Title :
Design of a neural network observer to control a FED-batch bioprocess
Author :
Boutalis, Y.S. ; Kosmidou, O.I.
Author_Institution :
Dept. of Electr. & Comput. Eng., Democritus Univ. of Thrace, Xanthi, Greece
fDate :
Aug. 31 1999-Sept. 3 1999
Abstract :
The present paper deals with the production of Saccharomyces cerevisiae, described by a sixth order nonlinear state space model. The control objective is to ensure the process stability and desirable specifications in the presence of disturbances and lack of reliable state measurements. First, the model of the process and its properties are presented. Next the ability of multi-layer Neural Networks to act as reliable emulators of the system dynamics is tested by simulation results and a training strategy is proposed to improve their performance. Finally, a non-linear adaptive observer is designed by means of artificial neural networks.
Keywords :
adaptive control; batch processing (industrial); microorganisms; multilayers; neurocontrollers; nonlinear control systems; stability; state-space methods; FED-batch bioprocess control; Saccharomyces cerevisiae; artificial neural network; control objective; multilayer neural networks; neural network observer; nonlinear adaptive observer; process stability; sixth order nonlinear state space model; state measurement; system dynamics; training strategy; Artificial neural networks; Mathematical model; Observers; Reliability; Sugar; Training; Bioprocess; Neural Networks; State Observer;
Conference_Titel :
Control Conference (ECC), 1999 European
Conference_Location :
Karlsruhe
Print_ISBN :
978-3-9524173-5-5
