Abstract
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 language | English |
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Publication date | 2012 |
Publication status | Published - 2012 |
Event | 17th Nordic Process Control Workshop - Kongens Lyngby, Denmark Duration: 25 Jan 2012 → 27 Jan 2012 Conference number: 17 http://npcw17.imm.dtu.dk/ |
Conference
Conference | 17th Nordic Process Control Workshop |
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Number | 17 |
Country | Denmark |
City | Kongens Lyngby |
Period | 25/01/2012 → 27/01/2012 |
Internet address |
Keywords
- Oxygen transfer
- Aspergillus oryzae
- Rheology
- Model based control
- Sensitivity analysis
- Pilot plant bioreactor
- Trichoderma reesei
- Process model
- Uncertainty analysis