Software Sensors Design for a Class of Aerobic Fermentation Processes
The problem of on-line state and parameter estimation (software sensors design) of a class of aerobic fermentation processes for metabolite product formation is considered. The class is characterized by: two limiting substrates one of which, growth factor, is practically depleted during the biomass...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Bulgarian Academy of Sciences
2010-08-01
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| Series: | International Journal Bioautomation |
| Subjects: | |
| Online Access: | http://www.clbme.bas.bg/bioautomation/2010/vol_14.2/files/14.2_02.pdf |
| Summary: | The problem of on-line state and parameter estimation (software sensors design) of a class of aerobic fermentation processes for metabolite product formation is considered. The class is characterized by: two limiting substrates one of which, growth factor, is practically depleted during the biomass growth where the product formation is negligible; corresponding general reaction scheme – a qualitative description of the main metabolic reactions between the main components in the liquid phase (biomass, substrates, product and dissolved oxygen concentrations). Two separate sensors – state and parameter estimators – are designed. The state estimator is developed based on knowledge of only one on-line measurable variable, the dissolved oxygen, and the yield factors assumed as constant coefficients. Parameter estimator of the specific reaction rates is developed under the assumption that all the process variables are known on-line by measurements or estimates. The yield factors are estimated also as non-stationary parameters, thus creating a basis for comparison with the specified constant values used for the state estimator design. As a case study industrial Lysine fermentation in fed-batch mode of operation is considered. Simulation investigations under different operating conditions are done in order to highlight the performances of the proposed sensors. |
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| ISSN: | 1314-1902 1314-2321 |