Slurry-Phase Experiments as Screening Protocol for Bioremediation of Complex Hydrocarbon Waste
Bibliographic record
Abstract
Flare pits have been used by the upstream oil and gas industry for decades to store and/or burn produced fluids at well sites, compressor stations, and batteries. Waste at such sites, or flare pit (FP) waste, usually contains high levels of hydrocarbons, metals, and salts. At present, bioremediation by land application is the most common method practiced by the oil and gas industry to treat FP waste. High rate slurry-phase and solid-phase biotreatment methods are viable alternatives to the low cost yet inefficient land treatment option. An ultimate biotreatability screening tool is needed to assess the viability of each treatment method. The effects of salinity, nutrient, soil type, and temperature on the ultimate biotreatability of FP waste were investigated using 2 L slurry reactors. The results showed an initial decrease in petroleum concentrations; however, biodegradation decreased or ceased with time, leaving recalcitrant compounds. Within a week, the majority of saturated hydrocarbons degraded to low levels. Aromatics remained stable, while the level of polar compounds fluctuated. Temperature (30–40°C), salinity levels (up to 40 dS/m), and nutrient concentrations (above 350 mg N/L as ammonium nitrogen) exhibited no statistically significant effects on hydrocarbon degradation. The primary effect of waste composition was highly significant; with higher soil clay content resulting in lower biodegradation. Results indicate that slurry phase experiments may serve as a screening tool; however, caution should be exercised because slurries do not contain some of the microflora found in the solid phase (e.g., fungus).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".