Design, Experimental Evaluation, Thermal Efficiency and Economic Performance of Kapenta Fish Greenhouse Solar Dryer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it