Kinetics of Ethanol Fermentation with High Biomass Concentration Considering the Effect of Temperature

Atala, Daniel Ibraim Pires; Costa, Aline Carvalho da; Maciel, Rubens; Maugeri, Francisco

doi:10.1007/978-1-4612-0217-2_30

Cited by 21 publications

(39 citation statements)

References 12 publications

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“…At 39°C, the fermentation was sluggish with very low biomass (2 g), ethanol (43 g) and by-products (0.7 and 0.5 g succinic and acetic acid respectively) production, except for the glycerol production (11 g) that was enhanced. Fermentations at temperature above 35°C were already found very restrictive for growth and ethanol production in yeast cultivated on white must in aerobic static bottles [5], in batch cultures on wheat mashes [1], and in continuous mode with total cell recycling [4].…”

Section: Impact Of Temperature On Fermentation Parametersmentioning

confidence: 99%

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Synergistic temperature and ethanol effect on Saccharomyces cerevisiae dynamic behaviour in ethanol bio-fuel production

Aldiguier

Alfenore

Cameleyre

et al. 2004

Bioprocess Biosyst Eng

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The impact of ethanol and temperature on the dynamic behaviour of Saccharomyces cerevisiae in ethanol biofuel production was studied using an isothermal fed-batch process at five different temperatures. Fermentation parameters and kinetics were quantified. The best performances were found at 30 and 33 degrees C around 120 g l(-1) ethanol produced in 30 h with a slight benefit for growth at 30 degrees C and for ethanol production at 33 degrees C. Glycerol formation, enhanced with increasing temperatures, was coupled with growth for all fermentations; whereas, a decoupling phenomenon occurred at 36 and 39 degrees C pointing out a possible role of glycerol in yeast thermal protection.

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Section: Impact Of Temperature On Fermentation Parametersmentioning

confidence: 99%

“…Very high ethanol performances in fermentation using Saccharomyces cerevisiae are determined by several factors such as medium composition and operating parameters including substrate and vitamin feeding strategy, oxygen level and temperature [1,2,3,4]. This latter parameter becomes crucial for optimising low-added product bioprocesses.…”

Section: Introductionmentioning

confidence: 99%

Synergistic temperature and ethanol effect on Saccharomyces cerevisiae dynamic behaviour in ethanol bio-fuel production

Aldiguier

Alfenore

Cameleyre

et al. 2004

Bioprocess Biosyst Eng

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“…Atala et al [21], modeling the effect of temperature upon the kinetics of ethanol fermentation with a high concentration of biomass in a continuous system with total cell retention, used an inhibitory factor (IF) of the exponential type to describe the inhibitory effect of the substrate upon the kinetics of cell growth, which was inserted in the expression of f(S), being f(S), in this case, given by the Monod equation.…”

Section: Effect Of Substrate Concentrationmentioning

confidence: 99%

“…Other aforementioned works in which the segregated approach, regarding cell viability, was applied to describe the microbial population are as follows: Kalil et al [54], Atala et al [21], Costa Filho et al [56], Nelson and Hamzah [53], and Watt et al [67].…”

Section: Loss Of Cell Viabilitymentioning

confidence: 99%

Kinetic Modeling of 1‐G Ethanol Fermentations

Oliveira¹,

Stremel²,

Dechechi³

et al. 2017

Fermentation Processes

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The most recent rise in demand for bioethanol, due mainly to economic and environmental issues, has required highly productive and efficient processes. In this sense, mathematical models play an important role in the design, optimization, and control of bioreactors for ethanol production. Such bioreactors are generally modeled by a set of first-order ordinary differential equations, which are derived from mass and energy balances over bioreactors. Complementary equations have also been included to describe fermentation kinetics, based on Monod equation with additional terms accounting for inhibition effects linked to the substrate, products, and biomass. In this chapter, a reasonable number of unstructured kinetic models of 1-G ethanol fermentations have been compiled and reviewed. Segregated models, as regards the physiological state of the biomass (cell viability), have also been reviewed, and it was found that some of the analyzed kinetic models are also applied to the modeling of second-generation ethanol production processes.

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“…Biomass concentration was determined by the dry weight method [13]. Ethanol content was determined by gas chromatography [14] and the fermentable sugars concentration was estimated using the 3,5-dinitrosalicylic acid method [15].…”

Section: Methodsmentioning

confidence: 99%

Influence of 1-butyl-3-methylimidazolium Chloride on the Ethanol Fermentation Process of Pichia pastoris GS115

Huang¹,

Tong²,

Huang³

et al. 2015

TOBIOTJ

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Kinetics of Ethanol Fermentation with High Biomass Concentration Considering the Effect of Temperature

Cited by 21 publications

References 12 publications

Synergistic temperature and ethanol effect on Saccharomyces cerevisiae dynamic behaviour in ethanol bio-fuel production

Synergistic temperature and ethanol effect on Saccharomyces cerevisiae dynamic behaviour in ethanol bio-fuel production

Kinetic Modeling of 1‐G Ethanol Fermentations

Influence of 1-butyl-3-methylimidazolium Chloride on the Ethanol Fermentation Process of Pichia pastoris GS115

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