Neural networks based model predictive control for a lactic acid production bioprocess

Authors:
Emil Petre;Dorin Şendrescu;Dan Selişteanu
Affiliations:
Department of Automatic Control, University of Craiova, Craiova, Romania;Department of Automatic Control, University of Craiova, Craiova, Romania;Department of Automatic Control, University of Craiova, Craiova, Romania
Venue:
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
Year:
2011

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Abstract

This work deals with the design and analysis of a nonlinear model predictive control (NMPC) strategy for a lactic acid production that is carried out in two continuous stirred bioreactors sequentially connected. The adaptive NMPC control structure is based on a dynamical neural network used as on-line approximator to learn the time-varying characteristics of process parameters. Minimization of a cost function depending on control inputs is realised using the Levenberg-Marquardt numerical optimisation method. The effectiveness and performance of the proposed control strategy is illustrated by numerical simulations applied in the case of a lactic fermentation bioprocess for which kinetic dynamics are strongly nonlinear, time varying and completely unknown.