One of the effective devices to reduce NO x in power plants is the ammonia based selective catalyst reduction (SCR) system. In this paper, kinetic SCR model simulator is tuned and validated with representative site data from an actual combined-cycle power plant. This simulator is then used for the development and implementation of model predictive control (MPC) for SCR systems to control the plant startup NO x emissions along with maintaining ammonia slip within the acceptable limit. Also, the results of linear and nonlinear MPC are compared. The startup NO x reduction obtained in both cases is seen to be close to ~85%, but the computational effort required for both these approaches differ significantly.
In situ adaptive tabulation (ISAT) is applied to dynamic process simulators for reducing computational run-time. Several enhancements of previous approaches are presented here, including a method for estimating the sensitivities using input−output data, along with different strategies for record distribution. A modified version of the original algorithm (mISAT) to improve performance of ISAT is also suggested. Case studies for first-principles and data-driven models using ISAT are performed to generate accurate trajectories, which are essentially the same as those obtained by direct integration. Computational speed-up using ISAT is also shown for these studies.
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