Energy and exergy evaluation of co2 closed-cycle gas turbine

  • 1 Faculty of Engineering, University of Rijeka, Croatia


This paper present energy and exergy evaluation of CO2 closed-cycle gas turbine process. The most important operating parameters of the whole observed cycle, as well as of each of its constituent components are presented and discussed. In the observed process, produced useful mechanical power for the power consumer drive is equal to 5189.78 kW, while the energy efficiency of the whole cycle is equal to 36.6%. Heat Regenerator is a crucial component of the observed process – without its operation energy efficiency of the whole cycle will be equal to only 16.91%. From the exergy aspect, Turbocompressor (TC) and Turbine (TU) shows good performances because its exergy efficiencies are higher than 90%. Regenerator exergy efficiency could be increased by lowering the temperature of the ambient in which analyzed CO2 closed-cycle gas turbine operates.



  1. Mrzljak, V., Anđelić, N., Lorencin, I., Car, Z.: Analysis of gas turbine operation before and after major maintenance, Journal of Maritime & Transportation Sciences 57 (1), p. 57-70, 2019. (doi:10.18048/2019.57.04)
  2. Lorencin, I., Anđelić, N., Mrzljak, V., Car, Z.: Genetic algorithm approach to design of multi-layer perceptron for combined cycle power plant electrical power output estimation, Energies 12 (22), 4352, 2019. (doi:10.3390/en12224352)
  3. Yoru, Y., Karakoc, T. H., Hepbasli, A.: Dynamic energy and exergy analyses of an industrial cogeneration system, International journal of energy research 34, p. 345–356, 2010. (doi:10.1002/er.1561)
  4. Lorencin, I., Anđelić, N., Mrzljak, V., Car, Z.: Multilayer perceptron approach to condition-based maintenance of marine CODLAG propulsion system components, Scientific Journal of Maritime Research 33, p. 181-190, 2019. (doi:10.31217/p.33.2.8)
  5. Baressi Šegota, S., Lorencin, I., Musulin, J., Štifanić, D., Car, Z.: Frigate Speed Estimation Using CODLAG Propulsion System Parameters and Multilayer Perceptron, International Journal of Maritime Science & Technology “Our Sea” 67 (2), p. 117-125, 2020. (doi:10.17818/NM/2020/2.4)
  6. Hou, S., Wu, Y., Zhou, Y., Yu, L.: Performance analysis of the combined supercritical CO2 recompression and regenerative cycle used in waste heat recovery of marine gas turbine, Energy Conversion and Management 151, p. 73–85, 2017. (doi:10.1016/j.enconman.2017.08.082)
  7. Gonca, G.: Exergetic and ecological performance analyses of a gas turbine system with two intercoolers and two re-heaters, Energy 124, p. 579-588, 2017. (doi:10.1016/
  8. Alklaibi, A.M.: Utilization of exhaust gases heat from gas turbine with air bottoming combined cycle, Energy 133, p. 1108-1120, 2017. (doi:10.1016/
  9. Ibrahim, T. K., Basrawi, F., Awad, O. I., Abdullah, A. N., Najafi, G., Mamat, R., Hagos, F. Y.: Thermal performance of gas turbine power plant based on exergy analysis, Applied Thermal Engineering 115, p. 977-985, 2017. (doi:10.1016/j.applthermaleng.2017.01.032)
  10. Kostyuk, A., Frolov, V.: Steam and gas turbines, Mir Publishers, Moscow, 1988.
  11. Sutton, I.: Plant design and operations, Elsevier Inc., 2015.
  12. Mrzljak, V., Blecich, P., Anđelić, N., Lorencin, I.: Energy and exergy analyses of forced draft fan for marine steam propulsion system during load change, Journal of Marine Science and Engineering 7, 381, 2019. (doi:10.3390/jmse7110381)
  13. Kanoğlu, M., Çengel, Y.A., Dincer, I.: Efficiency evaluation of energy systems, Springer Briefs in Energy, Springer, 2012.
