Exergy analysis of base and optimized high pressure feed water heating system from nuclear power plant

  • 1 Faculty of Engineering, University of Rijeka, Croatia


In this paper is performed exergy analysis of high pressure feed water heating system and all of its components which operates in nuclear power plant. Four cases are observed: system operation in the base case and system operation in three optimized cases. Exergy analysis show that optimization by using different algorithms has a different influence on the exergy destructions, while all the algorithms increase whole system and its components exergy efficiencies. An increase in the ambient temperature increases exergy destructions and decrease exergy efficiencies of the whole observed system and its components, regardless of operation case. The highest exergy efficiency of the whole analyzed system is 96.12% and is obtained by using an IGSA algorithm at the lowest observed ambient temperature of 5 °C. By observing exergy destructions only, it should be noted that GA and IGSA algorithms give almost identical results.



  1. Al Doori, W. H., Mohammed, M. K., Jassim, A. H., Ibrahim, T. K., & Al-Sammarraie, A. T. (2019). Energy and exergy analysis of the steam power plant based on effect the numbers of feed water heater. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 56(2), 211-222.
  2. Nandini, M., Sekhar, Y. R., & Subramanyam, G. (2021). Energy analysis and water conservation measures by water audit at thermal power stations. Sustainable Water Resources Management, 7(1), 1- 24. (doi:10.1007/s40899-020-00487-4)
  3. Mrzljak, V., Poljak, I., & Medica-Viola, V. (2017). Thermodynamical analysis of high-pressure feed water heater in steam propulsion system during exploitation. Brodogradnja: Teorija i praksa brodogradnje i pomorske tehnike, 68(2), 45-61. (doi:10.21278/brod68204)
  4. Moran, M. J., Shapiro, H. N., Boettner, D. D., & Bailey, M. B. (2010). Fundamentals of engineering thermodynamics. John Wiley & Sons.
  5. Mrzljak, V., Poljak, I., & Medica-Viola, V. (2017). Energy and exergy efficiency analysis of sealing steam condenser in propulsion system of LNG carrier. NAŠE MORE: znanstveni časopis za more i pomorstvo, 64(1), 20-25. (doi:10.17818/NM/2017/1.4)
  6. Naserbegi, A., Aghaie, M., Minuchehr, A., & Alahyarizadeh, G. (2018). A novel exergy optimization of Bushehr nuclear power plant by gravitational search algorithm (GSA). Energy, 148, 373- 385. (doi:10.1016/
  7. Wang, C., Yan, C., Wang, J., Tian, C., & Yu, S. (2017). Parametric optimization of steam cycle in PWR nuclear power plant using improved genetic-simplex algorithm. Applied Thermal Engineering, 125, 830-845. (doi:10.1016/j.applthermaleng.2017.07.045)
  8. Cengel, Y. A., & Boles, M. A. (2007). Thermodynamics: An Engineering Approach 6th Editon (SI Units). The McGraw-Hill Companies, Inc., New York.
  9. Mrzljak, V., Blec ich, P., Anđelić, N., & Lorencin, I. (2019). Energy and exergy analyses of forced draft fan for marine steam propulsion system during load change. Journal of Marine Science and Engineering, 7(11), 381. (doi:10.3390/jmse7110381)
  10. Baressi Šegota, S., Lorencin, I., Anđelić, N., Mrzljak, V., & Car, Z. (2020). Improvement of Marine Steam Turbine Conventional Exergy Analysis by Neural Network Application. Journal of Marine Science and Engineering, 8(11), 884. (doi:10.3390/jmse8110884)
  11. Medica-Vio la, V., Mrzljak, V., Anđelić, N., & Jelić, M. (2020). Analysis of Low-Power Steam Turbine With One Extraction for Marine Applications. NAŠE MORE: znanstveni časopis za more i pomorstvo, 67(2), 87-95. (doi: 10.17818/NM/2020/2.1)
  12. Kanoğlu, M., Çengel, Y. A., & Dinçer, İ. (2012). Effic iency evaluation of energy systems. Springer Science & Business Media.
