Article ID Journal Published Year Pages File Type
224081 Journal of Food Engineering 2010 7 Pages PDF
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.

Related Topics
Physical Sciences and Engineering Chemical Engineering Chemical Engineering (General)
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