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.
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Petre, E., Şendrescu, D., Selişteanu, D. (2011). Neural Networks Based Model Predictive Control for a Lactic Acid Production Bioprocess. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6884. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23866-6_41
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DOI: https://doi.org/10.1007/978-3-642-23866-6_41
Publisher Name: Springer, Berlin, Heidelberg
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