Multiobjective optimization of heat recovery steam generator in a combined cycle power using genetic algorithm

Due to the increasing demand for electrical energy, efforts to increase the thermal efficiency of the steam and gas power plants have led to extensive reform in these cycles. One of these common reforms is employing a conventional combined gas-steam cycle. In combined cycle power plants that are built to produce power, a significant portion of the input energy is lost. In this research to achieve the thermodynamic properties of a combined cycle power plant after modeling the cycle and determining the cycle potential independent variables, multiobjective optimization by imposing restrictions on cost functions and changing them concerning exergy efficiency has been analyzed. The results show that increasing the parameters of superheated temperature, pinch point temperature difference, pump exhaust pressure, and condenser inlet flow rate improves the system performance and increases exergy efficiency. It is shown by two-objective optimization that when costs are increased up to 40%, exergy efficiency is increased, and when an increase is more than 40% repeated results would be obtained. Applying costs lower than 5% is not considered according to software limitations. Also, the results show that it is possible to increase exergy efficiency up to 79.7% with a 40% investment cost. In this article, the best performance point of the heat recovery steam generator has been determined using genetic algorithm optimization. Also, the effect of various economic parameter types on technical performance has been investigated, and the technical limitations of pinch and approach temperature differences have been considered.image