Optimization of fog inlet air cooling system for combined cycle power plants using genetic algorithm

• To model the combined cycle power plant equipped with fog inlet air cooling method.
• To conduct both exergy and economic analyses for better understanding.
• To conduct a complete optimization using a genetic algorithm to determine the optimal design parameters of the system.

In this research paper, a comprehensive thermodynamic modeling of a combined cycle power plant is first conducted and the effects of gas turbine inlet fogging system on the first and second law efficiencies and net power outputs of combined cycle power plants are investigated. The combined cycle power plant (CCPP) considered for this study consist of a double pressure heat recovery steam generator (HRSG) to utilize the energy of exhaust leaving the gas turbine and produce superheated steam to generate electricity in the Rankine cycle. In order to enhance understanding of this research and come up with optimum performance assessment of the plant, a complete optimization is using a genetic algorithm conducted. In order to achieve this goal, a new objective function is defined for the system optimization including social cost of air pollution for the power generation systems. The objective function is based on the first law efficiency, energy cost and the external social cost of air pollution for an operational system. It is concluded that using inlet air cooling system for the CCPP system and its optimization results in an increase in the average output power, first and second law efficiencies by 17.24%, 3.6% and 3.5%, respectively, for three warm months of year.