Effects of Impurities on CO<sub>2</sub> Pipeline Performance
Bibliographic record
Abstract
Carbon dioxide (CO2) is a chief constituent of greenhouse gases and should be captured, transported and stored in saline aquifers or used for enhanced oil recovery. This study is focused on pipeline transportation of impure CO2. The major impurities in captured CO2 from power plant stations and gas processing facilities are mainly nitrogen, methane, hydrogen sulphide, and water. Impurities affect the density and viscosity of the CO2 stream thereby impacting on the fluid phase, pressure and temperature of the stream. CO2 pipeline models, however, rarely consider the effects of impurities in the determination of design parameters. Aspen HYSYS (ver.9) is used to model the effect of impurities on the pressure drop, phase envelope and critical pressure and temperature of captured CO2 fluids flowing in pipelines. Cortez, Canyon Reef and Choctaw pipelines in the USA and Weyburn pipeline in Canada were selected as the case studies. The results show that the pressure drop increased in these pipelines due to the impurities with the highest pressure drop occurring in the Canyon Reef pipeline. The impurities increased the pressure drop by about 0.09 bar/km, 0.2 bar/km, 0.10 bar/km and 0.04 bar/km for Cortez, Canyon Reef, Choctaw and Weyburn pipelines respectively. The lower molecular weight gases were found to decrease the mixture density and increase the pressure drop. The results also reveal that the bubble point pressure was increased by impurities in three pipelines but slightly reduced in the Weyburn pipeline and the critical temperature was reduced in all pipelines.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".