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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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https://hal.univ-reunion.fr/hal-01202296
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Submitted on : Saturday, September 19, 2015 - 4:46:31 PM
Last modification on : Friday, November 22, 2019 - 12:08:02 PM

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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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