  14. Medica-Viola, V., Baressi Šegota, S., Mrzljak, V., Štifanić, D.: Comparison of conventional and heat balance based energy analyses of steam turbine, Scientific Journal of Maritime Research 34 (1), p. 74-85, 2020. (doi:10.31217/p.34.1.9)
  15. Blažević, S., Mrzljak, V., Anđelić, N., Car, Z.: Comparison of energy flow stream and isentropic method for steam turbine energy analysis, Acta Polytechnica 59 (2), p. 109-125, 2019. (doi:10.14311/AP.2019.59.0109)
  16. Mrzljak, V., Poljak, I.: Energy analysis of main propulsion steam turbine from conventional LNG carrier at three different loads, International Journal of Maritime Science & Technology “Our Sea” 66 (1), p. 10-18, 2019. (doi:10.17818/NM/2019/1.2)
  17. Mrzljak, V., Prpić-Oršić, J., Poljak, I.: Energy power losses and efficiency of low power steam turbine for the main feed water pump drive in the marine steam propulsion system, Journal of Maritime & Transportation Sciences 54 (1), p. 37-51, 2018. (doi:10.18048/2018.54.03)
  18. Baldi, F., Ahlgren, F., Van Nguyen, T., Thern, M., Andersson, K.: Energy and exergy analysis of a cruise ship, Energies 11, 2508, 2018. (doi:10.3390/en11102508)
  19. Lorencin, I., Anđelić, N., Mrzljak, V., Car, Z.: Exergy analysis of marine steam turbine labyrinth (gland) seals, Scientific Journal of Maritime Research 33 (1), p. 76-83, 2019. (doi:10.31217/p.33.1.8)
  20. Mrzljak, V., Poljak, I., Medica-Viola, V.: Dual fuel consumption and efficiency of marine steam generators for the propulsion of LNG carrier, Applied Thermal Engineering 119, p. 331–346, 2017. (doi:10.1016/j.applthermaleng.2017.03.078)
  21. Koroglu, T., Sogut, O. S.: Conventional and advanced exergy analyses of a marine steam power plant, Energy 163, p. 392- 403, 2018. (doi:10.1016/
  22. Eboh, F. C., Ahlström, P., Richards, T.: Exergy analysis of solid fuel-fired heat and power plants: a review, Energies 10 (2), 165, 2017. (doi:10.3390/en10020165)
  23. Tan, H., Shan, S., Nie, Y., Zhao, Q.: A new boil-off gas re-liquefaction system for LNG carriers based on dual mixed refrigerant cycle, Cryogenics 92, p. 84–92, 2018. (doi:10.1016/j.cryogenics.2018.04.009)
  24. Lemmon, E.W., Huber, M.L., McLinden, M.O.: NIST reference fluid thermodynamic and transport properties-REFPROP, version 9.0, User’s guide, Colorado, 2010.
  25. Szargut, J.: Exergy method: technical and ecological applications (Vol. 18), WIT press, 2005.
  26. Ahmadi, G. R., Toghraie, D.: Energy and exergy analysis of Montazeri steam power plant in Iran, Renewable and Sustainable Energy Reviews 56, p. 454–463, 2016. (doi:10.1016/j.rser.2015.11.074)
  27. Lorencin, I., Anđelić, N., Španjol, J., Car, Z.: Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis, Artificial Intelligence in Medicine, 102, 101746, 2020. (doi:10.1016/j.artmed.2019.101746)
  28. Car, Z., Baressi Šegota, S., Anđelić, N., Lorencin, I., Mrzljak, V.: Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron, Computational and Mathematical Methods in Medicine, 2020. (doi:10.1155/2020/5714714)
  29. Lorencin, I., Anđelić, N., Mrzljak, V., Car, Z.: Marine objects recognition using convolutional neural networks, International Journal of Maritime Science & Technology “Our Sea” 66 (3), p. 112-119, 2019. (doi:10.17818/NM/2019/3.3)
  30. Baressi Šegota, S., Lorencin, I., Ohkura, K., Car, Z.: On the traveling salesman problem in nautical environments: an evolutionary computing approach to optimization of tourist route paths in Medulin, Croatia, Journal of Maritime & Transportation Sciences 57 (1), p. 71-87, 2019. (doi:10.18048/2019.57.05)

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