Thermodynamic Optimization of Turbine Lines for Maximum Exergy Efficiency in a Binary Geothermal Power Plant


For engineering applications related to techniques that optimise power plants or thermal systems, optimisation techniques are very important. Power plants with wasted geothermal resources and inefficient organic Rankine cycle (ORC) attract the attention of researchers, engineers and decision-makers. In this study, the pressure and mass flow rates on turbine lines are optimised to maximize exergy efficiency in a binary ORC geothermal power plant (GPP). With this aim, initially data collected from a real operating GPP are used to simulate the system. Then an artificial bee colony algorithm is developed for this model. As a result, the thermodynamic performance of the system is estimated at the same moment and with reasonable accuracy, it can be ensured that the physical process used for improvements is better understood.


Waste tire; Geothermal energy; ORC; Turbine; Exergy efficiency; Optimization.

DOI: 10.17350/HJSE19030000127

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1. Schiel K, Baume O, Carus G, Leopold U. GIS-based modelling of shallow geothermal energy potential for CO2 emission mitigation in urban areas. Renewable Energy 86 (2016) 1023–36.

2. TEIAS. Turkish Electricity Transmission Corporation,, Accessed date: 15.04.2017.

3. Bertani R. Geothermal power generation in the world 2010–2014 update report. Geothermics 60 (2016) 31–43.

4. Ileri A, Gürer T. Energy and exergy utilization in Turkey during 1995. Energy 23 (1998) 1099-106.

5. Wei D, Lu X, Lu Z, Gu J. Performance analysis and optimization of organic Rankine cycle (ORC) for waste heat recovery. Energy Conversion and Management 48 (2007) 1113–9.

6. Wei D, Lu X, Lu Z, Gu J. Dynamic modeling and simulation of an organic Rankine cycle (ORC) system for waste heat recovery. Applied Thermal Engineering 28 (2008) 1216–24.

7. Rashidi MM, Galanis N, Nazari F, Parsa AB, Shamekhi L. Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feed water heaters using artificial bees colony and artificial neural network. Energy 36 (2011) 5728–40.

8. Sun J, Li WH. Operation optimization of an organic Rankine cycle (ORC) heat recovery power plant. Applied Thermal Engineering 31 (2011) 2032–41.

9. Zhang J, Zhang W, Hou G, Fang F. Dynamic modeling and multivariable control of organic Rankine cycles in waste heat utilizing processes. Computers and Mathematics with Applications 64 (2012) 908–21.

10. Bamgbopa MO, Uzgoren E. Numerical analysis of an organic Rankine cycle under steady and variable heat input. Applied Energy 107 (2013) 219–28.

11. Bamgbopa MO, Uzgoren E. Quasi-dynamic model for an organic Rankine cycle. Energy Conversion and Management 72 (2013) 117–24.

12. Clarke J, McLay L, McLeskey Jr JT. Comparison of genetic algorithm to particle swarm for constrained simulation-based optimization of a geothermal power plant. Advanced Engineering Informatics 28 (2014) 81-90.

13. Clarke J, McLeskey Jr JT. Multi-objective particle swarm optimization of binary geothermal power plants. Applied
Energy 138 (2015) 302–14.

14. Sadeghi S, Saffari H, Bahadormanesh N. Optimization of a modified double-turbine Kalina cycle by using Artificial Bee Colony algorithm. Applied Thermal Engineering 91 (2015) 19–32.

15. Saffari H, Sadeghi S, Khoshzat M, Mehregan P. Thermodynamic analysis and optimization of a geothermal Kalina
cycle system using Artificial Bee Colony algorithm. Renewable Energy 89 (2016) 154–67.

16. Proctor MJ, Yu W, Kirkpatrick RD, Young BR. Dynamic modelling and validation of a commercial scale geothermal organic Rankine cycle power plant. Geothermics 61 (2016) 63–74.

17. Li H, Hu D, Wang M, Dai Y. Off-design performance analysis of Kalina cycle for low temperature geothermal source. Applied Thermal Engineering 107 (2016) 728–37.

18. Wu C, Wang SS, Jiang X, Li J. Thermodynamic analysis and performance optimization of transcritical power cycles
using CO2-based binary zeotropic mixtures as working fluids for geothermal power plants. Applied Thermal Engineering 115 (2017) 292–304.

19. MathWorks, Matlab,, Accessed date: 15.04.2017.

20. Bell IH, Quoilin S, Wronski J, Lemort V. CoolProp: an open-source referencequality thermophysical property library, in: ASME ORC 2nd International Seminar on ORC Power Systems, Rotterdam, Netherlands; (2013).

21. Bell IH, Wronski J, Quoilin S, Lemort V. Pure and pseudo-pure fluid thermophysical property evaluation and the
open-source thermophysical property library coolprop. Industrial and Engineering Chemistry Research, 53 (6) (2014) 2498–508.

22. Kanoglu M, Bolattürk A. Performance and parametric investigation of a binary geothermal power plant by exergy. Renewable Energy 33 (11) (2008) 2366–74.

23. Keçebaş A, Gökgedik H. Thermodynamic evaluation of a geothermal power plant for advanced exergy analysis. Energy 88 (2015) 746–55.

24. Gökgedik H, Yürüsoy M, Keçebaş A. Improvement potential of a real geothermal power plant using advanced exergy analysis. Energy 112 (2016) 254–63.

25. Koroneos C, Polyzakis A, Xydis G, Stylos N, Nanaki E. Exergy analysis for a proposed binary geothermal power plant in Nisyros Island, Greece. Geothermics 70 (2017) 38–46.

26. Karaboga D. An ideal based on honey bee swarm for numerical optimization, Technical Report – TR06, Erciyes University, Engineering Faculty, Department of Computer Engineering, Kayseri, Turkey; (2005).

27. Karaboga D, Akay B. A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation 214 (1) (2009) 108–32.

28. Uzlu E, Akpınar A, Öztürk HT, Nacar S, Kankal M. Estimates of hydroelectric generation using neural networks with
the Artificial Bee Colony algorithm for Turkey. Energy 69 (2014) 638–47.

29. Delgarm N, Sajadi B, Delgarm S. Multi-objective optimization of building energy performance and indoor thermal
comfort: A new method using Artificial Bee Colony (ABC). Energy and Buildings 131 (2016) 42–53.

30. Karaboga D, Ozturk C. A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied Soft Computation 11 (1) (2011) 652–57.

31. Baykasoglu A, Ozbakir L, Tapkan P. Artificial Bee Colony algorithm and its application to generalized assignment problem, in: F.T.S. Chan, M.K. Tiwari (Eds.), Swarm Intelligence: Focus on Ant and Particle Swarm Optimization, ITech
Education and Publishing, Vienna, Austria; (2007) pp. 113–44.

32. Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm. Journal of Global Optimization 39 (3) (2007) 459–71.

33. Karaboga D, Basturk B. Artificial Bee Colony (ABC) optimization algorithm for solving constrained optimization
problems. Foundations of Fuzzy Logic and Soft Computing 4529 (2007) 789–98.

34. Dhahri H, Alimi AM, Abraham A. Designing beta basis function neural network for optimization using Artificial Bee Colony (ABC). In: IEEE World Congress on Computational Intelligence Brisbane-Australia; (2012) pp. 10–5.
How to Cite
Kecebas, A. (2019). Thermodynamic Optimization of Turbine Lines for Maximum Exergy Efficiency in a Binary Geothermal Power Plant. Hittite Journal of Science & Engineering, 6(1), 07-15. Retrieved from