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

Abstract

For engineering applications related to techniques that optimize power plants or thermal systems, optimization 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 optimized 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 ABC algorithm is developed for this model. The results showed that, the total exergy efficiency of the system was 35.25% while its value increased with the ABC optimization in the maximum possible exergy efficiency of 38.45%. Optimizing the turbine lines in the system ensured improvement rate of 4-6% for the turbines. 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.

Keywords

• Geothermal energy,ORC,Turbine,Exergy efficiency,Optimization

References

1. Bamgbopa MO, Uzgoren E. Numerical analysis of an organic Rankine cycle under steady and variable heat input. Applied Energy 107 (2013) 219–28.
2. Bamgbopa MO, Uzgoren E. Quasi-dynamic model for an organic Rankine cycle. Energy Conversion and Management 72 (2013) 117–24.
3. 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.
4. Clarke J, McLeskey Jr JT. Multi-objective particle swarm optimization of binary geothermal power plants. Applied Energy 138 (2015) 302–14.
5. 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.
6. 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.
7. 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.
8. 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.

9. 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.
10. MathWorks, Matlab, http://www.mathworks.com/ products/matlab/, Accessed date: 15.04.2017.
11. 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).
12. 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.
13. Kanoglu M, Bolattürk A. Performance and parametric investigation of a binary geothermal power plant by exergy. Renewable Energy 33 (11) (2008) 2366–74.
14. Keçebaş A, Gökgedik H. Thermodynamic evaluation of a geothermal power plant for advanced exergy analysis. Energy 88 (2015) 746–55.
15. Gökgedik H, Yürüsoy M, Keçebaş A. Improvement potential of a real geothermal power plant using advanced exergy analysis. Energy 112 (2016) 254–63.
16. 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.
17. 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).
18. Karaboga D, Akay B. A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation 214 (1) (2009) 108–32.
19. 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.
20. 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.
21. Karaboga D, Ozturk C. A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied Soft Computation 11 (1) (2011) 652–57.
22. 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.
23. 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.
24. 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.
25. 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.