The aim of this work is the design of a master controller for IGCC (Integrated Gasification Combine Cycle) plant, based on an MPC (Model Predictive Control) approach, which is able to coordinate the main process variables interacting with the basic structure of standard controllers at the unit level. Generally, a master controller is obtained by conventional loops based on a “pressure driven” configuration. In the following , the MPC library for MATLAB, by Bemporad, Morari and Ricker (2000)  has been applied to a detailed IGCC plant stimulation tool in order to understand the performance of a reliable multivariable linear MPC when adopted for such a nonlinear complex process with crucial targets. A detailed first principle model has been used as a “real plant” when performing the step tests for the identification of the simplified linear model and when checking the reliability of the control tool. Moreover, the effectiveness of the designed controller has been proved through the comparison between the linear MPC approach and an ideal solution (“direct” approach) obtained by the direct inversion of the DAE model, where perfect setpoint tracking is imposed by additional constraint equations and using the corresponding manipulated variables as closing variables. Moreover the performance of the derived MPC controller, when compared with a more conventional control configuration , shows a significant reduction of the overshoots and settling time when the plant is subject to load variations. The paper clearly shows how the MPS approach for a master controller is reliable, easy to design and of real value for practical purposes.
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