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On‐Line State Estimation and Parameter Identification for Batch Fermentation
Author(s) -
Gee Douglas A.,
Ramirez W. Fred
Publication year - 1996
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1021/bp950049m
Subject(s) - extended kalman filter , identification (biology) , estimation theory , kalman filter , computer science , process (computing) , state (computer science) , system identification , control theory (sociology) , batch processing , model parameter , line (geometry) , algorithm , mathematics , data mining , artificial intelligence , control (management) , botany , geometry , programming language , biology , operating system , measure (data warehouse)
A sequential state and parameter identification technique is applied to batch beverage fermentation. The algorithm uses an extended Kalman filter (EKF) for state estimation and a recursive prediction error method (RPEM) for model parameter identification. This adaptive algorithm is tested using both simulated and real process data. Results show that, even with imperfect models, both states and parameters are estimated well enough to reliably track the dynamics of the true system.