Application of nonlinear multivariable model predictive control to transient operation of a gas turbine and NOX emissions reduction

Abstract

This work aims to apply a multivariable nonlinear model-based predictive control strategy (MPC) to avoid unsafe or inappropriate operation of gas turbines, while reducing NOx emissions. In this context, the control variables are the compressor speed and the temperature after the turbine. The controller maintains the speed proportional to the grid frequency during load changes. Additionally, in cases where the turbine is installed in a combined heat and power cycle, the discharge temperature must follow a reference, to ensure the quality of the steam generated. The control is achieved by manipulating the fuel flow in the combustion chamber and the variable inlet guide vanes of the compressor. The nonlinear dynamic behavior of an industrial gas turbine is modeled using a first principle process simulator, which solves the mass, energy and momentum conservation equations, together with an equation of state. Furthermore, pollutant emissions are minimized as part of the process, through an optimization procedure. The optimization problem is solved through the implementation of three different evolutionary algorithms and one direct search method. The proposed control strategy is successfully applied to a gas turbine in load rejection scenarios, and the optimization fulfills its goal by reducing nitrogen oxides emissions.