Thermodynamic and Economic Assessment of Two Semi-Closed CO2 Cycles for Emission Abatement and Power Augmentation at Compressor Stations
Bibliographic record
Abstract
Concerns about the effects of greenhouse gas emissions on the Earth’s climate have lead to a considerable focus by the public and governments on the levels of emissions that are generated by industrial activities. In Canada, it has been recognized that gas transmission systems are rated second in overall CO2 production in the Natural Gas Industry (next to gas processing). Most of the gas transmission systems are powered by gas turbines at compressor stations resulting in significant CO2 emissions (at the rate of ∼ 6 kilo tonnes/ per MW-year). This can be reduced if the CO2 can be separated from the gas turbine exhaust stream and directed for reuse or sequestration. This paper presents results of techno-thermodynamic assessment of two power cycle adjustments to increase CO2 concentrations in the exhaust gas from turbines. The working fluid in the two semi-closed cycles are made rich in CO2, thus making it easy to capture the CO2 from the flue gas by means of physical absorption techniques rather than by the conventional expensive amine adsorption methods. Additionally, the CO2 rich working fluid is shown to give rise to a higher exhaust gas temperature from the gas turbine semi-closed cycles, allowing a steam bottom cycle to be effective in augmenting the power delivered by the entire system by 50%, hence contributing to reducing emission by increasing the overall thermal efficiency of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".