Design, Experimental Evaluation, Thermal Efficiency and Economic Performance of Kapenta Fish Greenhouse Solar Dryer
Bibliographic record
Abstract
A natural convection greenhouse solar dryer for Kapenta fish was designed and evaluated for its effectiveness in drying performance, thermal efficiency, specific energy consumption, and economic viability in terms of net present value and payback period. The system featured a 1.0 m × 0.9 m drying tray and a 1.5 m² greenhouse floor area, with 40% of the surface exposed for additional solar heating. Constructed from LDPE film, timber, HDPE components, rocks, mosquito netting, and a zipper, the dryer was optimized for efficient airflow, heat retention, and user convenience. Natural convection facilitated continuous airflow, as heated air exited through a top outlet while cooler ambient air entered from the bottom. Internal temperatures ranged from 49 °C to 60 °C, sustained by heat-retaining rocks that extended drying beyond peak sunlight hours. During testing, a 3 kg batch of fish with an initial moisture content of 76.7% was dried to 2.1% (wet basis) within 4.5 hours, compared to 14.3% moisture under open sun drying. The system achieved a thermal efficiency of 22.3% and a specific energy consumption of 2.81 kWh/kg, with an average airflow rate of 0.021 kg/s. Even under moderate solar radiation (773.9 W/m²) and ambient temperatures (19.1 °C), the dryer performed effectively, allowing up to two drying cycles per day. With a payback period of only 1.2 years and nearly nine years of debt-free operation, the system offers a sustainable, low-cost, and practical solution for Kapenta fish preservation in solar-rich regions with limited low-temperature infrastructure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".