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We report the application of the Model-based Predictive Control (MPC) to improve the performance of the start-up of a 150⊕75 MW combined cycle power plant whose gas turbine is fueled by natural gas. In concrete the simulations have shown that the efficient drum level control is reflected on the improvement of power efficiency in the sense of reaching the 225 MW set point in around 45 minutes faster...
We present the simulation of the application of the Model-based Predictive Control (MPC) of the drum level in a Combined Cycle Plant in order to minimize the time for reaching the highest capacity of plant, around 225 MW. In contrast to others control techniques, our simulation yields that the MPC has shown capabilities as to reach its expected power in about 40 minutes before than PID, time which...
We report the results of the application of the Model-based Predictive Control (MPC) algorithm for a 3×3 MIMO balls mill grinding system by using computational simulation and Monte Carlo data generation. For this purpose, the system has been identified through a reduced scheme of Volterra formalism by which the proposed methodology has required to employ up to 20 parameters. Subsequently, the model...
In this paper, the formulation of a stochastic model and its subsequent incorporation into a predictive control of a balls mill grinding circuit, is presented. The apparition of stochastic variables is a consequence of variables interaction by which is impossible to know a well-defined determinist mathematical methodology. Thus, the perceived dynamics is simulated by emphasizing those possible scenarios...
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