Modelling Fungal Fermentations for Enzyme Production

Mads Orla Albæk, Krist Gernaey, Morten S. Hansen, Stuart M. Stocks

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We have developed a process model of fungal fed-batch fermentations for enzyme production. In these processes, oxygen transfer rate is limiting and controls the substrate feeding rate. The model has been shown to describe cultivations of both Aspergillus oryzae and Trichoderma reesei strains in 550L stirred tank pilot plant reactors well. For each strain, 8 biological parameters are needed as well as a correlation of viscosity, as viscosity has a major influence on oxygen transfer. The parameters were measured averages of at least 9 batches for each strain. The model is successfully able to cover a wide range of process conditions (0.3-2 vvm of aeration, 0.2-10.0 kW/m3 of specific agitation power input, and 0.1-1.3 barg head space pressure). Uncertainty and sensitivity analysis have shown that the uncertainty of the model is mainly due to difficulties surrounding the estimation of the biological parameters and to a lesser degree the uncertainty of the viscosity and mass transfer correlations. Until now, the model has been applied to evaluation of energy efficiency at different process conditions and bioreactor designs. Our goal is to expand the model to cover both pilot plant and production scale so that the model may assist downscaling operations as well as production optimization and production planning. Further developments of the model will enable more advanced applications such as model based control and simulated process optimization.
Original languageEnglish
Publication date2012
Publication statusPublished - 2012
Event17th Nordic Process Control Workshop - Kongens Lyngby, Denmark
Duration: 25 Jan 201227 Jan 2012
Conference number: 17


Conference17th Nordic Process Control Workshop
CityKongens Lyngby
Internet address


  • Oxygen transfer
  • Aspergillus oryzae
  • Rheology
  • Model based control
  • Sensitivity analysis
  • Pilot plant bioreactor
  • Trichoderma reesei
  • Process model
  • Uncertainty analysis


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