Improved efficiency in sensitivity calculations for bioreactor models

Sensitivity functions are a basic tool in the parametric identification of models. They can be used to obtain the gradient needed by optimization algorithms in the estimation of parameters and to calculate the Fisher information matrix in experimental design. In addition to systems identification, sensitivity functions are also used in robustness analysis, robust design, and robust control for batch and semi-batch processes. This paper shows that, for certain types of bioreactor models, there is a fixed relation between certain sensitivity functions. This property allows the computational load of the numerical calculation of the sensitivity trajectories to be reduced.