  13. Anđelić, N., Mrzljak, V., Lorenc in, I., & Baressi Šegota, S. (2020). Comparison of Exergy and Various Energy Analysis Methods for a Main Marine Steam Turbine at Different Loads. Pomorski zbornik, 59(1), 9-34. (doi:10.18048/2020.59.01.)
  14. Lorencin, I., Anđelić, N., Mrzljak, V., & Car, Z. (2019). Exergy analysis of marine steam turbine labyrinth (gland) seals. Pomorstvo, 33(1), 76-83. (doi:10.31217/p.33.1.8)
  15. Mrzljak, V., Poljak, I., & Medica-Viola, V. (2017). Dual fuel consumption and efficiency of marine steam generators for the propulsion of LNG carrier. Applied Thermal Engineering, 119, 331- 346. (doi:10.1016/j.applthermaleng.2017.03.078)
  16. Koroglu, T., & Sogut, O. S. (2018). Conventional and advanced exergy analyses of a marine steam power plant. Energy, 163, 392-403. (doi:10.1016/
  17. Medica-Vio la, V., Baressi Šegota, S., Mrzljak, V., & Štifanić, D. (2020). Comparison of conventional and heat balance based energy analyses of steam turbine. Pomorstvo, 34(1), 74-85. (doi:10.31217/p.34.1.9)
  18. Mrzljak, V., Prpić-Oršić, J., & Senčić, T. (2018). Change in steam generators main and auxiliary energy flow streams during the load increase of LNG carrier steam propulsion system. Pomorstvo, 32(1), 121-131. (doi: 10.31217/p.32.1.15)
  19. Lorencin, I., Anđelić, N., Mrzljak, V., & Car, Z. (2019). Genetic algorithm approach to design of multi-layer perceptron for combined cycle power plant electrical power output estimation. Energies, 12(22), 4352. (doi:10.3390/en12224352)
  20. Lemmon, E. W., Huber, M. L., & McLinden, M. O. (2010). NIST Standard Reference Database 23, Reference Fluid Thermodynamic and Transport Properties (REFPROP), version 9.0, National Institute of Standards and Technology. R1234yf. fld file dated December, 22, 2010.
  21. Anđelić, N., Baressi Šegota, S., Lorenc in, I., & Car, Z. (2020). Estimation of gas turbine shaft torque and fuel flow of a CODLAG propulsion system using genetic programming algorithm. Pomorstvo, 34(2), 323-337. (doi: 10.31217/p.34.2.13)
  22. Lorencin, I., Anđelić, N., Mrzljak, V., & Car, Z. (2019). Marine objects recognition using convolutional neural networks. NAŠE MORE: znanstveni časopis za more i pomorstvo, 66(3), 112- 119. (doi:10.17818/NM/2019/3.3)
  23. Lorencin, I., Anđelić, N., Mrzljak, V., & Car, Z. (2019). Multilayer perceptron approach to condition-based maintenance of marine CODLAG propulsion system components. Pomorstvo, 33(2), 181-190. (doi:10.31217/p.33.2.8)
  24. Baressi Šegota, S., Lorencin, I., Ohkura, K., & Car, Z. (2019). On the traveling salesman problem in nautical environments: an evolutionary computing approach to optimization of tourist route paths in Medulin, Croatia. Pomorski zbornik, 57(1), 71-87. (doi:10.18048/2019.57.05.)
  25. Anđelić, N., Baressi Šegota, S., Lorencin, I., Jurilj, Z., Šušteršič, T., Blago jević, A., ... & Car, Z. (2021). Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm. International Journal of Environmental Research and Public Health, 18(3), 959. (doi:10.3390/ijerph18030959)
  26. Car, Z., Baressi Šegota, S., Anđelić, N., Lorenc in, I., & Mrzljak, V. (2020). Modeling the spread of COVID-19 infection using a multilayer perceptron. Computational and mathematical methods in medicine, 2020. (doi:10.1155/2020/5714714)
  27. Baressi Šegota, S., Anđelić, N., Lorencin, I., Saga, M., & Car, Z. (2020). Path planning optimization of six-degree-of-freedom robotic manipulators using evolutionary algorithms. International Journal of Advanced Robotic Systems, 17(2), 1729881420908076. (doi:10.1177/1729881420908076)
  28. Lorencin, I., Baressi Šegota, S., Anđelić, N., Blago jević, A., Šušteršić, T., Protić, A., ... & Car, Z. (2021). Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks. Journal of Personalized Medicine, 11(1), 28. (doi:10.3390/jpm11010028)

Article full text

Download PDF