Prediction of liquid and vapor enthalpies of ammonia-water mixture

Energy Sources, Part A: Recovery, Utilization and Environmental Effects

Volumn 33, Issue 15, 2011, Pages 1463-1473

  Sencan A ^a   Gok S ^a   Dikmen, E ^a  

^a SULEYMAN DEMIREL UNIVERSITY   (Turkey)

Author keywords

ammonia water; liquid enthalpy; neural network; thermodynamic properties; vapor enthalpy

Indexed keywords

ABSORPTION CHILLERS; ALTERNATIVE METHODS; AMMONIA WATER; AMMONIA-WATER MIXTURE; ARTIFICIAL NEURAL NETWORK; CORRELATION COEFFICIENT; LIQUID ENTHALPY; WORKING FLUID;

AMMONIA; ENTHALPY; LIQUIDS; MIXTURES; NEURAL NETWORKS; THERMODYNAMICS; WATER ABSORPTION;

WATER VAPOR;

EID: 79957523432     PISSN: 15567036     EISSN: 15567230     Source Type: Journal    
DOI: 10.1080/15567030903397891     Document Type: Article

References (18)

1. ASHRAE. 1997. ASHRAE Handbook-Fundamentals. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.
2. Bechtler, H., Browne, M. W., Bansal, P. K., and Kecman, V. 2001. New approach to dynamic modelling of vapour-compression liquid chillers: Artificial neural networks. Appl. Therm. Eng. 21:941-953. (Pubitemid 32272869)
3. Chouai, A., Laugier, S., and Richon, D. 2002. Modeling of thermodynamic properties using neural networks application to refrigerants. Fluid Phase Equilib. 199:53-62. (Pubitemid 34621375)
4. Darwish, N. A., Al-Hashimi, S. H., and Al-Mansoori, A. S. 2008. Performance analysis and evaluation of a commercial absorption-refrigeration water-ammonia (ARWA) system. Int. J. Ref. 31:1214-1223.
5. Eslamloueyan, R., and Khademi, M. H. 2009. Estimation of thermal conductivity of pure gases by using artificial neural networks. Intl. J. Therm. Sci. 48:1094-1101.
6. Fu, L. M. 1994. Neural Networks in Computer Intelligence. Singapore: McGraw-Hill International Editions.
7. Haykin, S. S. 1999. Neural Networks: A Comprehensive Foundation. Upper Saddle River, NJ: Prentice Hall.
8. Herold, K. E., Radermacher, R., and Klein, S. A. 1996. Absorption Chillers and Heat Pumps. New York: CRC Press.
9. Kalogirou, S. A. 2000a. Artificial neural networks in renewable energy systems applications: A review. Renew. Sustain. Energy Rev. 5:373-401.
10. Kalogirou, S. A. 2000b. Long-term performance prediction of forced circulation solar domestic water heating systems using artificial neural networks. Appl. Energy 66:63-74. (Pubitemid 30157339)
11. Lazzús, J. A. 2009. Prediction of solid vapor pressures for organic and inorganic compounds using a neural network. Thermochimica Acta 489:53-62.
12. Lin, C. T., and Lee, C. S. G. 1996. Neural Fuzzy Systems. Upper Saddle River, NJ: PTR Prentice Hall.
13. Pacheco-Vega, A., Sen, M., Yang, K. T., and McClain, R. L. 2001. Neural network analysis of fintube refrigerating heat exchanger with limited experimental data. Int. J. Heat & Mass Transfer 44:763-770. (Pubitemid 32152907)
14. Sözen, A., Arcaklioǧlu, E., and özalp, M. 2004a. Performance analysis of ejector absorption heat pump using ozone safe fluid couple through artificial neural networks. Energy Convers. Manage. 45:2233-2253.
15. Sözen, A., özalp, M., and Arcaklioǧlu, E. 2004b. Investigation of thermodynamic properties of refrigerant/absorbent couples using artificial neural networks. Chem. Eng. Proc. 43:1253-1264. (Pubitemid 39112634)
16. Sözen, A., and Akçayol, M. A. 2004. Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle. Appl. Energy 79:309-325. (Pubitemid 39026948)
17. Tsoukalas, L. H., and Uhrig, R. E. 1997. Fuzzy and Neural Approaches in Engineering. New York: John Wiley & Sons Inc.
18. Yamankaradeniz, R., Horuz, I., and Coşkun, S. 2002. Soǧutma Tekniǧi ve Uygulamalari. VIPAş A. ş. Uludaǧ üniversitesi, Bursa (in Turkish).