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Estimation of second‐order chemical kinetic parameters by using information‐based extended Kalman filtering
Author(s) -
Barker Todd Q.,
Brown Steven D.
Publication year - 1988
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180020206
Subject(s) - kalman filter , filter (signal processing) , computer science , convergence (economics) , extended kalman filter , rate of convergence , reduction (mathematics) , algorithm , mathematics , control theory (sociology) , artificial intelligence , computer vision , computer network , channel (broadcasting) , geometry , economics , economic growth , control (management)
Abstract The extended Kalman filter has been used to estimate initial reactant concentrations and rate constants for rate‐based chemical assays employing a second‐order chemical reaction. Application of first‐ and second‐order models to data permits reaction order identification by examining either the filter innovations or the evolution of the filter states. Because of non‐linearities in the second‐order kinetic model, repetitive filtering is necessary for convergence to reliable state estimates. Reduction of the filter calculation burden is investigated through the use of information‐based filter methods, and it is demonstrated that substantial decreases in the computational burden are possible without loss of filter accuracy. These decreases make possible the application of second‐order filters on large data sets, and they make real‐time filtering possible with a fast processor.