Self-sustaining smouldering combustion for the treatment of industrial oily waste
Bibliographic record
Abstract
Significant onsite handling and offsite management costs are incurred by oilfield operators annually to properly manage hydrocarbon waste streams such as tank bottoms or other oily sludge or oil impacted soil generated during oil and gas production processes. The current study reports for the first-time technical results of a field trial on use of a smouldering combustion technology performed in an active oilfield. Two treatment batches with oily sludges, stabilized through blending with soil, resulted in permanent hydrocarbon removal (98–99.9% reduction) to create treated soil that met standards for reuse as clean backfill onsite. Emissions profile data collected pre- and post-thermal oxidizer indicated effective removal of volatile organic compounds, CO and SO 2 , but had increased NO and CO 2 due to combustion of propane to affect the thermal oxidation. Regulatory, financial, environmental and safety considerations are discussed in context of future full-scale smouldering technology deployment. The technology has the potential to lower overall unit costs for management of hydrocarbon impacted waste and reduce waste sent to landfills, which can benefit more remote sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".