(444f) On-Line States And Parameter Identification Of A Membrane Bioreactor

During high-cell-density cultivations, which are becoming increasingly popular in biotechnology and wastewater treatment, very low growth rates and changes in cell metabolism occur [1]. The emerging phenomena cannot be sufficiently described by kinetic models used during earlier phases in the process when growth rates were higher. On the other hand, knowledge on near zero-growth conditions is scarce. Therefore, model-based monitoring and control requires switching to new parameters or even to a different model at a certain growth rate [2]. Growth rate, however, is a value which cannot be determined directly online. A model-based identification approach utilising online data is thus needed [3].

In this work, novel numerical strategies are presented which recognise the switching time and improve the quality of model prediction for different time horizon lengths. Here, an optimization-based approach to improving the predictivity of kinetic models based on available measurements together with a process model is proposed. For the dynamic adjustment of the changing kinetics, a moving horizon estimator MHE is used. Based on online respiration data, the varying kinetic parameters are determined to increase the predictivity of long-term limited cultures. In addition, experimental data from cultivation of Ustilago maydis are also used for model-based parameter identification.

[1] Ihssen J, Egli T. 2004. Specific growth rate and not cell density controls the general stress response in Escherichia coli. Microbiol 150:1637-1648.

[2] Drews A, Kraume M. 2005. Process Improvement by Application of Membrane Bioreactors. Trans IChemE, Chem Eng Res Des 83(A3):276-284.

[3] Sun Z, Ramsay JA, Guay M, Ramsay BA. 2006. Automated feeding strategies for high-cell-density fed-batch cultivation of Pseudomonas putida KT2440. Appl Microbiol Biotechnol 71:423-431.