화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Journal of Industrial and Engineering Chemistry, Vol.16, No.5, 708-716, September, 2010
DOI10.1016/j.jiec.2010.07.014  Export Citation
NMPC of an industrial crystallization process using model-based observers
Cedric Damour, Michel Benne†, Lionel Boillereaux1, Brigitte Grondin-Perez, and Jean-Pierre Chabriat
  • Lab. of Energetic, Electronics and Processes, University of La Reunion, 15 Av. Cassin, BP 7151, 97715 Saint-Denis, France
  • 1GEPEA, UMR CNRS 6144, ENITIAA, rue de la Geraudiere, BP 82225, F-44322 Nantes cedex 3, France
E-mail:
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 problemis 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.
Keywords:Crystallization;Nonlinear model predictive control;Industrial control;Extended Luenberger observer;Artificial neural network
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