NMPC of an industrial crystallization process using model-based observers

Abstract : This paper illustrates the benefits of a nonlinear model-based predictive control (NMPC) approach applied to an industrial crystallization process. This relevant approach proposes a setpoint tracking of the crystal mass. The controlled variable, unavailable, is obtained using an extended Luenberger observer. A neural network 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. The performances of this strategy are demonstrated via simulation in cases of setpoint tracking and disturbance rejection. The results reveal a significant improvement in terms of robustness and energy efficiency.
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Journal of Industrial and Engineering Chemistry, Elsevier, 2010, 16 (5), pp.708--716. 〈10.1016/j.jiec.2010.07.014〉
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http://hal.univ-reunion.fr/hal-01202295
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Soumis le : samedi 19 septembre 2015 - 16:46:30
Dernière modification le : jeudi 11 janvier 2018 - 06:28:13

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Cédric Damour, Michel Benne, Lionel Boillereaux, Brigitte Grondin-Perez, Jean-Pierre Chabriat. NMPC of an industrial crystallization process using model-based observers. Journal of Industrial and Engineering Chemistry, Elsevier, 2010, 16 (5), pp.708--716. 〈10.1016/j.jiec.2010.07.014〉. 〈hal-01202295〉

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