CO2 Capture from Off-shore Gas Turbines Using Supersonic Gas Separation
Bibliographic record
Abstract
CO 2 capture from gas turbine based off-shore application face challenges such as size (foot-print), weight and stability (wave motion) in addition to the challenges faced by on-shore industry. Space- and weight challenges are given priority, and the size of the capture installations will be of importance when selecting capture technology rather than process efficiency alone. In this work, CO 2 capture from an FPSO turbine exhaust gas using a supersonic separator is investigated. To assess the operational performance of the capture process, a Laval nozzle (converging- diverging geometry) model is implemented and successfully integrated in a steady-state process flow sheet simulator. The model includes equilibrium thermodynamics describing freeze-out of dry ice from a gas mixture containing CO 2 . To determine under which conditions this process is thermodynamically and fluid dynamically feasible, different boundary conditions are explored. By integrating the supersonic separator unit in a flow sheet model, the interaction between the capture and the rest of the process is studied. The results indicate that supersonic expansion is a viable strategy for capturing CO 2 from off-shore gas turbines.
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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".