Energy Tri-Generation: Combined Gas Cogeneration/Solar Cooling
Bibliographic record
Abstract
The article presents an analysis of a tri-generation system using a solar energy as primary energy to provide a reliable alternative to minimize energy consumption in countries with a large solar field as the MENA region. At the University of Nancy, within the faculty of Sciences and Technologies, we are experimenting energy tri-generation through solar cooling of water for air-conditioning and heat/electricity cogeneration using gas fuelled internal combustion engine. The technology used for refrigeration is combined thermal solar-adsorption cooling. The physical principle of an adsorption based refrigerating machine is in some respect similar to the operating principle of a conventional compression based refrigerating machine [1-4]. Both systems rely on a “condenser - expansion valve – evaporator” refrigerant cycle. However, the way the two systems achieve refrigerant’s vapor compression is quite different. While the conventional machine provides a “mechanical” compression of the refrigerant’s vapor (standard refrigerant), the sorption machine achieves rather a “thermal” compression of the refrigerant’s vapor (water) in two steps: in the first stage, the vapor issue from the evaporator is assimilated within a solid adsorbent (silica gel) by rejecting heat; in the second stage, this vapor is resituated back to the system loop at high pressure due to solar heat input through an intermediary fluid (water).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".