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Performance Evaluation and Drying Kinetics for Solar Drying of Hygroscopic Crops in Vacuum Tube Assisted Hybrid Dryer
被引:15
|作者:
Saxena, Gaurav
[1
]
Gaur, M. K.
[1
]
机构:
[1] Madhav Inst Sci & Technol, Dept Mech Engn, Gwalior 475005, Madhya Pradesh, India
来源:
JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME
|
2020年
/
142卷
/
05期
关键词:
agricultural drying;
solar-assisted system;
dryer performance;
drying kinetics;
mathematical modeling and neural network;
drying;
heating;
simulation;
MODEL;
QUALITY;
LEAVES;
SYSTEM;
D O I:
10.1115/1.4046465
中图分类号:
TE [石油、天然气工业];
TK [能源与动力工程];
学科分类号:
0807 ;
0820 ;
摘要:
The present experimentation work discloses drying of hygroscopic crops under the new concept of solar-assisted greenhouse type dryer integrated with evacuated tube water heating system to control and maintain the temperature of the greenhouse environment according to the regulated flowrate of heated water in the drying trays. The dryer consists of an evacuated tube solar collector, flow regulating device and drying bed with provision for the flow of heated water. The power supply for forced circulation of solar-heated water inside the copper tube as well as the greenhouse environment air is maintained by solar photovoltaic (PV) modules. The dryer is tested for drying two hygroscopic crops namely coriander and fenugreek. The drying performance of the hybrid system is evaluated in terms of mass reduction and its derived influence on moisture content and drying rate. The derived parameters are compared with the corresponding evaluations under open sun drying. The rise in the greenhouse environment temperature and the crop surface temperature at hourly intervals as compared to the ambient condition were used as parameters for the thermal performance of the dryer. The drying curve for change in mass shows complete drying time for coriander and fenugreek reduced by 3.5 and 2.5 h, respectively, for present sample sizes. The most suitable mathematical model was also regressed using matlab followed by the development of a neural network for more precise prediction of moisture ratio (MR) for present hybrid drying.
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页数:14
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