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Application of modern estimation and identification techniques to chemical processes
Author(s) -
Wells Charles H.
Publication year - 1971
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690170433
Subject(s) - kalman filter , extended kalman filter , estimator , computation , nonlinear system , computer science , line (geometry) , process (computing) , chemical reactor , identification (biology) , estimation theory , control theory (sociology) , chemical process , moving horizon estimation , system identification , control engineering , process control , algorithm , engineering , control (management) , mathematics , data mining , artificial intelligence , biology , operating system , geometry , quantum mechanics , measure (data warehouse) , statistics , physics , botany , chemical engineering
This paper describes a new application of modern estimation theory to nonlinear chemical processes. A particularly convenient form of the extended Kalman estimator is presented and its applications are discussed. The method described may be used to compute nonmeasureable process states and system parameters in real time with an on‐line process control computer. An application of the extended Kalman filter to a six‐dimensional nonlinear well‐stirred reactor is discussed in detail. The results clearly indicate the feasibility of on‐line application of this technique. In some cases these computations could eliminate the requirement for an on‐line analysis.