Experimental evaluation of a solar-assisted heat pump system as a hybrid thermal reactor for energy-efficient drying of agricultural biomass
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Lohith Kumar P
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Sunil Kumar M
Abstract
This study investigates the design, operation, and thermal performance of a solar-assisted heat pump dryer (SAHPD), conceptualized as a low-temperature hybrid thermal reactor for efficient drying of agricultural biomass. Focused on post-harvest processing of cardamom, the system integrates a vapor compression heat pump with a flat-plate solar collector, aiming to enhance energy recovery and improve moisture removal efficiency under tropical climatic conditions. Experiments were conducted in Karnataka, India, using a 10 kg batch of fresh cardamom subjected to drying over an 8-h period. The system was tested in three configurations: solar drying (SD), heat pump drying (HPD), and the integrated SAHPD mode. Key environmental parameters, including ambient temperature, relative humidity, and solar radiation (with peaks up to 870 W/m2), were recorded in real-time. Drying kinetics, energy efficiency, and total moisture loss were evaluated for each mode. The integrated SAHPD system demonstrated superior reactor performance, achieving approximately 80 % moisture removal (about 6 kg), while maintaining product quality and significantly reducing drying time. Compared to standalone modes, the SAHPD showed improved thermal utilization and operational stability under fluctuating weather conditions. The experimental results position the SAHPD as a promising thermal reactor configuration for sustainable, energy-efficient drying of moisture-laden biomass. This study contributes to the growing body of work on thermal reactor engineering for agricultural processes, supporting the development of environmentally responsible and process-intensified technologies in biomass conversion and post-harvest systems.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: No Tools were employed.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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