Characterizing agricultural product drying in solar systems using thin-layer drying models: comprehensive review
Abstract Thin Layer drying models provide a comprehensive framework for understanding drying processes, estimating drying times, and developing generalized drying curves of the agricultural products. Most existing reviews on thin-layer drying models do not focus on specific drying mechanisms. Instea...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-03-01
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| Series: | Discover Food |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44187-025-00362-1 |
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| Summary: | Abstract Thin Layer drying models provide a comprehensive framework for understanding drying processes, estimating drying times, and developing generalized drying curves of the agricultural products. Most existing reviews on thin-layer drying models do not focus on specific drying mechanisms. Instead, they discuss thin-layer drying models across various drying methods. In contrast, this review specifically focuses on the application of thin-layer drying models to agricultural products dried in solar drying systems. The review presents a comprehensive bibliometric analysis of research on thin-layer drying models used to simulate the drying behaviour of agricultural products in solar drying systems. The findings indicate that publication activity in this field began in 1976 and has grown significantly, peaking in 2021. India leads in contributions followed by China. The review underscores the critical role of factors such as inlet air temperature and pretreatments in enhancing the drying process. The selection of an optimal drying model depends on the unique properties of the agricultural product, specific drying conditions, and the model's ability to predict moisture removal under varying environmental factors accurately. Among the various models, the Midilli et al. model has demonstrated effectiveness across a diverse range of agricultural products. The review also highlighted the role of artificial neural networks (ANNs) in improving the prediction of drying behavior for agricultural products in solar drying methods. It also outlined future research directions for ANNs as a tool in this field. |
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| ISSN: | 2731-4286 |