THERMAL SCIENCE
International Scientific Journal
THERMODYNAMIC ANALYSIS AND SIMULATION OF A NEW COMBINED POWER AND REFRIGERATION CYCLE USING ARTIFICIAL NEURAL NETWORK
ABSTRACT
In this study, a new combined power and refrigeration cycle is proposed, which combines the Rankine and absorption refrigeration cycles. Using a binary ammonia-water mixture as the working fluid, this combined cycle produces both power and refrigeration output simultaneously by employing only one external heat source. In order to achieve the highest possible exergy efficiency, a secondary turbine is inserted to expand the hot weak solution leaving the boiler. Moreover, an artificial neural network (ANN) is used to simulate the thermodynamic properties and the relationship between the input thermodynamic variables on the cycle performance. It is shown that turbine inlet pressure, as well as heat source and refrigeration temperatures have significant effects on the net power output, refrigeration output and exergy efficiency of the combined cycle. In addition, the results of ANN are in excellent agreement with the mathematical simulation and cover a wider range for evaluation of cycle performance.
KEYWORDS
PAPER SUBMITTED: 2010-11-02
PAPER REVISED: 2010-12-18
PAPER ACCEPTED: 2010-12-23
THERMAL SCIENCE YEAR
2011, VOLUME
15, ISSUE
Issue 1, PAGES [29 - 41]
- Kalina,AI., Combined cycle and waste-heat recovery power systems based on a novel thermodynamic energy cycleutilizing low-temperature heat for power generation, ASME Paper, (1983), 83-JPGC-GT-3
- Maloney,JD., Robertson,RC., Thermodynamic study of ammonia-water heat power cycles, Oak Ridge National Laboratory Report, (1953); CF-53-8-43
- Erickson,D.C., Anand,G., Kyung,I., Heat-activated dual-functionabsorption cycle, ASHRAE Transactions, 110 (2004), 1, pp. 515-524 16
- Amano,Y., Suzuki,T., Hashizume,T., Akiba,M., Tanzawa,Y., Usui,A., A hybrid power generation and refrigeration cycle withammonia-water mixture, IJPGC2000-15058, in: Proceedings of 2000 Joint Power Generation Conference ASME, (2000)
- Xu,F., Goswami,D.Y., Bhagwat,SS., A combined power/cooling cycle, Energy, 25 (2000), pp. 233-246
- Hasan,A.A., Goswami,G.Y., Vijayaraghavan,S., First and second law analysis of anew power and refrigeration thermodynamic cycle using a solar heat source, Solar Energy, 73 (2002), pp. 385-393
- Goswami,D.Y., Vijayaraghavan,S., Lu,S., Tamn,G., New and emergingdevelopments in solar energy, Solar Energy, 76 (2004), pp.33-43
- Tamm,G., Goswami,D.Y., Lu,S., Hasan,A.A., Theoretical and experimentalinvestigation of an ammonia-water power and refrigeraton thermodynamiccycle, Solar Energy, 76 (2004), pp. 217-228
- Vidal,A., Best,R., Rivero,R., Cervantes,J., Analysis of a combined power andrefrigeration cycle by the exergy method, Energy, 31 (2006), pp. 3401-3414
- Vijayaraghavan,S., Goswami,D.Y., A combined power and cooling cyclemodified to improve resource utilization efficiency using a distillation stage, Energy, 31 (2006), pp. 1177-1196
- Martin,C., Goswami,D.Y., Effectiveness of cooling production with a combinedpower and cooling thermodynamic cycle, Applied Thermal Engineering, 26 (2006), pp. 576-582
- Sadrameli,S.M., Goswami,D.Y., Optimum operating conditions for a combinedpower and cooling thermodynamic cycle, Applied Energy, 84 (2007), pp. 254-265
- Zheng,D., ChenB., Qi,Y., Jin,H., Thermodynamic analysis of a novel absorption power/cooling combined-cycle, Applied Energy, 83 (2006), pp. 311-323
- Liu,M., Zhang,N., Proposal and analysis of a novel ammonia-water cycle for power and refrigeration cogeneration, Energy, 32 (2007), pp. 961-970
- Zhang,N., Lior,N., Development of a novel combined absorption cycle for powergeneration and refrigeration, ASME Journal of Energy Resources Technology,129 (2007), pp. 254-265
- Wang,J.F., Dai,Y.P., Gao,L., Parametric analysis and optimization for a combined power and refrigeration cycle, Applied energy, 85 (2008), pp. 1071-1085
- Xu,F., Goswami,DY., Thermodynamic properties of ammonia-water mixtures for power-cycle applications, Energy, 24 (1999), pp. 525-536
- SoleimaniAlamdari,G., Simple equations for predicting entropy of ammonia-water mixture, International Journal of Engineering, 20 (2007), pp. 97-106
- Patek,J., Klomfar,J., Simple functions for fast calculations of selected thermodynamic properties of he ammonia-water system,,International Journal of Refrigeration, 31(1995), 4, pp. 228-234
- Thorin,E., Thermophysical properties of ammonia-water mixture for prediction of heat transfer areas in power cycles, International Journal of Thermophysics, 22 (2001), 1, pp. 201-214
- Kalogirou, S.A., Applications of artificial neural-networks for energy systems, Applied Energy Journal, 67 (2000), pp-17-35
- Ertunc, H.M., Hosoz, M., Artificial neural network analysis of a refrigeration systemwith an evaporative condenser, Appl. Therm. Eng., 26 (2006), pp. 627-635 17
- Jambunathan, K., Hartle, S.L., Ashforth-Frost, S., Fontama, V.N., Evaluating convectiveheat transfer coefficients using neural networks, International Journal of Heat and Mass Transfer, 39 (1996), pp. 2329-2332
- Parlak, A., Islamoglu, Y., Yasar, H., Egrisogut, A., Application of artificial neuralnetwork to predict specific fuel consumption and exhaust temperature for adiesel engine, Appl. Therm. Eng., 26 (2006), pp. 824-828
- Arcaklioglu, E., Performance comparison of CfCs with their substitutes using artificial neural network, Int. J. Energy Res., 28 (2004), pp. 1113-1125
- Bechtler, H., Browne, M.W., Bansal, P.K., Kecman, V., New approach to dynamic modeling of vapor-compression liquid chillers: artificial neural networks, Applied Thermal Engineering, 21 (2001), pp. 941-953
- Pacheco-Vega, A., Sen, M., Yang, K.T., McClain, R.L., Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data, International Journal of Heat and Mass Transfer, 44 (2001), pp. 763-770
- Palau, A., Velo, E., Puigjaner, L., "Use of neural networks and expert systems to control a gas/solid sorption chilling machine," International Journal of Refrigeration, 22 (1999), pp. 59-66
- Chow, TT., Zhang, GQ., Lin, Z., Song, CL., Global optimization of absorption chiller system by genetic algorithm and neural network, Energy Buildings, 34 (2002), pp. 103-109
- Widrowo, B., Hoff, M.E., Adaptive switching networks, in: Parallel DistributiveProcessing, vol. 1, MIT Press, MA, (1986), pp. 318-362