Authors:
V. E. Ntampasi
and
O. I. Kosmidou
Affiliation:
Democritus University of Thrace, Greece
Keyword(s):
Robust Model Predictive Control (RMPC), Uncertain Systems, Bioreactor, Bioprocess Control.
Related
Ontology
Subjects/Areas/Topics:
Engineering Applications
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Modeling
;
Systems Modeling and Simulation
Abstract:
Biotechnology industry is expanded rapidly due to the progress in the understanding of bio-systems and the
increased demand for products. Since bioprocess dynamics are almost always affected by physical parameter
variations and external disturbances, the need for robust control techniques is of major importance in order
to ensure the desired behavior of the process. The overall process equilibrium is guaranteed if all quantities
in the bioreactor remain into prescribed ranges. In recent years, closed-loop control methods have been used
in order to cope with uncertainty and an important number of constraints imposed by the physical system.
For this purpose, predictive control is a quite promising technique. In the present paper three robust model
predictive control (RMPC) techniques are used in order to regulate the substrate concentration and the biomass
production in a bioreactor. These techniques are applied to a continuous bioreactor in which the pH changes
are considered as distu
rbances while the air pressure is ignored by the process model. For the simulation
purposes a linearized model of the system has been used in which the uncertainty is described in the form of
a disturbance term. The effectiveness of the methods is illustrated by means of simulation results.
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