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Modeling and experimental validation of an indirect active hybrid solar dryer for mint leaves: Coupled thermal and moisture-transfer analysis
Faculty
Technology and Development
Year:
2026
Type of Publication:
ZU Hosted
Pages:
Authors:
Staff Zu Site
Abstract In Staff Site
Journal:
Energy Exploration & Exploitation Sage
Volume:
Keywords :
Modeling , experimental validation , , indirect active hybrid
Abstract:
A mathematical model was developed and experimentally validated to predict the thermal performance and drying behavior of an indirect active solar dryer (IAHSD) for mint leaves. The distinctive contribution of the proposed approach is its integration of solar-energy input, auxiliary gas heating, controlled fresh–recirculated air mixing, ambient-humidity effects, chamber heat losses, and mint-leaf moisture removal within a computationally accessible model suitable for operational assessment and control-oriented applications. The model describes coupled heat and mass transfer processes while considering key operating parameters, including drying air temperature (50–60°C), air recirculation ratio (70–90%), and ambient relative humidity (20–80%). Simulation results showed that increasing drying air temperature and recirculation ratio enhanced the drying chamber temperature, whereas higher ambient humidity reduced the thermal level and slowed moisture removal. Predicted chamber temperatures ranged from 37.83°C to 67.31°C depending on the inlet air temperature, while experimental values followed similar trends but were slightly lower due to environmental variations. Maximum temperatures occurred near midday, highlighting the influence of solar radiation on system performance. The model also captured moisture removal dynamics, indicating that higher drying temperatures accelerated drying rates, while elevated humidity reduced evaporation efficiency. Under low temperature and high humidity conditions, temporary moisture absorption was observed due to reversed vapor pressure gradients. Model validation showed strong agreement between predicted and measured data, with coefficients of determination (R2) ranging from 0.85 to 0.96, confirming the reliability of the proposed model.
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