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Second Law Based Optimization of Micro Gas Turbine (MGT) Cycle for Residental Application Using a Genetic Algorithm

[+] Author Affiliations
N. Enadi

Power and Water University of Technology (PWUT), Tehran, Iran

P. Ahmadi

University of Ontario Institute of Technology, Oshawa, ON, Canada

F. Atabi, M. R. Heibati

Islamic Azad University, Tehran, Iran

Paper No. IMECE2010-39544, pp. 281-291; 11 pages
doi:10.1115/IMECE2010-39544
From:
  • ASME 2010 International Mechanical Engineering Congress and Exposition
  • Volume 5: Energy Systems Analysis, Thermodynamics and Sustainability; NanoEngineering for Energy; Engineering to Address Climate Change, Parts A and B
  • Vancouver, British Columbia, Canada, November 12–18, 2010
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4429-8
  • Copyright © 2010 by ASME

abstract

Exergoeconomic analysis helps designers to find ways to improve the performance of a system in a cost effective way. Most of the conventional exergoeconomic optimization methods are iterative in nature and require the interpretation of the designer at each iteration. In this work, a cogeneration system that produces 50MW of electricity and 33.3 kg/s of saturated steam at 13 bars is optimized using exergoeconomic principles and evolutionary programming such as Genetic algorithm. The optimization program is developed in Matlab Software programming. The plant is comprised of a gas turbine, air compressor, combustion chamber, and air pre-heater as well as a heat recovery steam generator (HRSG).The design Parameters of the plant, were chosen as: compressor pressure ratio (rc ), compressor isentropic efficiency (ηac ), gas turbine isentropic efficiency (ηgt ), combustion chamber inlet temperature (T3 ), and turbine inlet temperature (T4 ). In order to optimally find the design parameters a thermoeconomic approach has been followed. An objective function, representing the total cost of the plant in terms of dollar per second, was defined as the sum of the operating cost, related to the fuel consumption. Subsequently, different pars of objective function have been expressed in terms of decision variables. Finally, the optimal values of decision variables were obtained by minimizing the objective function using Evolutionary algorithm such as Genetic Algorithm. The influence of changes in the demanded power on the design parameters has been also studied for 50, 60, 70 MW of net power output.

Copyright © 2010 by ASME

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