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• W3201540369 endingPage "107544" @default.
• W3201540369 startingPage "107544" @default.
• W3201540369 abstract "Adequate frequent information on state variables of a process is sometimes needed for effective control and monitoring of the process. However, it is not often available in practice, which can be addressed using a state estimator. This work deals with distributed state estimation in large-scale processes. The decomposition of a process into observable subsystems is formulated as an optimization problem, which is solved using an efficient whale optimization algorithm. Four nonlinear state estimation methods (extended Kalman, unscented Kalman, spherical unscented Kalman, and cubature Kalman filtering) are then implemented and compared using distributed and centralized architectures on a process consisting of two reactors and a separator, and the Tennessee Eastman process. A parallelization strategy that improves the computational efficiency of the distributed architecture is proposed. Simulation results show that the parallel implementation of the distributed filtering methods is computationally more efficient than their centralized counterparts while yielding similarly accurate state estimates." @default.
• W3201540369 created "2021-09-27" @default.
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• W3201540369 date "2022-01-01" @default.
• W3201540369 modified "2023-10-16" @default.
• W3201540369 title "Distributed state estimation in large-scale processes decomposed into observable subsystems using community detection" @default.
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• W3201540369 doi "https://doi.org/10.1016/j.compchemeng.2021.107544" @default.
• W3201540369 hasPublicationYear "2022" @default.
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