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

Cédric Damour 1 Michel Benne 1 Brigitte Grondin-Perez 1 Jean-Pierre Chabriat 1 
1 ENERGY Lab - LE2P - Energy Lab - Laboratoire d'Energétique, d'Electronique et Procédés
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.
Keywords : Artificial neural network Crystallization Heat and mass balance Industrial processes optimization Nonlinear model predictive control
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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, January 28, 2022 - 3:27:28 AM

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AFRIQ | UNIV-REUNION | LE2P | FST-REUNION

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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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