Exergoeconomic Based Optimization of a Gas Fired Steam Power Plant Using Genetic Algorithm

Abstract

This research paper mainly deals with exergy, economic, and environmental investigation of a 250 MW steam power plant located in Iran. In order to model this power plant, energy balance equations are used and each part of the power plant is modeled accordingly. Further by introducing the boiler as the main source of irreversibility, two approaches are presented to improve the boiler performance, reduction of excess air, and temperature reduction of gasses leaving the stacks. To study the effect of these two approaches, an objective function including the cost rate of exergy destruction of boiler, fuel cost, and cost rate of environmental impact is presented. The optimization process is done using a genetic algorithm. It is concluded that by optimizing, 20% reduction in the overall cost rate and 88% reduction in the cost rate of environmental impact can be achieved.

Modeling and Efficiency Optimization of Steam Boilers by Employing Neural Networks and Response-Surface Method (RSM)

“…It was monitored that increasing the primary air temperature was more practical and also improved the thermal performance of the boiler instead of increasing the air ratio. Javan et al [36] performed an exergoeconomic optimization of a gas-fired steam power plant by applying a genetic algorithm. Based on the results, the boiler was the most exergy-destructive source in the power plant.…”

Modeling and Efficiency Optimization of Steam Boilers by Employing Neural Networks and Response-Surface Method (RSM)

“…It was monitored that increasing the primary air temperature was more practical and also improved the thermal performance of the boiler instead of rising the air ratio. Javan et al [36] performed an exergoeconomic optimization of a gas-fired steam power plant by applying a genetic algorithm. Based on the results, the boiler was the most exergy destructive source in the power plant.…”