Nonlinear predictive control based on artificial neural network model for industrial crystallization

Abstract : This paper illustrates the benefits of a nonlinear model based predictive control (NMPC) strategy for setpoint tracking control of an industrial crystallization process. A neural networks model is used as internal model to predict process outputs. An optimization problem is solved to compute future control actions taking into account real-time control objectives. Furthermore, a more suitable output variable is used for process control: the mass of crystals in the solution is used instead of the traditional electrical conductivity. The performance of the NMPC implementation is assessed via simulation results based on industrial data.
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Journal of Food Engineering, Elsevier, 2010, 99 (2), pp.225--231. 〈10.1016/j.jfoodeng.2010.02.027〉
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http://hal.univ-reunion.fr/hal-01202296
Contributeur : Réunion Univ <>
Soumis le : samedi 19 septembre 2015 - 16:46:31
Dernière modification le : dimanche 5 novembre 2017 - 15:58:05

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Cédric Damour, Michel Benne, Brigitte Grondin-Perez, Jean-Pierre Chabriat. Nonlinear predictive control based on artificial neural network model for industrial crystallization. Journal of Food Engineering, Elsevier, 2010, 99 (2), pp.225--231. 〈10.1016/j.jfoodeng.2010.02.027〉. 〈hal-01202296〉

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