Published at : 27 Apr 2018
Volume : IJtech Vol 9, No 2 (2018)
DOI : https://doi.org/10.14716/ijtech.v9i2.1040
Nasruddin, Nasution, S., Aisyah, N., Surachman, A., Wibowo, A.S., 2018. Exergy Analysis and Exergoeconomic Optimization of a Binary Cycle System using a Multi Objective Genetic Algorithm. International Journal of Technology. Volume 9(2), pp. 275-286
|Syaiful Nasution||Universitas Indonesia|
|Nyayu Aisyah||Universitas Indonesia|
|Arief Surachman||Universitas Indonesia|
|Agung Satrio Wibowo||Universitas Indonesia|
The increasing demand for energy and the current environmental issues are motivating experts to develop appropriate technology to face both problems. The binary cycle system is a highly effective generating technology which can be applied in the utilization of small-scale geothermal energy by using a working fluid that has a lower boiling point than water. In this paper, a geothermal power plant binary cycle system model was tested by using waste brine at a temperature of 180oC at well pad 4 of the Dieng geothermal power plant. In the optimization procedure, total exergy destruction and total annual cost are chosen as the objective functions. Optimization is made by using a multi objective genetic algorithm. Based on the simulation, it is known that the exergy efficiency and economic value of the optimal binary cycle of the geothermal power plant system has optimum conditions at an evaporation temperature of 163.3oC, a brine temperature in the preheater outlet of 130oC, and a water cooling temperature at condenser outlet of 35.4oC. The working fluid pressure at pump outlet is 3859 kPa with the composition of the working fluid mixture being 86% R601 and 14% R744, resulting in turbine power of 119.8 kW, total exergy destruction of 742.4 kW, and a total annual cost of 36,723 US dollars. These results indicate that, by setting the above operating conditions, the system can achieve optimum efficiency, as indicated by the minimum values of both exergy destruction and total annual cost.
Binary cycle system; Cost; Exergy destruction; Exergy efficiency; Genetic algorithm
The world is currently experiencing environmental problems, especially climate change caused of global warming. One of this is the occurrence of the greenhouse effect from the carbon dioxide (CO2) emissions generated by uncontrolled use of fossil fuels. The rapid development of industry today results in higher energy consumption. To overcome this problem, various resources of large potential renewable energies have been used and still being developed in Indonesia such as solar energy and geothermal. The application of solar energy for absorption and adsorption cooling systems have been developed at Universitas Indonesia (Lubis et al. 2018 and Nasruddin et al. 2015). Meanwhile, it is estimated that 40% of world geothermal energy reserves exist in Indonesia. Compared to the total potential that exists, geothermal energy utilization in Indonesia is still very low, at 1.4 GW or only 4.5% of the existing potential (Nasruddin et al., 2016a). The Organic Rankine Cycle (ORC) is a power plant system that uses organic fluids. It is estimated that there will be around 10% additional power in the power generation in the main ORC system (Prasetyo et al., 2010).
Study of the selection of the best working fluids for thermal systems has been conducted by Budisulistyo and Krumdieck. They investigated a pre-FS design of binary cycles with three types of working fluid, n-pentane, R245fa and R134a. Their results showed that the use of n-pentane resulted in greater output power and thermal efficiency compared to use of the other working fluids (Budisulistyo & Krumdieck, 2015).
Systems with pure working fluids have a limitation on isothermal boiling, which causes poor heating between working fluids and heat sources on pinch points, that producing major irreversibility (Chen et al., 2011). Therefore, the use of mixed working fluids can be solution to this problem (Chen et al., 2006). Wu et al. (2017) conducted a study by performing thermodynamic analysis and performance optimization of geothermal power plant using six types of working fluids, with each mixed with CO2. Their results showed that the mixture of R161 with CO2 was the best working fluid in terms of thermal efficiency and cost. The selected CO2-based mixture can reduce the cost per kWh even though larger areas of heat acquisition are required. With regard to refrigeration system, Nasruddin et al. (2016b) successfully conducted optimization scenario of a cascade refrigeration system by using refrigerant C3H8 in high temperature circuits and a mixture of C2H6/CO2 in low temperature circuits. Garg et al. (2013) evaluated the CO2 mixture with isopentane and propane in an ORC system by utilizing solar energy. The results showed an increase of pressure and a larger temperature glide, which the mixture of isopentane with CO2 had greater irreversibility compared to the propane mixture with CO2. Dai et al. (2014) performed thermodynamic analysis by mixing CO2 with seven types of working fluids. The simulation results showed that the R1234yf / CO2 mixture was suitable for large capacity transcritical power cycles due to its lower burnability and increased thermal and exergy efficiency compared to pure CO2.
The study has presented a thermodynamic analysis of the binary cycle system based on modeling and multi-objective optimization. With the help of multi-objective optimization, exergy destruction and total annual cost have been minimized. The decision making method was also used to find the optimal point among a set of Pareto fronts. Based on the optimization scenario, the system has optimum condition at an evaporation temperature of 163.3oC, a brine temperature outlet the pre heater of 130oC, a cooling water temperature outlet the condenser of 35.4oC, while the pumping fluid outflow is about 3859 kPa with a working fluid mixture of 86% R601 and 14% R744 which has total exergy destruction of 742.4 kW and a total annual cost of about 36,723 US dollars.
The authors gratefully acknowledge DRPM Universitas Indonesia for
supporting this research with a PITTA Research Grant 2017 No:
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