期刊
SOLAR ENERGY
卷 257, 期 -, 页码 45-57出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.solener.2023.04.024
关键词
Artificial neural network; Bifacial photovoltaic thermal; Food drying; Greenhouse dryer; Multi -objective genetic algorithm; Phase change material
A modified hybrid greenhouse dryer with a thermal energy storage system and bifacial photovoltaic thermal panels was studied. The system effectively minimized heat losses and improved operating hours. The results showed that the dryer was sustainable, providing electrical power even during off sunshine hours, and the drying room temperature was significantly higher than the ambient temperature.
A greenhouse dryer is the most economical and environmentally friendly device used to dry various products like fruits, vegetables and meats. The operating hours and heat losses from the north wall are the main concerns for its performance. To minimize the heat losses and improve the operating hours, the present study is undertaken on a modified hybrid greenhouse dryer with a thermal energy storage system and a bifacial photovoltaic thermal for electrical (BIFPVT) and thermal energy. A thermocol was wrapped in an aluminium foil (As a reflector) and placed on the north wall to minimize energy losses. The integration of PCM made the system sustainable to use while off sunshine hours as it stores the thermal energy during daytime. It was observed that the BIFPVT system provided electrical power ranging from 2.0 to 85.5 W and 0.6-81 W with respect to solar radiation intensity on the front (IF) and rear (IR) side of BIFPVT which varied from 34-950 W/m2 and 13-350 W/m2, respectively. The drying room temperature was found to be 16-44% higher than the ambient temperature for both days. Further, the experiment result was trained using an artificial neural network and optimized with the help of multi -objective genetic algorithm tools.
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