Multi-rate data fusion for state and parameter estimation in (Bio-)chemical process engineering - Dürr, Robert; Duvigneau, Stefanie; Seidel, Carsten; Kienle, Achim; Bück, Andreas (Processes, 2021)
For efficient operation, modern control approaches for biochemical process engineering require information on the states of the process such as temperature, humidity or chemical composi-tion. Those measurement are gathered from a set of sensors which differ with respect to sampling rates and measurement quality. Furthermore, for biochemical processes in particular, analysis of physical samples is necessary, e.g., to infer cellular composition resulting in delayed information. As an alternative for the use of this delayed measurement for control, so-called soft-sensor approaches can be used to fuse delayed multirate measurements with the help of a mathematical process model and provide information on the current state of the process. In this manuscript we present a complete methodology based on cascaded unscented Kalman filters for state estimation from delayed and multi-rate measurements. The approach is demonstrated for two examples, an exothermic chemical reactor and a recently developed model for biopolymer production. The results indicate that the the current state of the systems can be accurately reconstructed and therefore represent a promising tool for further application in advanced model-based control not only of the considered processes but also of related processes.
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Keywords: Bayesian estimation; Model identifi-cation; Multisensor data fusion; Unscented Kalman filtering