Soft computing based multi-objective optimization of steam cycle power plant using NSGA-II and ANN

Authors:
Farzaneh Hajabdollahi;Zahra Hajabdollahi;Hassan Hajabdollahi
Affiliations:
Department of Mechanical Engineering, Behbahan Branch, Islamic Azad University, Behbahan, Iran;Department of Mechanical Engineering, Behbahan Branch, Islamic Azad University, Behbahan, Iran;Department of Mechanical Engineering, Behbahan Branch, Islamic Azad University, Behbahan, Iran
Venue:
Applied Soft Computing
Year:
2012

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Abstract

In this paper a steam turbine power plant is thermo-economically modeled and optimized. For this purpose, the data for actual running power plant are used for modeling, verifying the results and optimization. Turbine inlet temperature, boiler pressure, turbines extraction pressures, turbines and pumps isentropic efficiency, reheat pressure as well as condenser pressure are selected as fifteen design variables. Then, the fast and elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) is applied to maximize the thermal efficiency and minimize the total cost rate (sum of investment cost, fuel cost, and maintenance cost) simultaneously. The results of the optimal design are a set of multiple optimum solutions, called 'Pareto optimal solutions'. The optimization results in some points show 3.76% increase in efficiency and 3.84% decrease in total cost rate simultaneously, when it compared with the actual data of the running power plant. Finally as a short cut to choose the system optimal design parameters a correlation between two objectives and fifteen decision variables with acceptable precision are presented using Artificial Neural Network (ANN).