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Artificial neural network analysis of liquid desiccant dehumidification system

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  • Gandhidasan, P.
  • Mohandes, M.A.

Abstract

The dehumidification process involves simultaneous heat and mass transfer and reliable transfer coefficients are required in order to analyze the system. This has been proved to be difficult and many assumptions are made to simplify the analysis. The present research proposes the use of ANN based model in order to simulate the relationship between inlet and outlet parameters of the dehumidifier. For the analysis, randomly packed dehumidifier with lithium chloride as the liquid desiccant is chosen. A multilayer ANN is used to investigate the performance of dehumidifier. For training ANN models, data is obtained from analytical equations. Eight parameters are used as inputs to the ANN, namely: air and desiccant flow rates, air and desiccant inlet temperatures, air inlet humidity, desiccant inlet concentration, dimensionless temperature ratio, and inlet temperature of the cooling water. The outputs of the ANN are the water condensation rate and the outlet desiccant concentration as well as its temperature. ANN predictions for these parameters are validated well with experimental values available in the literature with R2 value in the range of 0.9251–0.9660. This study shows that liquid desiccant dehumidification system can be alternatively modeled using ANN with a reasonable degree of accuracy.

Suggested Citation

  • Gandhidasan, P. & Mohandes, M.A., 2011. "Artificial neural network analysis of liquid desiccant dehumidification system," Energy, Elsevier, vol. 36(2), pages 1180-1186.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:2:p:1180-1186
    DOI: 10.1016/j.energy.2010.11.030
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    References listed on IDEAS

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    1. Zhang, L.Z., 2006. "Energy performance of independent air dehumidification systems with energy recovery measures," Energy, Elsevier, vol. 31(8), pages 1228-1242.
    2. Kabeel, A.E., 2010. "Dehumidification and humidification process of desiccant solution by air injection," Energy, Elsevier, vol. 35(12), pages 5192-5201.
    3. Mohandes, M. & Rehman, S. & Halawani, T.O., 1998. "Estimation of global solar radiation using artificial neural networks," Renewable Energy, Elsevier, vol. 14(1), pages 179-184.
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    6. Ali, Ameer & Ishaque, Kashif & Lashin, Aref & Al Arifi, Nassir, 2017. "Modeling of a liquid desiccant dehumidification system for close type greenhouse cultivation," Energy, Elsevier, vol. 118(C), pages 578-589.
    7. Ali Pakari & Saud Ghani, 2022. "Regression Models for Performance Prediction of Internally-Cooled Liquid Desiccant Dehumidifiers," Energies, MDPI, vol. 15(5), pages 1-19, February.
    8. Zendehboudi, Alireza & Tatar, Afshin & Li, Xianting, 2017. "A comparative study and prediction of the liquid desiccant dehumidifiers using intelligent models," Renewable Energy, Elsevier, vol. 114(PB), pages 1023-1035.
    9. Luo, Yimo & Wang, Meng & Yang, Hongxing & Lu, Lin & Peng, Jinqing, 2015. "Experimental study of the film thickness in the dehumidifier of a liquid desiccant air conditioning system," Energy, Elsevier, vol. 84(C), pages 239-246.
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