Optimization of a Gas turbine model (KWU V94.2) Siemens by using PSO and GA for reducing CO2 and NOx emission based on exergy and economic analysis

Carbon dioxide and oxides of nitrogen as greenhouse gases play a vital role in global warming. A significant contribution to CO2 and NOx emitted to the atmosphere is attributed to fossil fuel combustion. Optimally tuning design parameters of gas turbine power plants from an exergoeconomic perspective and economic analysis is a challenging optimization problem to reduce CO2 and NOx emission. This study first develops a cost function based on key parameters of the plant which include air compressor pressure ratio, gas turbine inlet temperature, gas turbine isentropic efficiency and compressor isentropic efficiency. The integration of gas turbines then leads to a reduction in the operating costs due to fuel savings, and it also provides flexibility for importing and exporting power. Evolutionary algorithms such genetic algorithm (GA) and particle swarm optimization (PSO) are applied to minimize the cost function and optimally adjusting four design parameters. Also environmental impact is considered to reduce CO2 and NOx with respect to the base case. The proposed methods and algorithms are implemented for a gas turbine power plant made by Siemens (model V94.2). Gas turbine model SGT5-2000E (KWU V94.2) is one of the most known that uses in a lot of power plants in Iran. The results exhibit that the GA is more performance PSO.

Keywords:

Exergoeconomic , Exergoenvironmental , Gas Turbine ,