Atmospheric Dispersion of Gaseous Amine Emitted from Absorption-Based Carbon Capture Plants in Saskatchewan, Canada
Bibliographic record
Abstract
Carbon capture and storage (CCS) is a key strategy to reduce carbon dioxide (CO2) emissions from industrial point sources. Gas absorption into aqueous amine solutions is an immediate technology for carbon capture that has been tested in many demonstration plants. One concern of using the amine-based carbon capture process is the environmental impacts and health risk caused by emissions of gaseous amines from the process to the atmosphere. This work applied the knowledge of air dispersion modelling to map out the atmospheric dispersion and resulting ground surface level concentration of gaseous amine, namely Monoethanolamine (MEA), from a coal-fired power plant (with a carbon capture unit) and in surrounding areas, in case of an accidental leaking of amine from the CCS system to the atmosphere. The chosen study area was centered on a coal-fired power plant in the province of Saskatchewan, Canada. The Environmental Protection (EPA) approved air pollution model (CALPUFF), together with meteorological and geophysical data were used for gaseous amine dispersion simulation. The results were presented, and the ground amine concentrations were found to vary with wind patterns (wind direction and wind speed). The maximum ground surface amine concentrations standard is 15.2 µg/m3. However, the results showed that when using the water wash unit, the MEA concentrations were well below the standard level, compared to those without the water wash unit. It is essential for CO2 capture plants located in highly populated areas to be equipped with water wash units.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".