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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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Submitted on : Saturday, September 19, 2015 - 4:46:30 PM
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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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