(488b) State Estimation of Energy Integrated Systems with Time-Scale Multiplicity
To demonstrate the effectiveness of the proposed framework, two case studies with time-scale multiplicity are addressed: (i) a chemical reactor system  as a motivating example. In particular, this example illustrates the challenges on the implementation of previously proposed state estimation techniques for low-dimensional systems with time-scale multiplicity; and (ii) an energy-integrated system that corresponds to a reactor-feed effluent heat exchanger (FEHE) network . In addition to the presence of multiple time scales, the FEHE system is also high dimensional with more than 2,000 state variables, which introduces additional challenges to the proposed approach. To address these challenges, a framework with multiple extended Kalman filters (EKFs) for different time scales will be initially presented. Then, an implementation strategy with extended EKFs for the fast time scale and moving horizon estimators (MHEs) [6,7] for the slow time scale will be discussed.
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