Constrained Hybrid Neural Modelling of Biotechnological Processes

Asma Karama 1 Olivier Bernard 2 Jean-Luc Gouzé 2
2 COMORE - Modeling and control of renewable resources
LOV - Laboratoire d'océanographie de Villefranche, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : We propose a general methodology to develop a hybrid neural model for a wide range of biotechnological processes. The hybrid neural modelling approach combines the flexibility of a neural network representation of unknown process kinetics with a global mass-balance based process description. The hybrid model is built in such a way that its trajectories keep their physical and biological meaning (mass balance, positivity of the concentrations, boundness, saturation or inhibition of kinetics) even far from the identification data conditions. We examine the constraints (a priori knowledge) that must be satisfied by the model and that provide additional conditions to be imposed on the neural network. We illustrate our approach with various biotechnological processes showing how to select the appropriate neural network architecture. The method is detailed for modelling an anaerobic wastewater treatment bioreactor using experimental data.
Mots-clés : NEURNET MOD WWT IDENT BPSS
Type de document :
Article dans une revue
International Journal of Chemical Reactor Engineering, De Gruyter, 2010, 8, pp.A21. 〈10.2202/1542-6580.2117〉
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Contributeur : Jean-Luc Gouzé <>
Soumis le : mardi 23 juillet 2013 - 11:22:55
Dernière modification le : lundi 17 décembre 2018 - 01:26:36

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Asma Karama, Olivier Bernard, Jean-Luc Gouzé. Constrained Hybrid Neural Modelling of Biotechnological Processes. International Journal of Chemical Reactor Engineering, De Gruyter, 2010, 8, pp.A21. 〈10.2202/1542-6580.2117〉. 〈hal-00847289〉

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