Abstract
A systematic approach for system identification is applied to experimental data of ethanol production from cellulose. Special attention is given to the identification of model parameters, which can be reliably estimated from available measurements. For this purpose, an identifiable parameter subset selection algorithm for nonlinear least squares parameter estimation is used. The procedure determines the parameters whose effects are unique and have a strong effect on the predicted (measurement variables) output variables. The system is described by a generic process model for the simultaneous saccharification and fermentation including three enzyme-catalyzed reactions. The process model is clearly over-parameterized. By applying the subset selection approach the parameter space is reduced to a reasonable subset, whose estimated parameters are still able to predict the experimental data accurately.