Design strategies for oxy-combustion power plant captured CO <sub>2</sub> purification
Bibliographic record
Abstract
Abstract This study presents a novel design and techno-economic analysis of processes for the purification of captured CO 2 from the flue gas of an oxy-combustion power plant fueled by petroleum coke. Four candidate process designs were analyzed in terms of GHG emissions, thermal efficiency, pipeline CO 2 purity, CO 2 capture rate, levelized costs of electricity, and cost of CO 2 avoided. The candidates were a classic process with flue-gas water removal via condensation, flue-gas water removal via condensation followed by flue-gas oxygen removal through cryogenic distillation, flue-gas water removal followed by catalytic conversion of oxygen in the flue gas to water via reaction with hydrogen, and oxy-combustion in a slightly oxygen-deprived environment with flue-gas water removal and no need for flue gas oxygen removal. The former two were studied in prior works and the latter two concepts are new to this work. The eco-technoeconomic analysis results indicated trade-offs between the four options in terms of cost, efficiency, lifecycle greenhouse gas emissions, costs of CO 2 avoided, technical readiness, and captured CO 2 quality. The slightly oxygen-deprived process has the lowest costs of CO 2 avoided, but requires tolerance of a small amount of H 2 , CO, and light hydrocarbons in the captured CO 2 which may or may not be feasible depending on the CO 2 end use. If infeasible, the catalytic de-oxygenation process is the next best choice. Overall, this work is the first study to perform eco-technoeconomic analyses of different techniques for O 2 removal from CO 2 captured from an oxy-combustion power plant.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".