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Discrimination between ethanol inhibition models in a continuous alcoholic fermentation process using flocculating

S C Oliveira1, T C Paiva, A E Visconti

  • 1Departmento de Biotecnologia, Faculdade de Engenharia Química de Lorena, Brazil.

Applied Biochemistry and Biotechnology
|March 3, 1999
PubMed
Summary

This study evaluated models for ethanol inhibition in continuous alcoholic fermentation. A power-law model best described yeast growth, while a linear model fit product formation.

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Area of Science:

  • Biochemical Engineering
  • Fermentation Science
  • Process Modeling

Background:

  • Ethanol production via fermentation is sensitive to ethanol's inhibitory effects on yeast.
  • Accurate modeling is crucial for optimizing continuous fermentation processes, especially in tower reactors with cell recycling.

Purpose of the Study:

  • To discriminate between various mathematical models describing ethanol's inhibitory effects on yeast growth and product formation.
  • To identify the most suitable kinetic models for yeast growth and fermentation under specific continuous process conditions.

Main Methods:

  • Continuous alcoholic fermentation was conducted in a tower reactor with recycling of flocculating yeast cells.
  • Several kinetic models (linear, parabolic, hyperbolic, exponential, power-law) were tested to describe ethanol inhibition.

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  • Kinetic parameters were estimated using steady-state data at varying sugar concentrations (160-200 g/L), constant dilution rate (0.2 h-1), recycle ratio (13.6), and temperature (30°C).

    • Main Results:

    • A generalized nonlinear power-law model provided the best fit for yeast growth kinetics.
    • A linear model was identified as the most appropriate for describing product formation.
    • Both selected models yielded feasible kinetic parameters and satisfactorily reproduced process variable trends.

    Conclusions:

    • The power-law model effectively captures yeast growth inhibition by ethanol in this system.
    • The linear model accurately represents product formation under the studied conditions.
    • These validated models enhance the understanding and optimization of continuous alcoholic fermentation processes.