OPTIMIZATION OF MICRO CHP GAS TURBINE BY GENETIC ALGORITHM

Abstract

In this paper, a comprehensive thermodynamic modeling and multi-objective optimization of a micro turbine cycle in combined heat and power generation, which provides 100KW of electric power. This CHP System is composed of air compressor, combustion chamber (CC), Air Preheater, Gas Turbine (GT) and a Heat Recovery Heat Exchanger. In this paper, at the first stage, the each part of the micro turbine cycle is modeled using thermodynamic laws. Next, with using the energetic and exergetic concepts and applying economic and environmental functions, the multi-objectives optimization of micro turbine in combined heat and power generation is performed. The design parameters of this cycle are compressor pressure ratio (rAC), compressor isentropic efficiency (ηAC), GT isentropic efficiency (ηGT), CC inlet temperature (T3), and turbine inlet temperature (T4). In the multi-objective optimization three objective functions, including CHP exergy efficiency, total cost rate of the system products, and CO2 emission of the whole plant, are considered. The exergo-environmental objective function is minimized whereas power plant exergy efficiency is maximized usinga Genetic algorithm. To have a good insight into this study, a sensitivity analysis of the result to the fuel cost is performed. The results show that at the lower exergetic efficiency, in which the weight of exergo-environmental objective is higher, the sensitivity of the optimal solutions to the fuel cost is much higher than the location of the Pareto Frontier with the lower weight of exergo-environmental objective. In addition, with increasing exergy efficiency, the purchase cost of equipment in the plant is increased as the cost rate of the plant increases.

Keywords

Dates

  • Submission Date2012-12-18
  • Revision Date2013-09-30
  • Acceptance Date2013-10-27
  • Online Date2013-11-16

DOI Reference

10.2298/TSCI121218141Y

References

  1. Dincer, I.; Rosen, M.A., Exergy: Energy, Environment and Sustainable Development. Elsevier, 2007
  2. Balli O.Aras H, Energetic&exergetic performance evaluation of a combined heat and power system with the micro gas turbine (MGTCHP), International Journal of Energy Research, 31(14), (2007), pp.1425-1440
  3. Ahmadi P, Ameri M, Hamidi A. Energy, exergy and exergo-economic analysis of a steam power plant. International Journal of Energy Research (2009); 33:499-512.
  4. Sayyaadi, H. and Sabzaligol, T., Exergoeconomic optimization of a 1000MW light water reactor power generation system. Int. J. Energy Research, Vol. 33, (2009), pp. 378-395
  5. Sahoo PK. Exergoeconomic analysis and optimization of a cogeneration system using evolutionary programming. Applied Thermal Engineering, 28(13), (2008), 1580-1588
  6. Rosen MA, Dincer I. Exergy-cost-energy-mass analysis of thermal systems and processes. Energy coversion and Management 2003; 44(10):1633-1651.
  7. Ahmadi P, et.al. Multi-objective optimization of a combined heat and power (CHP) system for heating purpose in a paper mill using evolutionary algorithm. Int. J. Energy Res. (2012); 36: 46-63.
  8. Pourhasanzadeh, M. and Bigham, S., Optimization of a Micro Gas Turbine Using Genetic Algorithm, Proceedings, ASME Turbo Expo, Vancouver, Canada, 2011, Vol. 3, pp. 929-937
  9. Ehyaei, M.A., Mozafari, A., Energy, economic and environmental (3E) analysis of a micro gas turbine employed for on-site combined heat and power production, Int. J. Energy and Buildings. 42, (2010), 259-264
  10. Sanaye, S. and RaessiArdali, M., Estimating the power and number of micro-turbines in small scale combined heat and power systems, Int. J. Applied Energy (2009)
  11. Bejan, A., Tsatsaronis, G., Moran, M., Thermal design and optimization. John Wiley & sons, USA, 1996
  12. Valero, A., Lozano, M.A., Serra, L., Tsatsaronis, G., CGAM problem: definition and conventional solution, Int. J. Energy 19(3), 1994, 279-286
  13. Cengel, Y.A., Boles M.A. Thermodynamics an Engineering Approach. McGraw-Hill, Boston, 1998
  14. Dincer I, Al-Muslim H. Thermodynamic analysis of reheats cycle steam power plants. International Journal of Energy Research (2001); 25:727-739.
  15. Schaffer JD. Multiple objective optimization with vector evaluated genetic algorithms. Proceedings of the International Conference on Genetic Algorithm and their Applications, Consortia, Canada, 1985.
  16. Bejan A, Tsatsaronis G. Moran M. Thermal Design and Optimization. Wiley: New York, 1996.
  17. Kotas T.J. The Exergy Method of Thermal Plant Analysis. Krieger Publishing Company., Florida, 1995
  18. Gülder, OL. Flame temperature estimation of conventional and future jet fuels, Journal of Engineering for Gas Turbine and Power, 108, (1986) 376-380
  19. Rizk NK, Mongia HC. Semi analytical correlations for NOx, CO and UHC emissions. Journal of Engineering for Gas Turbines and Power; 15(3), 1993, 612-619
  20. Roosen P, Uhlenbruck S, Lucas K. Pareto optimization of a combined cycle power system as a decision support tool for trading off investment vs. operating costs. Int. J of Thermal Sciences, 42, (2003), 553-560
  21. Deb K Pratap A, Agarwal S, Meyarvian T., A fast and elitist multi-objective genetic algorithm: NSGA II. IEEE Transactions on Evolutionary Computing, 6(2), (2002):182-197
  22. Deb K, Goel T. Controlled elitist non-dominated sorting genetic algorithms for better convergence. Proceedings, 1st Int. Conference on Evolutionary Multi-Criterion Optimization, Zurich, 2001, 385-399
  23. Deb K. Multi-objective Optimization Using Evolutionary Algorithms. Wiley: Chichester, 2001
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