day:02 month:08 pages:1243-1253 https://dx.doi.org/10.1007/s10295-015-1661-2 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_101 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 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_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 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_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 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_2446 GBV_ILN_2470 GBV_ILN_2472 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_4246 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_4367 GBV_ILN_4393 GBV_ILN_4700 42.30 ASE 58.00 ASE AR 42 2015 9 02 08 1243-1253
allfieldsSound	10.1007/s10295-015-1661-2 doi (DE-627)SPR009380078 (SPR)s10295-015-1661-2-e DE-627 ger DE-627 rakwb eng 570 ASE 42.30 bkl 58.00 bkl Gabardo, Sabrina verfasserin aut The modeling of ethanol production by Kluyveromyces marxianus using whey as substrate in continuous A-Stat bioreactors 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We investigated the kinetics of whey bioconversion into ethanol by Kluyveromyces marxianus in continuous bioreactors using the “accelerostat technique” (A-stat). Cultivations using free and Ca-alginate immobilized cells were evaluated using two different acceleration rates (a). The kinetic profiles of these systems were modeled using four different unstructured models, differing in the expressions for the specific growth (μ) and substrate consumption rates (rs), taking into account substrate limitation and product inhibition. Experimental data showed that the dilution rate (D) directly affected cell physiology and metabolism. The specific growth rate followed the dilution rate (μ≈D) for the lowest acceleration rate (a = 0.0015 $ h^{−2} $), condition in which the highest ethanol yield (0.52 g $ g^{−1} $) was obtained. The highest acceleration rate (a = 0.00667 $ h^{−2} $) led to a lower ethanol yield (0.40 g $ g^{−1} $) in the system where free cells were used, whereas with immobilized cells ethanol yields increased by 23 % (0.49 g $ g^{−1} $). Among the evaluated models, Monod and Levenspiel combined with Ghose and Tyagi models were found to be more appropriate for describing the kinetics of whey bioconversion into ethanol. These results may be useful in scaling up the process for ethanol production from whey. Bioprocess modeling (dpeaa)DE-He213 Ethanol (dpeaa)DE-He213 Continuous fermentation (dpeaa)DE-He213 A-stat control (dpeaa)DE-He213 Whey (dpeaa)DE-He213 Pereira, Gabriela Feix verfasserin aut Rech, Rosane verfasserin aut Ayub, Marco Antônio Záchia verfasserin aut Enthalten in Journal of industrial microbiology and biotechnology Berlin : Springer, 1986 42(2015), 9 vom: 02. Aug., Seite 1243-1253 (DE-627)300589514 (DE-600)1482484-X 1476-5535 nnns volume:42 year:2015 number:9 day:02 month:08 pages:1243-1253 https://dx.doi.org/10.1007/s10295-015-1661-2 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_101 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 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_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 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_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 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_2446 GBV_ILN_2470 GBV_ILN_2472 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_4246 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_4367 GBV_ILN_4393 GBV_ILN_4700 42.30 ASE 58.00 ASE AR 42 2015 9 02 08 1243-1253
language	English
source	Enthalten in Journal of industrial microbiology and biotechnology 42(2015), 9 vom: 02. Aug., Seite 1243-1253 volume:42 year:2015 number:9 day:02 month:08 pages:1243-1253
sourceStr	Enthalten in Journal of industrial microbiology and biotechnology 42(2015), 9 vom: 02. Aug., Seite 1243-1253 volume:42 year:2015 number:9 day:02 month:08 pages:1243-1253
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Bioprocess modeling Ethanol Continuous fermentation A-stat control Whey
dewey-raw	570
isfreeaccess_bool	false
container_title	Journal of industrial microbiology and biotechnology
authorswithroles_txt_mv	Gabardo, Sabrina @@aut@@ Pereira, Gabriela Feix @@aut@@ Rech, Rosane @@aut@@ Ayub, Marco Antônio Záchia @@aut@@
publishDateDaySort_date	2015-08-02T00:00:00Z
hierarchy_top_id	300589514
dewey-sort	3570
id	SPR009380078
language_de	englisch
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author	Gabardo, Sabrina
spellingShingle	Gabardo, Sabrina ddc 570 bkl 42.30 bkl 58.00 misc Bioprocess modeling misc Ethanol misc Continuous fermentation misc A-stat control misc Whey The modeling of ethanol production by Kluyveromyces marxianus using whey as substrate in continuous A-Stat bioreactors
authorStr	Gabardo, Sabrina
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topic_title	570 ASE 42.30 bkl 58.00 bkl The modeling of ethanol production by Kluyveromyces marxianus using whey as substrate in continuous A-Stat bioreactors Bioprocess modeling (dpeaa)DE-He213 Ethanol (dpeaa)DE-He213 Continuous fermentation (dpeaa)DE-He213 A-stat control (dpeaa)DE-He213 Whey (dpeaa)DE-He213
topic	ddc 570 bkl 42.30 bkl 58.00 misc Bioprocess modeling misc Ethanol misc Continuous fermentation misc A-stat control misc Whey
topic_unstemmed	ddc 570 bkl 42.30 bkl 58.00 misc Bioprocess modeling misc Ethanol misc Continuous fermentation misc A-stat control misc Whey
topic_browse	ddc 570 bkl 42.30 bkl 58.00 misc Bioprocess modeling misc Ethanol misc Continuous fermentation misc A-stat control misc Whey
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title	The modeling of ethanol production by Kluyveromyces marxianus using whey as substrate in continuous A-Stat bioreactors
ctrlnum	(DE-627)SPR009380078 (SPR)s10295-015-1661-2-e
