Thermodynamic analysis and simulation of a new combined power and refrigeration cycle using artificial neural network

Thermal Science

Volumn 15, Issue 1, 2011, Pages 29-41

  Fazeli, Seyyed Abdolreza ^a   Rezvantalab, Hossein ^a   Kowsary, Farshad ^a  

^a UNIVERSITY OF TEHRAN   (Iran)

Author keywords

Ammonia water; Artificial neural network; Combined cycle; Exergy efficiency

Indexed keywords

ABSORPTION COOLING; ABSORPTION REFRIGERATION; AMMONIA; AMMONIUM HYDROXIDE; EFFICIENCY; EXERGY; THERMOANALYSIS; TURBINES;

AMMONIA/WATER; ANALYSIS AND SIMULATION; COMBINED CYCLE; CYCLE PERFORMANCE; EXERGY EFFICIENCIES; POWER CYCLE; RANKINE; REFRIGERATION CYCLES; THERMO DYNAMIC ANALYSIS; THERMODYNAMIC SIMULATIONS;

NEURAL NETWORKS;

EID: 83155164445     PISSN: 03549836     EISSN: None     Source Type: Journal    
DOI: 10.2298/TSCI101102009F     Document Type: Article

References (30)

1. Kalina, A. I., Combined Cycle and Waste-Heat Recovery Power Systems Based on a Novel Thermodynamic Energy Cycle Utilizing Low-Temperature Heat for Power Generation, ASME Paper, 83-JPGC-GT-3, 1983
2. Maloney, J. D., Robertson, R. C., Thermodynamic Study of Ammonia-Water Heat Power Cycles, Oak Ridge National Laboratory Report, Oak Ridge, Tenn., USA., CF-53-8-43, 1953
3. Erickson, D. C., Anand, G., Kyung, I., Heat-Activated Dual-Function Absorption Cycle, ASHRAE Transactions, 110 (2004), 1, pp. 515-524
4. Amano, Y., et al., A Hybrid Power Generation and Refrigeration Cycle with Ammonia-Water Mixture, Proceedings, Joint Power Generation Conference ASME, IJPGC2000-15058, 2000, pp. 1-6
5. Xu, F., Goswami, D. Y., Bhagwat, S. S., A Combined Power/Cooling Cycle, Energy, 25 (2000), 3, pp. 233-246
6. 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), 5,pp. 385-393
7. Goswami, D. Y., et al., New and Emerging Developments in Solar Energy, Solar Energy, 76 (2004), 1-3, pp. 33-43
8. Tamm, G., et al., Theoretical and Experimental Investigation of an Ammonia-Water Power and Refrigeraton Thermodynamic Cycle, Solar Energy, 76 (2004), 1-3, pp. 217-228
9. Vidal, A., et al., Analysis of a Combined Power Andrefrigeration Cycle by the Exergy Method, Energy, 31 (2006), 15, pp. 3401-3414
10. Vijayaraghavan, S., Goswami, D. Y., A Combined Power and Cooling Cycle Modified to Improve Resource Utilization Efficiency Using a Distillation Stage, Energy, 31 (2006), 8-9, pp. 1177-1196
11. Martin, C., Goswami, D. Y., Effectiveness of Cooling Production with a Combined Power and Cooling Thermodynamic Cycle, Applied Thermal Engineering, 26(2006), 5-6, pp. 576-582
12. Sadrameli, S. M., Goswami, D. Y., Optimum Operating Conditions for a Combined Power and Cooling Thermodynamic Cycle, Applied Energy, 84 (2007), 3, pp. 254-265
13. Zheng, D., et al., Thermodynamic Analysis of a Novel Absorption Power/Cooling Combined-Cycle, Applied Energy, 83 (2006), 4, pp. 311-323
14. Liu, M., Zhang, N., Proposal and Analysis of a Novel Ammonia-Water Cycle for Power and Refrigeration Cogeneration, En ergy, 32 (2007), 6, pp. 961-970
15. Zhang, N., Lior, N., Development of a Novel Combined Absorption Cycle for Power Generation and Refrigeration, ASME Journal of Energy Resources Technology, 129 (2007), 3,pp. 254-265
16. Wang, J. F., Dai, Y. P., Gao, L., Parametric Analysis and Optimization for a Combined Power and Refrigeration Cycle, Applied Energy, 85 (2008), 11, pp. 1071-1085
17. Xu, F., Goswami, D. Y., Thermodynamic Properties of Ammonia-Water Mixtures for Power-Cycle Applications, Energy, 24 (1999), 6,pp. 525-536
18. Soleimani, A. G., Simple Equations for Predicting Entropy of Ammonia-Water Mixture, International Journal of Engineering, 20 (2007), 1, pp. 97-106
19. Patek, J., Klomfar, J., Simple Functions for Fast Calculations of Selected Thermodynamic Properties of the Ammonia-Water System, International Journal of Refrigeration, 31 (1995), 4, pp. 228-234
20. Thorin, E., Thermophysical Properties of Ammonia-Water Mixture for Prediction of Heat Transfer Areas in Power Cycles, International Journal ofThermophysics, 22 (2001), 1, pp. 201-214
21. Kalogirou, S. A., Applications of Artificial Neural-Networks for Energy Systems, Applied Energy Journal, 67 (2000), 1-2, pp. 17-35
22. Ertunc, H. M., Hosoz, M., Artificial Neural Network Analysis of a Refrigeration System with an Evaporative Condenser, Appl. Therm. Eng., 26 (2006), 5-6, pp. 627-635
23. Jambunathan, K., et al., Evaluating Convective Heat Transfer Coefficients Using Neural Networks, International Journal of Heat and Mass Transfer, 39 (1996), 11, pp. 2329-2332
24. Parlak, A., et al., Application of Artificial Neural Network to Predict Specific Fuel Consumption and Exhaust Temperature for Adiesel Engine, Appl. Therm. Eng., 26 (2006), 8-9, pp. 824-828
25. Arcaklioglu, E., Performance Comparison of CfCs with Their Substitutes Using Artificial Neural Network, Int. J. Energy Res., 28 (2004), 12, pp. 1113-1125
26. Bechtler, H., et al., New Approach to Dynamic Modeling of Vapor-Compression Liquid Chillers: Artificial Neural Networks, Applied Thermal Engineering, 21 (2001), 9, pp. 941-953
27. Pacheco-Vega, A., et al., Neural Network Analysis of Fin-Tube Refrigerating Heat Exchanger with Limited Experimental Data, International Journal ofHeat and Mass Transfer, 44 (2001), 4, pp. 763-770
28. 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), 1, pp. 59-66
29. Chow, T. T., et al., Global Optimization of Absorption Chiller System by Genetic Algorithm and Neural Network, Energy Buildings, 34 (2002), 1, pp. 103-109
30. Widrow, B., Hoff, M. E., Adaptive Switching Networks, in: Parallel Distributive Processing, Vol. 1, MIT Press, Cambridge, Mass., USA, 1986, pp. 318-362