Biotechnological processes still represent a challenge for process optimization and automation as the data landscape consists of unavailable, inaccurate, delayed or missing measurement information. As a first step towards automation of biotechnological processes, methods have to be refined for estimating the unknown states with an acceptable precision, using a mathematical model of the system. Due to the technological advances, knowledge and computational powers are constantly increasing so that models of a higher complexity and predictive quality are now available. Hybrid cybernetic models offer a flexible, yet detailed description of the biotechnological process under consideration. They connect the nonlinear system dynamics to the metabolic information of the organism and allow to consider cell internal regulations. In this work we explore if this class of models can be successfully applied for realtime process monitoring. We do this by evaluating the performance of two commonly used state estimators, an unscented Kalman filter and a moving horizon estimator, which both use a hybrid cybernetic model to observe the non-linear process of poly-β-hydroxybutyrate production in the organismCupriavidus necator. To our knowledge this is the first time that this class of models is used for model-based process observation.
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