title_full	The modeling of ethanol production by Kluyveromyces marxianus using whey as substrate in continuous A-Stat bioreactors
author_sort	Gabardo, Sabrina
journal	Journal of industrial microbiology and biotechnology
journalStr	Journal of industrial microbiology and biotechnology
lang_code	eng
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author_browse	Gabardo, Sabrina Pereira, Gabriela Feix Rech, Rosane Ayub, Marco Antônio Záchia
container_volume	42
class	570 ASE 42.30 bkl 58.00 bkl
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author-letter	Gabardo, Sabrina
doi_str_mv	10.1007/s10295-015-1661-2
dewey-full	570
author2-role	verfasserin
title_sort	modeling of ethanol production by kluyveromyces marxianus using whey as substrate in continuous a-stat bioreactors
title_auth	The modeling of ethanol production by Kluyveromyces marxianus using whey as substrate in continuous A-Stat bioreactors
abstract	Abstract We investigated the kinetics of whey bioconversion into ethanol by Kluyveromyces marxianus in continuous bioreactors using the “accelerostat technique” (A-stat). Cultivations using free and Ca-alginate immobilized cells were evaluated using two different acceleration rates (a). The kinetic profiles of these systems were modeled using four different unstructured models, differing in the expressions for the specific growth (μ) and substrate consumption rates (rs), taking into account substrate limitation and product inhibition. Experimental data showed that the dilution rate (D) directly affected cell physiology and metabolism. The specific growth rate followed the dilution rate (μ≈D) for the lowest acceleration rate (a = 0.0015 $ h^{−2} $), condition in which the highest ethanol yield (0.52 g $ g^{−1} $) was obtained. The highest acceleration rate (a = 0.00667 $ h^{−2} $) led to a lower ethanol yield (0.40 g $ g^{−1} $) in the system where free cells were used, whereas with immobilized cells ethanol yields increased by 23 % (0.49 g $ g^{−1} $). Among the evaluated models, Monod and Levenspiel combined with Ghose and Tyagi models were found to be more appropriate for describing the kinetics of whey bioconversion into ethanol. These results may be useful in scaling up the process for ethanol production from whey.
abstractGer	Abstract We investigated the kinetics of whey bioconversion into ethanol by Kluyveromyces marxianus in continuous bioreactors using the “accelerostat technique” (A-stat). Cultivations using free and Ca-alginate immobilized cells were evaluated using two different acceleration rates (a). The kinetic profiles of these systems were modeled using four different unstructured models, differing in the expressions for the specific growth (μ) and substrate consumption rates (rs), taking into account substrate limitation and product inhibition. Experimental data showed that the dilution rate (D) directly affected cell physiology and metabolism. The specific growth rate followed the dilution rate (μ≈D) for the lowest acceleration rate (a = 0.0015 $ h^{−2} $), condition in which the highest ethanol yield (0.52 g $ g^{−1} $) was obtained. The highest acceleration rate (a = 0.00667 $ h^{−2} $) led to a lower ethanol yield (0.40 g $ g^{−1} $) in the system where free cells were used, whereas with immobilized cells ethanol yields increased by 23 % (0.49 g $ g^{−1} $). Among the evaluated models, Monod and Levenspiel combined with Ghose and Tyagi models were found to be more appropriate for describing the kinetics of whey bioconversion into ethanol. These results may be useful in scaling up the process for ethanol production from whey.
abstract_unstemmed	Abstract We investigated the kinetics of whey bioconversion into ethanol by Kluyveromyces marxianus in continuous bioreactors using the “accelerostat technique” (A-stat). Cultivations using free and Ca-alginate immobilized cells were evaluated using two different acceleration rates (a). The kinetic profiles of these systems were modeled using four different unstructured models, differing in the expressions for the specific growth (μ) and substrate consumption rates (rs), taking into account substrate limitation and product inhibition. Experimental data showed that the dilution rate (D) directly affected cell physiology and metabolism. The specific growth rate followed the dilution rate (μ≈D) for the lowest acceleration rate (a = 0.0015 $ h^{−2} $), condition in which the highest ethanol yield (0.52 g $ g^{−1} $) was obtained. The highest acceleration rate (a = 0.00667 $ h^{−2} $) led to a lower ethanol yield (0.40 g $ g^{−1} $) in the system where free cells were used, whereas with immobilized cells ethanol yields increased by 23 % (0.49 g $ g^{−1} $). Among the evaluated models, Monod and Levenspiel combined with Ghose and Tyagi models were found to be more appropriate for describing the kinetics of whey bioconversion into ethanol. These results may be useful in scaling up the process for ethanol production from whey.
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container_issue	9
title_short	The modeling of ethanol production by Kluyveromyces marxianus using whey as substrate in continuous A-Stat bioreactors
url	https://dx.doi.org/10.1007/s10295-015-1661-2
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author2	Pereira, Gabriela Feix Rech, Rosane Ayub, Marco Antônio Záchia
author2Str	Pereira, Gabriela Feix Rech, Rosane Ayub, Marco Antônio Záchia
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doi_str	10.1007/s10295-015-1661-2
up_date	2024-07-04T01:50:13.235Z
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The modeling of ethanol production by Kluyveromyces marxianus using whey as substrate in continuous A-Stat bioreactors

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