Dynamic behaviour simulation of a liquid desiccant dehumidification system

Dynamic behaviour simulation of a liquid desiccant dehumidification system
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DOI:
10.1016/j.energy.2017.11.161
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发表时间:
2018-02
期刊:
影响因子:
9
通讯作者:
A. Kabeel;A. Khalil;S. S. Elsayed-S.;A. M. Alatyar
A. Kabeel;A. Khalil;S. S. Elsayed-S.;A. M. Alatyar
中科院分区:
工程技术1区
文献类型:
--
作者:
A. Kabeel;A. Khalil;S. S. Elsayed-S.;A. M. Alatyar

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Among heat driven air conditioning systems, liquid desiccant air conditioning system can be assisted by low water temperature solar collectors. This can reduce the electricity demand of the air conditioning systems. Furthermore, these systems can provide possibility of energy storage in the form of chemical energy in the liquid desiccant solution or thermal energy in the solar collector hot water. This article simulates a liquid desiccant dehumidification cycle by two dynamic models: transient and discrete-steady state model. The transient model takes into consider the unsteady term in the governing equations for both the dehumidifier/regenerator model and also liquid-liquid heat exchanger model. While, in the other dynamic model, the steady state terms are used only. Correspondingly, a liquid desiccant dehumidification system was constructed, troubleshoot and field tested at summer conditions. According to the simulation results, the dynamic models are compatible with the variances in operating conditions and supply heating water. Also, the deviation between the humidity ratio results from simulation and field test effects on the simulated solution concentration with less than 4% deviation. The maximum deviation between the moisture removal rate results from simulation and test are −0.21 g/s in the dehumidifier and −0.5 g/s in the regenerator. Thus, the models of liquid desiccant system have reliability to predict the performance with solution storage mode.The comparison between simulation results shows that the disagreement between the two employed dynamic models is insignificant. However, the transient model reduces the computational time by 21% less than the discrete-steady state model. This can speed up the processing time.