Energy, exergy, exergoenvironmental, and exergoeconomic (4E) analyses of a gas boosting station
Bibliographic record
Abstract
Abstract Energy, exergy, exergoenvironmental, and exergoeconomic analyses of a natural gas boosting station are presented using a real‐gas model and actual operational data. The effect of varies performance parameters on the thermodynamic efficiencies, specific fuel consumption (SFC), gas‐phase emissions, and cost rates are assessed. The results show that, for the actual operational conditions of the gas boosting station, the exergy efficiencies are 76.1% and 73.9% in the hot and cold seasons, respectively. Moreover, the energy analysis at partial load reveals that the SFC varies from 0.285 kg/kWh to 0.302 kg/kWh, respectively, in maximum and minimum ambient temperatures. The exergoeconomic analysis along with the exergoenvironmental analysis shows that the total cost rate of gas boosting stations in hot and cold ambient conditions is 7390 US$/h and 8070 US$/h, respectively, with more than 60% related to the environmental impact. In this system, the highest exergoeconomic factor is attributed to the centrifugal gas compressor at 40.9%‐44.2% and the lowest to the air cooler at 0.030%‐0.036%, depending on the ambient temperature, which specifies the balance between capital cost and the cost of exergy destruction. The cost rate of the exergy destruction is more pronounced in the combustion chamber, and the overall cost rate of the exergy destruction can be improved significantly by increasing turbine inlet temperature which needs additional investment cost for 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".