New Approach in the Energy Analysis of an NH3-NaSCN Single Effect Absorption Machine (SEAM) with a Counter-Current Coaxial Tubes Internal Heat Exchanger (3CT-IHE)
Bibliographic record
Abstract
Energy analysis provides the ability to assess performance and determine the size of equipment in a system. However, precisely integrating the thermal behavior of the component equipment makes it possible to better assess the performance of the system. This work highlights the influence of the thermal behavior of a 3CT-IHE on the performance and size of the equipment of SEAM with NH3-NaSCN as a working fluid. Based on equations of conservation balances of mass, species and energy, the energy analysis made showed that depending on the magnitude of the heat flows of the weak and strong solutions involved in the heat exchange at the level of the 3CT-IHE, there are different temperature ranges for which the weak solution or the strong solution governs the heat transfer, thus giving rise to the notions of “thermal heating efficiency” and “thermal cooling efficiency” respectively. The results obtained were compared with those of Sun. This new approach has demonstrated better performance of the SEAM for the same operating conditions. It has also been shown that it is possible to increase the temperature of the generator above 90 °C without resorting to another working fluid and that in the case of subjecting NH3-NaSCN to supercritical conditions in temperature at the generator; it is possible to obtain a machine COP greater than 1 and an even smaller pump size.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".