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On the use of detailed models in the MPC algorithm: The pressure-swing distillation case



A novel approach to the design of a model predictive control (MPC) algorithm for a complex plant (with energy integration and mass recycles) is given. Sensitivity analysis and steady-state optimization are used to determine the manipulated variables that have the strongest influence on the objective function of the operation. This allows a reduction of the number of variables that are optimized on-line, as well as the use of detailed, first-principle–based models in the MPC algorithm, thus resulting in more reliable predictions. Moreover, the same algorithm can be used to control plants of different size, without the need of a new calibration of the parameters of the model. The application of this procedure to a pressure-swing distillation unit is given as an example. © 2006 American Institute of Chemical Engineers AIChE J, 2006