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Monitoring and control of a class of distributed parameter bioreactors with application of the substrate consumption rate

Czeczot J., Metzger M., Babary J. P., Nihtilä M.

Archives of Control Sciences

2001

Vol. 11, no. 1/2

5-22

This paper deals with the idea of estimation of the substrate consumption rate at each point of the tube of the classical distributed parameter bioreactor and with its application to the monitoring and control of this system. It is shown how to approximate the profile of this parameter on the basis of the orthogonal collocation method and of the recursive least-squares procedure with adjustable forgetting factor. Then, it is suggested how to apply this profile for monitoring of the bioreactor work (calculation of the current mass flux of the substrate being reacted in the reactor tube and of the total mass of the substrate converted in the bioreactor during its activity). Finally, the linearizing adaptive control law is derived on the basis of the substrate consumption rate estimated at the outlet of the bioreactor. This controller allows the outlet substrate concentration to be kept at its desired value by manipulating the velocity of the flowing medium. The idea presented in this paper has been validated by means of the computer simulation and the results, proving its very good properties, complete the paper.

Linearising control via FEM scheme of a distributed parameter bioreactor

Nihtila M., Tervo J., Kaipio J. P., Babary J. P.

Archives of Control Sciences

1999

Vol. 9, no. 3/4

135-156

Control of a distributed parameter bioreactor model is studied via linearisations and numerical approximations. Galerkin's finite element method (FEM) is applied to approximate the original nonlinear partial differential equation (PDE) model by an ordinary differential equation (ODE) system. Then linearising control which tracks a given reference is applied in the ODE system. Controllability of the original PDE system and the approximate ODE model are studied via physical considerations and linearisation pointing out a difference in the controllability properties of the two models. Some simulation results based on the FEM approximation by using realistic model parameter values illustrate feasibility of the joint FEM approximation and linearising control technique.