Share This Article:

Optimization of Desiccant Absorption System Using a Genetic Algorithm

Abstract Full-Text HTML Download Download as PDF (Size:1482KB) PP. 527-533
DOI: 10.4236/jsea.2011.49061    5,380 Downloads   9,268 Views   Citations
Author(s)    Leave a comment

ABSTRACT

Optimization of the open absorption desiccant cooling system has been carried out in the present work. A finite difference method is used to simulate the combined heat and mass transfer processes that occur in the liquid desiccant regenerator which uses calcium chloride (CaCl2) solution as the working desiccant. The source of input heat is assumed to be the total radiation incident on a tilted surface. The system of equations is solved using the Matlab-Simulink platform. The effect of the important parameters, namely the regenerator length, desiccant solution flow rate and concentration, and air flow rates, on the performance of the system is investigated. In order to optimize the system performance, a genetic algorithm technique has been applied. The system coefficient of performance COP has been maximized for different design parameters. It has been found that the maximum values of COP could be obtained for different combinations of regenerator length solution flow rate and air flow rate. Therefore, it is essential to select the design parameters for each ambient condition to maximize the performance of the system.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

A. Aly, "Optimization of Desiccant Absorption System Using a Genetic Algorithm," Journal of Software Engineering and Applications, Vol. 4 No. 9, 2011, pp. 527-533. doi: 10.4236/jsea.2011.49061.

References

[1] A. Kakabaev and A. Khandurdyev, “Absorption Solar Refrigeration Unit with Open Regeneration of Solution,” Gliotekhnika, Vol. 5, No. 4, 1969, pp. 28-32.
[2] R. Yang and P. L. Wang, “Experimental Study of a Forced Convection Solar Collector/Regenerator for Open Cycle Absorption Cooling,” Transactions of the ASME Journal of Solar Energy Engineering, Vol. 116, 1994, pp. 194-199. doi:10.1115/1.2930081
[3] G. Grossman, “Solar-Powered Systems for Cooling, Dehumidification and Air-Conditioning,” Solar Energy, Vol. 72, No. 1, 2002, pp. 53-62. doi:10.1016/S0038-092X(01)00090-1
[4] M. Krause, W. Saman and K. Vajen, “Open Cycle Liquid Desiccant Air Conditioning Systems-Theoretical and Experimental Investigations,” ANZSES Conference, Dunedin, New Zealand, 2005.
[5] K. Daou, R. Z. Wang and Z. Z. Xia, “Desiccant Cooling Air Conditioning: A Review,” Renewable and Sustainable Energy Reviews, Vol. 10, 2006, pp. 55-77. doi:10.1016/j.rser.2004.09.010
[6] J. Dieckmann, K. Roth and J. Brodrick, “Liquid Desiccant Air Conditioners,” ASHRAE Journal, Vol. 50, No. 10, 2004, pp. 90-95.
[7] H. Factor and G. A. Grossman, “A Packed Bed Dehumidifier/Regenerator for Solar Air Conditioning with Liquid Desiccant,” Solar Energy, Vol. 24, 1980, pp. 541-550. doi:10.1016/0038-092X(80)90353-9
[8] R. Yang and P. L. Wang, “A Simulation Study of the Performance Evaluation of Single-Glazed and Double-Glazed Collectors/Regenerators for an Open-Cycle Absorption Solar Cooling System,” Solar Energy, Vol. 71, No. 4, 2001, pp. 263-268. doi:10.1016/S0038-092X(01)00047-0
[9] S. Alizadeh and W. Saman, “Modeling and Performance of a Forced Flow Solar Collector/Regenerator Using Liquid Desiccant,” Solar Energy, Vol. 72, No. 2, 2002, pp. 143-154. doi:10.1016/S0038-092X(01)00087-1
[10] A. A. Aly, E. B. Zeidan and A. M. Hamed, “Performance Evaluation of Open-Cycle Solar Regenerator Using Artificial Neural Network Technique,” Journal of Energy and Buildings, Vol. 43, 2011, pp. 454-457.
[11] J. R. Koza, M. A. Keane, M. J. Streeter, W. Mydlowec, J. Yu and G. Lanza, “Genetic Programming IV: Routine Human-Competitive Machine Intelligence,” Kluwer Academic Publishers, Boston, 2003.
[12] W. L. McCabe, J. C. Smith and P. Harriott, “Unit Operation of Chemical Engineering,” McGraw-Hill, New York, 1985.
[13] C. R. Reeves, “Using Genetic Algorithms with Small Populations,” Proceedings of the 5th International Conference on Genetic Algorithms, 1993, pp. 92-99.
[14] D. E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning,” Addison-Wesley, Boston, 1989.

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.