By A. Johnson
Modelling and keep watch over of Biotechnological procedures includes the complaints of the overseas Federation of computerized Controls First Symposium on Modeling and keep an eye on of Biotechnological approaches held in Noordwijkerhout, The Netherlands, on December 11-13, 1985. The papers discover modeling and keep watch over of biotechnological techniques similar to fermentation and organic wastewater remedy.
This ebook includes 37 chapters divided into eleven sections and starts with a dialogue at the keep an eye on of fermentation methods; modeling of biotechnical methods; and alertness of size and estimation strategies to biotechnology. the next sections concentrate on adaptive keep watch over conception, functions of adaptive keep watch over, and keep an eye on and modeling of bioreactors. The reader is usually brought to dimension recommendations and sensors, with emphasis on pyrolysis mass spectrometry; quick bioelectrochemical tools; and a self-tuning controller for multiloop managed fed-batch fermentation. the remainder sections care for parameter identity and estimation; Kalman filtering strategies; optimization of creation procedures; modeling of microkinetics; and optimization thought.
This monograph should be of curiosity to researchers and practitioners within the box of biotechnology.
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Extra info for Modelling and Control of Biotechnological Processes: Proceedings of the 1st Ifac Symposium Noordwijkerhout, the Netherlands 11-13 Dec 1985
The EDS is rewritten as an ARMAX repre sentation and the unknown parameters are estimated using a least squares method. The sum criterion is reformulated in terms of output and control. Because the transformation of the original state equations into the ARMAX-representation is done analytically, the relationship between the para meters in the ARMAX-model and the original micro biological parameters can be established. An adaptive control law is formulated which is close to optimal with respect to the averaged sum criterion.
A pulse of toxic substance is i n t r o d u c e d after 1 day. Fig 2. shows clearly the s t a b i l i z a t i o n of the plant by the a d a p t i v e c o n t r o l l e r (while in open l o o p it is led to w a s h - o u t ) . define es = S* - S(t) d 2) It is w o r t h noting that the r e g u l a t o r a c h i e v e s a zero s t e a d y - s t a t e error even w i t h a varying input d i s t u r b a n c e S i n ( t ) , since the a l g o r i t h m includes a f e e d f o r ward action (see the s i m u l a t i o n s in the next s e c t i o n ) .
Adaptive control; biocontrol; equivalent discrete time system; nonlinear control systems; parameter estimation; time varying systems. INTRODUCTION calculated optimal control to compensate for these parameter uncertainties (Alvarez Gallegos and Alvarez Gallegos, 1982). In this. paper robustness of the control is achieved by applying - sub - optimal adaptive control to the fermentation process. Our objective is to control the system such that it follows some prespecified trajectory. An integral criterion is formulated, which should be minimized to achieve this goal.
Modelling and Control of Biotechnological Processes: Proceedings of the 1st Ifac Symposium Noordwijkerhout, the Netherlands 11-13 Dec 1985 by A. Johnson