Exploratory Experiments to Determine Effects of Injected Aerosolized Water, Hydrochloric Acid, and Sodium Chloride Solutions on Lab-Scale Flare Emissions
Bibliographic record
Abstract
During the flowback process of well-completion at hydraulic fracturing sites, there is the potential for liquid aerosol carry-over into gases sent to a flare.This thesis presents an exploratory investigation into the potential effects of non-hydrocarbon aerosols on flare emissions based on controlled experiments on lab-scale flares.Combustion emission and particulate matter optical properties were measured from lab-scale flares injected with industry relevant aerosolized liquids (water, aqueous HCl, and aqueous NaCl).Effects of liquid concentration, droplet size and liquid loading were compared to dry and distilled water base cases.Concentrations of NaCl (5%m and 15%m) and HCl (3.17%m and 9.51%m) solutions were chosen to match chlorine content, and liquid loadings up to 14.3% (kg/kg flare gas × 100%) were tested.Generally, water and HCl caused similar and relatively weak changes in gas-phase species yields relative to dry flame emission results.Conversely, NaCl solutions affected gas-phase measurements significantly: CO yields increased up to a factor of 25 and NOx yields decreased by 26% compared to that of dry flame results.Methane emissions were zero for all cases except those with injected NaCl aerosols.Particulate matter emission rates and optical properties were effectively identical for dry flames and flames with water and HCl aerosols at all tested liquid loadings and concentrations considered in the study.However, NaCl test results indicated that liquid loading and concentration affected both the amount and form of the particulate.Absorption coefficient, scattering coefficient, and soot yield results indicated that these augmented To my supervisor, Prof. Johnson, thank you for being supportive, encouraging, and for always taking the time to work through all the issues I ran into.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".