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Analysis of the Effect of the CaCl 2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model

Author

Listed:
  • Xiaoqing Wei

    (College of Civil Engineering, Hunan University, Changsha 410082, China)

  • Nianping Li

    (College of Civil Engineering, Hunan University, Changsha 410082, China)

  • Jinqing Peng

    (College of Civil Engineering, Hunan University, Changsha 410082, China)

  • Jianlin Cheng

    (College of Civil Engineering, Hunan University, Changsha 410082, China)

  • Lin Su

    (College of Civil Engineering, Hunan University, Changsha 410082, China)

  • Jinhua Hu

    (College of Civil Engineering, Hunan University, Changsha 410082, China)

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 (CaCl 2 ) 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 CaCl 2 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. CaCl 2 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 CaCl 2 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 CaCl 2 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 CaCl 2 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 CaCl 2 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.

Suggested Citation

  • Xiaoqing Wei & Nianping Li & Jinqing Peng & Jianlin Cheng & Lin Su & Jinhua Hu, 2016. "Analysis of the Effect of the CaCl 2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model," Sustainability, MDPI, vol. 8(5), pages 1-14, April.
  • Handle: RePEc:gam:jsusta:v:8:y:2016:i:5:p:410-:d:68974
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    Citations

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    Cited by:

    1. Yuyi Wang & Yahui Guo & Shunqiang Hu & Yong Li & Jingzhe Wang & Xuesong Liu & Le Wang, 2019. "Ground Deformation Analysis Using InSAR and Backpropagation Prediction with Influencing Factors in Erhai Region, China," Sustainability, MDPI, vol. 11(10), pages 1-23, May.
    2. Xiaoqing Wei & Nianping Li & Jinqing Peng & Jianlin Cheng & Jinhua Hu & Meng Wang, 2017. "Modeling and Optimization of a CoolingTower-Assisted Heat Pump System," Energies, MDPI, vol. 10(5), pages 1-18, May.
    3. Haolu Liu & Khurram Yousaf & Kunjie Chen & Rui Fan & Jiaxin Liu & Shakeel Ahmed Soomro, 2018. "Design and Thermal Analysis of an Air Source Heat Pump Dryer for Food Drying," Sustainability, MDPI, vol. 10(9), pages 1-17, September.
    4. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    5. Abdelazim Abbas Ahmed & Mohsen Assadi & Adib Kalantar & Tomasz Sliwa & Aneta Sapińska-Śliwa, 2022. "A Critical Review on the Use of Shallow Geothermal Energy Systems for Heating and Cooling Purposes," Energies, MDPI, vol. 15(12), pages 1-22, June.
    6. Xiangyu Yao & Rong Feng & Xiuzhen Li, 2024. "A Review on the Heat-Source Tower Heat Pump Systems in China," Energies, MDPI, vol. 17(10), pages 1-20, May.

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