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Sustainability 2016, 8(5), 410; doi:10.3390/su8050410

Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model

College of Civil Engineering, Hunan University, Changsha 410082, China
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Authors to whom correspondence should be addressed.
Academic Editors: Marc A. Rosen, Lin Lu, Jinqing Peng and Fu Xiao
Received: 30 November 2015 / Revised: 21 April 2016 / Accepted: 22 April 2016 / Published: 26 April 2016
(This article belongs to the Special Issue Renewable Energy Applications and Energy Saving in Buildings)
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Abstract

An existing idle cooling tower can be reversibly used as a heat-source tower (HST) to drive a heat pump (HP) in cold seasons, with calcium chloride (CaCl2) aqueous solution commonly selected as the secondary working fluid in an indirect system due to its good thermo-physical properties. This study analyzed the effect of CaCl2 mass fraction on the effectiveness (ε) of a closed HST and the coefficient of performance (COP) of a HP heating system using an artificial neural network (ANN) technique. CaCl2 aqueous solutions with five different mass fractions, viz. 3%, 9%, 15%, 21%, and 27%, were chosen as the secondary working fluids for the HSTHP heating system. In order to collect enough measured data, extensive field tests were conducted on an experimental test rig in Changsha, China which experiences hot summer and cold winter weather. After back-propagation (BP) training, the three-layer (4-9-2) ANN model with a tangent sigmoid transfer function at the hidden layer and a linear transfer function at the output layer was developed for predicting the tower effectiveness and the COP of the HP under different inlet air dry-/wet-bulb temperatures, hot water inlet temperatures and CaCl2 mass fractions. The correlation coefficient (R), mean relative error (MRE) and root mean squared error (RMSE) were adopted to evaluate the prediction accuracy of the ANN model. The results showed that the R, MRE, and RMSE between the training values and the experimental values of ε (COP) were 0.995 (0.996), 2.09% (1.89%), and 0.005 (0.060), respectively, which indicated that the ANN model was reliable and robust in predicting the performance of the HP. The findings of this paper indicated that in order to guarantee normal operation of the system, the freezing point temperature of the CaCl2 aqueous solution should be sufficiently (3–5 K) below its lowest operating temperature or lower than the normal operating temperature by about 10 K. The tower effectiveness increased with increasing CaCl2 mass fraction from 0 to 27%, while the COP of the HP decreased. A tradeoff between the tower effectiveness and the COP of the HP should be considered to further determine the suitable mass fraction of CaCl2 aqueous solution for the HSTHP heating system. The outputs of this study are expected to provide guidelines for selecting brine with an appropriate mass fraction for a closed HSTHP heating system for actual applications, which would be a reasonable solution to improve the system performance. View Full-Text
Keywords: heat-source-tower heat pump; calcium chloride (CaCl2) aqueous solution; artificial neural network; tower effectiveness; coefficient of performance (COP) heat-source-tower heat pump; calcium chloride (CaCl2) aqueous solution; artificial neural network; tower effectiveness; coefficient of performance (COP)
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MDPI and ACS Style

Wei, X.; Li, N.; Peng, J.; Cheng, J.; Su, L.; Hu, J. Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model. Sustainability 2016, 8, 410.

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