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This document compares the Extended Kalman Filter (EKF) and Multivariable Adaptive Estimator (MAE) for estimating state variables in biotechnological processes involving Saccharomyces cerevisiae. The EKF provides superior global estimations of biomass, substrate, and product, while the MAE is effective for biomass estimation. The study utilizes a dynamic physiological model to simulate the growth of yeast under various conditions and assess the performance of both estimation techniques.

Author

Carlos Araújo Queiroz

Language

EN