Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
224081 | Journal of Food Engineering | 2010 | 7 Pages |
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
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Chemical Engineering (General)
Authors
Cédric Damour, Michel Benne, Brigitte Grondin-Perez, Jean-Pierre Chabriat,