Solar dryers: A review of mechanism, methods and critical analysis of transport models applicable in solar drying of product
Bibliographic record
Abstract
As the world transitions towards green energy sources solar drying has become a vital technology for sustainable agricultural production, offering a cleaner, more efficient alternative to traditional drying methods. Solar drying has been demonstrated to be a sustainable and eco-friendly drying process for drying and preserving agricultural products, offering advantages over traditional methods that include faster drying rates, improved product quality, and reduced energy costs. This review examines the mechanisms and methods applicable to solar drying, including indirect and direct solar drying, hybrid systems combining solar drying with other heating sources, and thermal storage materials to address challenges such as intermittent solar radiation. The designs of solar drying systems include various solar collector configurations, drying chamber geometries, and air conveyance mechanisms crucial for efficient drying. This review therefore explores different design approaches and their effects on drying performance, highlighting the importance of understanding the complex interactions between system components. Additionally, the approach for Energy and exergy analysis of solar drying systems was explored, providing insights into energy utilization and efficiency. Finally, this review elucidates the complex transport phenomena governing solar drying, including moisture diffusion, heat and mass transfer, and airflow patterns. It identifies knowledge gaps in existing models and future research directions in transport modelling phenomena to advance sustainable, efficient, and scalable solar drying techniques. • Solar dryer mechanisms and methods for food preservation reviewed. • Overview of direct, indirect, and hybrid solar drying systems provided. • Research on solar collector designs and configurations synthesized. • Key transport phenomena in solar drying discussed. • Knowledge gaps and future research directions in solar drying identified.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".