Investigation of Methods of Enhancing the Performance of Propane Pre-Cooling Refrigeration Cycles in Natural Gas Liquefaction Processes
Bibliographic record
Abstract
Abstract This paper examines the viability of using low cost techniques to increase the performance (COP) of the overall natural gas liquefaction processes. Mixing propane with ammonia, sulfur dioxide or carbon dioxide and their effect on the compressor's required work is studied. The simulation results show that propane-ammonia and propane-sulfur dioxide mixtures enhance the COP by a maximum value of 7% and 9%, respectively. The COP enhancement in these blends is due to the closeness of their boiling temperatures with the operating temperature. However, the addition of carbon dioxide to the propane refrigerant reduces the COPof the cycle. This reduction is due to the poor energy absorption capabilities of carbon dioxide at the cycle operating temperature. The goal of this study is to investigate the feasibility of enhancing the COP using mixed refrigerants consisting of a hydrocarbon (propane) and natural fluids (ammonia, sulfur dioxide and carbon dioxide).
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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) | 0.001 | 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".