Technology Platform ENERBAT - Gas Cogeneration, Solar Heating and Cooling
Bibliographic record
Abstract
Energy cogeneration is a way to improve global efficiency of energy production systems since it consumes a unique resource in order to supply heat and electrical power through optimal use of heat fluxes associated to power production. Energy trigeneration enlarges the concept to the production of cold also. It consumes a unique resource to produce electricity, heat and cold. Nevertheless, we could go more ahead by substituting a part of the primary fuel resource by renewable energy as solar energy in order to reduce the carbon impact. This is conducted through the use of adsorption refrigeration which needs hot water to produce cold water. However, even if energy utilities are provided with the best efficient way, the final use of energy could make all the efforts fall. Cooling ceilings present one of the best solutions to be coupled to solar cooling since it needs a medium range cold temperature of the fluid in order to avoid condensation if the wall ceiling temperature drops below the ambient air dew point temperature. All these constraints need to be checked experimentally and confronted to numerical simulation. For this purpose, an experimental platform has been developed combining an internal combustion gas engine (cogenerator), a refrigerating adsorption machine, thermal solar collectors and wooden construction split in two compartment, a cold one conditioned by cooling ceilings and a hot one conditioned by heating floors. The platform is completely instrumented. In this paper we focus only on the refrigeration machine for which we developed a simulation model that is confronted to experimental measurements.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.017 |
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".