Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
229215 | Journal of Industrial and Engineering Chemistry | 2010 | 9 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Chemical Engineering (General)
Authors
Cédric Damour, Michel Benne, Lionel Boillereaux, Brigitte Grondin-Perez, Jean-Pierre Chabriat,