Predicting by-product gradients of baker’s yeast production at industrial scale: A practical simulation approach

Christopher Sarkizi Shams Hajian, Cees Haringa, Henk Noorman, Ralf Takors*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)
21 Downloads (Pure)


Scaling up bioprocesses is one of the most crucial steps in the commercialization of bioproducts. While it is known that concentration and shear rate gradients occur at larger scales, it is often too risky, if feasible at all, to conduct validation experiments at such scales. Using computational fluid dynamics equipped with mechanistic biochemical engineering knowledge of the process, it is possible to simulate such gradients. In this work, concentration profiles for the by-products of baker’s yeast production are investigated. By applying a mechanistic black-box model, concentration heterogeneities for oxygen, glucose, ethanol, and carbon dioxide are evaluated. The results suggest that, although at low concentrations, ethanol is consumed in more than 90% of the tank volume, which prevents cell starvation, even when glucose is virtually depleted. Moreover, long exposure to high dissolved carbon dioxide levels is predicted. Two biomass concentrations, i.e., 10 and 25 g/L, are considered where, in the former, ethanol production is solely because of overflow metabolism while, in the latter, 10% of the ethanol formation is due to dissolved oxygen limitation. This method facilitates the prediction of the living conditions of the microorganism and its utilization to address the limitations via change of strain or bioreactor design or operation conditions. The outcome can also be of value to design a representative scale-down reactor to facilitate strain studies.

Original languageEnglish
Article number1554
Pages (from-to)1-19
Number of pages19
Issue number12
Publication statusPublished - 2020


  • Bioprocess engineering
  • Bioreactor
  • Computational fluid dynamics
  • Concentration gradients
  • Digital twin
  • Mechanistic kinetic model
  • Saccharomyces cerevisiae
  • Scale-down
  • Scale-up


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