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Journal articles
Nonlinear predictive control based on artificial neural network model for industrial crystallization
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.
https://hal.univ-reunion.fr/hal-01202296
Contributor : Réunion Univ Connect in order to contact the contributor
Submitted on : Saturday, September 19, 2015 - 4:46:31 PM
Last modification on : Tuesday, October 19, 2021 - 5:56:11 PM
Contributor : Réunion Univ Connect in order to contact the contributor
Submitted on : Saturday, September 19, 2015 - 4:46:31 PM
Last modification on : Tuesday, October 19, 2021 - 5:56:11 PM