Model-based determination of changing kinetics in high cell density cultures using respiration data

During high cell density cultivations, very low growth rates and changes in cell metabolism occur. These have to be accounted for in the kinetic modelling. In this work, an optimisation-based approach is presented which recognises the switching to new parameters at a certain growth rate and thereby improves the quality of the model prediction for different time horizon lengths. For the dynamic automatic adjustment to changing kinetics, a moving horizon estimator (MHE) is applied. Experimental data from cultivations of Ustilago maydis are used for the model-based parameter identification. To validate the method, initially offline data are utilised. In the next step, respiration data, which are available online, are used to enable real-time monitoring. The embedded MHE was successfully applied to predict changes in biokinetic constants during membrane bioreactor (MBR) fermentation. Setting suited horizon lengths and parameter bounds was found to be crucial for convergence and parameter estimation. The expected drop in maintenance parameters at low growth rates was confirmed when using an optimum number of data points.