Enhanced exergy analysis of a bubble-pump-driven LiCl-H<SUB align="right">2O absorption air-conditioning system
Bibliographic record
Abstract
A thermally-driven bubble pump can replace the electrical pump in the absorption refrigeration cycle for lifting solution from the absorber to the generator and producing the required refrigerant vapour for the cooling effect. However, the lower efficiency of bubble-pump-driven absorption refrigeration cycle hinders its widespread application. The potential of a bubble-pump-driven LiCl-H2O absorption refrigeration system that can be powered by solar thermal energy or waste heat energy is discussed and analysed in this study. The new concept of enhanced exergy analysis is integrated in the thermodynamic analyses, which quantifies the available exergy destruction of each component for overall system performance improvement. The analyses uncovered that 80% of the total exergy loss is due to each component's own internal irreversibilities, whereas the remaining is through the interaction of the irreversibilities of other components in the system. The analyses revealed that though the highest exergy losses (46%) are in the absorber, priority for improvement should be given to the generator. Furthermore, the exergy losses of the condenser, the evaporator, and the solution heat exchanger are mostly unavoidable and can be reduced by improving the other components of the system.
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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) | 0.002 | 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".