4.7 Article

Thermo-economic analysis of solar drying of Jerusalem artichoke (Helianthus tuberosus L.) integrated with evacuated tube solar collector and phase change material

Journal

JOURNAL OF ENERGY STORAGE
Volume 52, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.est.2022.104688

Keywords

Artificial neural network; Exergy efficiency; Jerusalem artichoke payback time; Phase change material

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Funding

  1. Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kemran, Iran [7/S/98/790]

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In this study, experimental and analytical methods were used to investigate the drying process of Jerusalem artichoke slices using an indirect cabinet solar dryer with phase change materials. The results showed that the use of phase change materials significantly improved the overall drying efficiency and reduced the drying time.
In the present work, the experimental studies performed through the thermo-economical analysis of Jerusalem artichoke slices dried by an indirect cabinet solar dryer with evacuated tube collectors and phase change ma-terials. It shows that in the drying process of 5 mm thickness of Jerusalem artichoke slices the effect of using phase change materials in the solar dryer on the movement of the water content from the crops was significant (P < 0.05). The drying time decreased with increasing the airflow rate, but it was not significant (P > 0.05). Using phase change materials increased activation energy (33.4 kJ/mol) and improved the overall drying effi-ciency 1.51-7.81%. Specific energy consumption with PCM usage decreases from 14.51 to 13.38 MJ/kg. In addition the exergy efficiency for the drying process about 35.3-59.7%. The optimized condition for the system was obtained, and it showed that payback time for the solar dryer with optimized condition could be decreased to 22 months which was lower than basic condition (15%). Moreover, artificial neural network, computational fluid dynamic and evolutionary polynomial regression were considered to predict the variations of thermal behavior of the system. The values of RMSE and COV for artificial neural network method are lower and R-2 is higher than the computational fluid dynamic method. The result of evolutionary polynomial regression implied that it predicted outlet temperature, and the collector thermal efficiency with the acceptable accuracy (R-2 > 0.98). According to the exergetic, economic and quality consideration the proper condition was obtained with 0.9 kg/s for air flow rate assisted with PCM.

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