NMPC of an industrial crystallization process using model-based observers
详细信息 查看全文
- 作者：Cé ; dric Damour ; Michel Benne ; Lionel Boillereaux ; Brigitte Grondin-Perez ; Jean-Pierre Chabriat
- 关键词：Crystallization ; Nonlinear model predictive control ; Industrial control ; Extended Luenberger observer ; Artificial neural network
- 刊名：Journal of Industrial and Engineering Chemistry
- 出版年：2010
- 出版时间：25 September 2010
- 年：2010
- 卷：16
- 期：5
- 页码：708-716
- 全文大小：388 K
文摘
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.