Improving the sustainability and effectiveness of photovoltaic evaporative cooling greenhouse in the Sahel
Bibliographic record
Abstract
Anthropogenic climate change has caused worldwide extreme weather events including droughts, floods and heatwaves. It disproportionately affects developing countries through food insecurity. Greenhouse is important and relevant to the food-energy-water security in many regions. This study investigates the thermal behavior of photovoltaic evaporative cooling greenhouse made with eco-friendly coolers. The cooling potential of local plant materials was assessed under ambient conditions. Experimental thermal data obtained from optimized evaporative cooling system equipped with Hyphaene thebaica fibers (HF-pad) and conventional Celdek pad (C-pad), were used in heat and mass transfer equations to derive the greenhouse cooling performances. Computational fluid dynamics analysis software was used to investigate the refrigerant fluid distribution in the greenhouse. Cooler using HF-pad allows to keep the microclimate below 25 °C, with maximum moisture rate up to 80%, under harsh ambient conditions (temperature: 30-45 °C, humidity: 10-15%). HF-pad had the highest cooling coefficient of performance (COP = 9 against 6 for C-pad), the best cost to efficiency ratio (CER = 5; 4 times less than C-pad) and the lowest outlet temperature (20.0 °C). Due to higher outlet air velocity (1.116 m/s against 0.825 m/s for HF-pad), C-pad cooler spread cool air (20.5 °C) up to 1.25 m farther than its counterpart, creating higher pressure in the atmosphere (1.42 Pa against 0.71 Pa), with 2 times turbulent kinetic energy (0.014 J/kg). HF-pad presented cooling performances that compete with conventional pads. Moreover, optimization of HF-pad frame engineering and the technology scaling up to industrial level can allow better thermal and economic performances.
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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 | 0.001 | 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".