Abstract
A data-driven model is presented that can serve two important purposes. First, the specific growth rate and the specific product formation rate are determined as a function of time and thus the dependency of the specific product formation rate from the specific biomass growth rate. The results appear in form of trained artificial neural networks from which concrete values can easily be computed. The second purpose is using these results for online estimation of current values for the most important state variables of the fermentation process. One only needs online data of the total carbon dioxide production rate (tCPR) produced and an initial value x of the biomass, i.e., the size of the inoculum, for model evaluation. Hence, given the inoculum size and online values of tCPR, the model can directly be employed as a softsensor for the actual value of the biomass, the product mass as well as the specific biomass growth rate and the specific product formation rate. In this paper the method is applied to fermentation experiments on the laboratory scale with an E. coli strain producing a recombinant protein that appears in form of inclusion bodies within the cells’ cytoplasm.
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Acknowledgments
The financial support from the “Ministry of Cultural Affairs” of Sachsen-Anhalt, Germany, is gratefully acknowledged.
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Gnoth, S., Jenzsch, M., Simutis, R. et al. Product formation kinetics in genetically modified E. coli bacteria: inclusion body formation. Bioprocess Biosyst Eng 31, 41–46 (2008). https://doi.org/10.1007/s00449-007-0161-9
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DOI: https://doi.org/10.1007/s00449-007-0161-9