Study on the Performance of the Solar Cooling System Using Cavity Receiver Based on the Neural Network

• Published in: Energy procedia Vol. 70; pp. 275 - 284
• Main Authors: Li, Haoju, Wang, Shuhuai, Zhang, Zheng, Xu, Xu, Song, Kai
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2015
• Subjects: cavitysolar receiver; double-effect absorption; RBF neural network; solar refrigeration; cavitysolar receiver; RBF neural network; double-effect absorption; solar refrigeration
• ISSN: 1876-6102; 1876-6102
• DOI: 10.1016/j.egypro.2015.02.124

A solar absorption cooling system with cavity receiverfor demonstration was introduced. The system consisted of a double-effectlithium bromide chiller and a parabolic trough concentrator with cavitysolar receiver.The high temperature heat from the cavity receiver enabled the chiller to operate in double-effect state,and the refrigerant water produced by the refrigerator flowed through the end fan coil toadjust the room temperature.Monitoring data from the demonstration system showed that instantaneous efficiency of the solar cavity receiverwas about 44%in direct solar irradiance of about 500W/m2, the receiver outlet temperature was150oC and thetemperature difference between the receiver outlet and the receiverinlet was 10oC.The lithium bromide refrigerator operated in the state of double effect in good solar irradiation, the refrigeration efficiency was about 1.2, and chilled watertemperature in the storage tank was about 15oC, and the room temperature was maintained at about 25oC. The experimental results showed that the design system could meet the demand of refrigeration and it was verified that the cavity receiver used in double-effect lithium bromide absorption refrigeration scheme was suitable.The relationships between the heat gain, inlet temperature, outlet temperature and flow rate of the working medium in the solar cavity receiver were analyzed, and the main influence factors on the heat gain of the solar cavity receiver were determined. On this basis, acavity receiver heat gain prediction model was establishedbased on a RBF neural network.The simulation results showed that the model predicted values and the measured values had good consistency,so the RBF neural network model could accurately predict the temperature rise of working medium of the solar cavity receiver.