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• W2557550002 abstract "Growing complexity of processes necessitates the use of information from sensors along with first-principles mathematical models to ensure safe and optimal operations. Use of sensors in complex processes requires identifying optimal location of sensors that can maximize information from a process. Classical sensor placement approaches for nonlinear systems that use state estimation schemes usually incorporate linearized models around the steady-state operating point. However, such approaches face difficulties when abnormalities or disturbances drift the system away from the normal operating point. Therefore, use of models that can appropriately track the behavior of the system in the sensor placement framework are of interest. However, the computational complexity of the detailed models makes such approaches intractable. In this work, we develop a sensor placement framework that combines genetic algorithms and the extended Kalman filter to obtain optimal sensor locations. Within this framework, we have in..." @default.
• W2557550002 created "2016-12-08" @default.
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• W2557550002 date "2016-12-20" @default.
• W2557550002 modified "2023-09-26" @default.
• W2557550002 title "State and Parameter Estimation in Distributed Constrained Systems. 2. GA-EKF Based Sensor Placement for a Water Gas Shift Reactor" @default.
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• W2557550002 doi "https://doi.org/10.1021/acs.iecr.6b02797" @default.
• W2557550002 hasPublicationYear "2016" @default.
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