Application of Binary Fluid Ejector in Thermal Vapor Compression Distillation Systems
Bibliographic record
Abstract
Ejector Refrigeration Systems (ERS) offer the use of low grade energy sources. These systems are simple in principle; however, suffer from relatively low COP, mainly due to inefficient energy exchange between the primary and secondary fluid flows in the ejector. The use of two chemically distinct fluids in forward and reversed Rankine cycles of such systems has been shown to improve COP. These systems are known as Binary Fluid ERS (BFERS). This paper focuses on the application of BFERS for the purpose of water distillation. First, a model for an ideal ejector was developed and the maximum theoretical COP of the system was determined. Actual COP of the system was also estimated by computing the entrainment ratio in the ejector using computational fluid dynamic modeling (CFD). The effects of certain fluid properties such as molecular masse and specific heat ratio of the two fluids were investigated with respect to mass entrainment ratio. A number of guidelines for the selection of suitable primary and secondary fluids were developed based on research literature and the potential for increasing COP through the use of chemically distinct fluids, i.e. binary operating fluids.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".