Novel Cascade Refrigeration Cycle for Cold Supply Chain of COVID-19 Vaccines at Ultra-Low Temperature -80°C Using Ethane (R170) Based Hydrocarbon Pair
Bibliographic record
Abstract
Several media report highlight on that the pharmaceutical companies require ultra-low temperatures -80°C to transport and store its COVID-19 vaccines. This research presents the thermodynamic analysis on cascade refrigeration system (CRS) with several refrigerant pairs which are R32/R170, R123/R170, R134a/R170, R404A/R170, R407c/R170, R410/R170, and the hydrocarbon (HC) refrigerant pair Propane/Ethane, namely R290/R170. Besides, the results of R22/R170 pair, which is not recommended to be used due to phase out of R22 as per Montréal Protocol, are included as base case to compare the novel hydrocarbon pairs in CRS and the old trend of refrigerant pairs. Thermodynamic properties of all these pairs were investigated and compared under different intermediate temperature used in CRS heat exchanger, which thermally connected both the Low and High temperature cycles (LTC) and (HTC). By applying the first law of thermodynamics, the coefficients of performance (COPs) and the specific power consumptions (SPC) in kW/TR are presented and compared. In addition, by applying the second law of thermodynamics the exergetic efficiencies were estimated. The results reveal the promising opportunity of using the HC pair (R290/R170). The minimum SPC in kW/TR is recorded for the pair R123/R170. One the other hand, the highest exegetic efficiency values are observed to be 40%, 38%, and 35% for the pairs R123/R170, R290/R170, and R134/R170, respectively. This research concludes that the HC pair (R290/R170) is highly recommended for CRS applications either to transport the COVID-19 or store it in cold storage rooms in hospitals and clinics. All precautionary measures should be carefully applied in design and operation of HC pair (R290/R170) due to its flammability hazard.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".