Energy, Exergy and Economic Analysis of a Mixed-mode Natural Convection Solar Tunnel Dryer for Banana Slices
Bibliographic record
Abstract
The energy, exergy and economic analysis of a mixed-mode natural convection solar tunnel dryer for drying banana slices is presented. The collector, drying chamber and chimney energy and exergy are analysed for a load of 3.75 kg of fresh banana slices. Further, the economic analysis in terms of Net Present Value (NPV), Profitability Index (PI), the Payback Period (PB) and the Discounted Payback Period are presented. Drying experiments were done in a period of two days. During the experiment, solar radiation varied from 206.1 W/m2 to 934.5 W/m2; drying chamber temperature varied from 51.7ºC to 81.84ºC while the ambient temperature varied from 21.7ºC to 31.9ºC. The relative humidity ranged from 4.63% to 28.46% for the drying chamber and, 14.9% to 31.8% for the ambient. Under these conditions, the moisture content of the bananas was reduced from 73.89% to 14.27% in seven hours of drying on the first day. Energy and exergy efficiencies ranged from 5.56% to 57.32%, and 0.33% to 2.81%, respectively on Day 1. For Day 2 the energy and exergy efficiencies were from 14.15% to 59.16%, and 0.75% to 3.76%, respectively. For the drying chamber, the efficiency increased gradually in the first two hours to a maximum of 5.36%, corresponding to the period when there was maximum evaporation. The chimney, which is a bare flat plate type without glazing and insulation, lost heat from its surfaces. Nonetheless, it generated enough buoyancy to move air through the dryer. With the solar dryer lifespan of 10 years and the Discounted Payback Period of four years, the project is attractive and worth investing in.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".