Direct Measurement of Fugitive Emissions of Hydrocarbons from a Refinery
Bibliographic record
Abstract
Refineries are a source of emissions of volatile hydrocarbons that contribute to the formation of smog and ozone. Fugitive emissions of hydrocarbons are difficult to measure and quantify. Currently these emissions are estimated based on standard emission factors for the type and use of equipment installed. Differential absorption light detection and ranging (DIAL) can remotely measure concentration profiles of hydrocarbons in the atmosphere up to several hundred meters from the instrument. When combined with wind speed and direction, downwind vertical DIAL scans can be used to calculate mass fluxes of the measured gas leaving the site. Using a mobile DIAL unit, a survey was completed at a Canadian refinery to quantify fugitive emissions of methane, C2+ hydrocarbons, and benzene and to apportion the hydrocarbon emissions to the various areas of the refinery. Refinery fugitive emissions as measured with DIAL during this demonstration study were 1240 kg/hr of C2+ hydrocarbons, 300 kg/hr of methane, and 5 kg/hr of benzene. Storage tanks accounted for over 50% of the total emissions of C2+ hydrocarbons and benzene. The coker area and cooling towers were also significant sources. The C2+ hydrocarbons emissions measured during the demonstration amounted to 0.17% of the mass of the refinery hydrocarbon throughput for that period. If the same loss were repeated throughout the year, the lost product would represent a value of US$3.1 million/yr (assuming US$40/bbl). The DIAL-measured hourly emissions of C2+ hydrocarbons were 15 times higher than the emission factor estimates and gave a different perspective on which areas of the refinery were the main source of emissions. Methods, such as DIAL, that can directly measure fugitive emissions would improve the effectiveness of efforts to reduce emissions, quantify the reduction in emissions, and improve the accuracy of emissions data that are reported to regulators and the public.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".