Neural networks based model predictive control for a lactic acid production bioprocess
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
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This paper presents the design and the analysis of an indirect adaptive control strategy for a lactic acid production that is carried out in two cascaded continuous stirred tank bioreactors. The indirect adaptive control structure is based on the nonlinear process model and is derived by combining a linearizing control law with a new parameter estimator, which plays the role of the software sensor for on-line estimation of the bioprocess unknown kinetics. The behaviour and performance of both estimation and control algorithms are illustrated by simulations applied in the case of a lactic fermentation bioprocess for which kinetic dynamics are strongly nonlinear, time-varying and completely unknown.