Modeling of Batch Alcohol Fermentation with Free and Immobilized Yeasts<i>Saccharomyces Cerevisiae</i>46 EVD

Kostov, Georgi; Popova, Silviya; Gochev, Velizar; Koprinkova–Hristova, Petia; Angelov, Mihail; Georgieva, Atanaska

doi:10.5504/bbeq.2012.0025

Cited by 28 publications

(45 citation statements)

References 18 publications

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“…However, it is not easy to choose a single best fitting. In order to choose the best model it is import ant to consider how well it describes the transition from ex ponential t o stationary phase of t he process model (Kostov et al, 2012).…”

Section: Resultsmentioning

confidence: 99%

Kinetic models and parameters estimation study of biomass and ethanol production from inulin by Pichia caribbica (KC977491)

Ali¹,

Hiligsmann²,

Outili³

et al. 2017

Afr. J. Biotechnol.

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The growth kinetics and modeling of ethanol production from inulin by Pichia caribbica (KC977491) were studied in a batch system. Unstructured models were proposed using the logistic equation for growth, the Luedeking-Piret equation for ethanol production and modified Leudeking-Piret model for substrate consumption. Kinetic parameters (X 0 , μ m , m, n, p and q) were determined by nonlinear regression, using Levenberg-Marquart method implemented in a Mathcad program. Since the production of ethanol was associated with P. caribbica cell growth, a good agreement between model predictions and experimental data was obtained. Indeed, significant R 2 values of 0.91, 0.96, and 0.95 were observed for biomass, ethanol production and substrate consumption, respectively. Furthermore, analysi s of variance (ANOVA) w as also used to validate the proposed models. According to the obtained results, the predicted kinetic values and experimental data agreed well . Finally, it is possible to predict the development of P. caribbica using these models.

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Section: Resultsmentioning

confidence: 99%

Kinetic models and parameters estimation study of biomass and ethanol production from inulin by Pichia caribbica (KC977491)

Ali¹,

Hiligsmann²,

Outili³

et al. 2017

Afr. J. Biotechnol.

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show abstract

“…An additional β-term for retained biomass introduced in the differential equation for product, which is adjusted with an effectiveness factor η P [35][36][37] that stands for the relative difference between non-growth associated production of lactate from free and retained cells. Finally, the model to describe fixed-bed cultivations consists of the following set of differential equations:…”

Section: Model Development For Biomass Substrate and Productmentioning

confidence: 99%

“…For this, some authors have employed different interpretations of the dimensionless, internal effectiveness factor η, which ranges from 0 to 1 and was originally postulated by Thiele to describe reaction rates affected by diffusion limitations within carriers. Up to date, this factor has successfully been used for immobilized ethanol fermentations with yeasts wherein η was used in different works to describe the efficiency of the immobilized biocatalyst versus free biocatalyst with respect to biomass formation [35], substrate consumption [36] and ethanol production [35,37].…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation and Reaction Kinetics Modeling of Biomass Formation, Substrate Consumption and Product Formation During Startup of Fixed-Bed Cultures with Immobilized Lactococcus lactis ssp. lactis

Faschian¹,

Minden²,

Pörtner³

2016

J Bioprocess Biotech

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“…The operating conditions were 1.0 mL/min flow rate, 30 C temperature, 210 nm wavelength and 10 mL injection volume. The metabolite product yield coefficients (Yp/s, mg/g) were calculated as follows [24]:…”

Section: Metabolite Analysismentioning

confidence: 99%

Acetate metabolism ofSaccharomyces cerevisiaeat different temperatures during lychee wine fermentation

Shang

Zeng

Zhu

et al. 2016

Biotechnology & Biotechnological Equipment

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The yeast (Saccharomyces cerevisiae) strain 2137 involved in lychee wine production was used to investigate acetate metabolism at different temperatures during lychee wine fermentation. Fermentation tests were conducted using lychee juice supplemented with acetic acid. The ability of yeast cells to metabolize acetic acid was stronger at 20 C than at 25 C or 30 C. The addition of acetic acid suppressed the yeast cell growth at the tested temperatures. The viability was higher and the reactive oxygen species concentration was lower at 20 C than at 30 C; this result indicated that acid stress adaptation protects S. cerevisiae from acetic-acid-mediated programmed cell death. The acetic acid enhanced the thermal death of yeast at high temperatures. The fermentation temperature modified the metabolism of the yeasts and the activity of related enzymes during deacidification, because less acetaldehyde, less glycerol, more ethanol, more succinic acid and more malic acid were produced, with higher level of acetylÀCoA synthetase and isocitrate lyase activity, at 20 C.

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Modeling of Batch Alcohol Fermentation with Free and Immobilized YeastsSaccharomyces Cerevisiae46 EVD

Cited by 28 publications

References 18 publications

Kinetic models and parameters estimation study of biomass and ethanol production from inulin by Pichia caribbica (KC977491)

Kinetic models and parameters estimation study of biomass and ethanol production from inulin by Pichia caribbica (KC977491)

Experimental Investigation and Reaction Kinetics Modeling of Biomass Formation, Substrate Consumption and Product Formation During Startup of Fixed-Bed Cultures with Immobilized Lactococcus lactis ssp. lactis

Acetate metabolism ofSaccharomyces cerevisiaeat different temperatures during lychee wine fermentation

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