State of the Art of Remediation for Petroleum Industries
Bibliographic record
Abstract
Abstract The practice of environmental remediation itself has evolved from the 1980s and continues to evolve today. There is an increased level of awareness of the applicability and limitations of various remediation technologies. In the last decade, there has been significant improvement of remediation technologies from the early containment techniques to today's very aggressive site closure techniques. Many new and innovative technologies have been introduced to develop faster and more cost effective solutions for the petroleum industries. The sources of contamination from different areas of the petroleum industry will be discussed: EXPLORATION AND PRODUCTION REFINING TRANSPORTATION AND PIPELINES DEPOTS AND TERMINALS SERVICE STATIONS / USTS The contamination from these areas of petroleum industry will be presented including: contaminated soil and groundwater impacts, crude petroleum sludge, brines and drilling soil and artificial drilling additives. This paper will review the evolution of remediation technologies in the United States from the 1990s to the present time. The technologies that will be presented will include the following: Soil. Soil excavation, ex-situ and in-situ soil stabilization and solidification, soil capping, thermal treatment (on site and in-situ), soil washing, soil oxidation, bio-pile and bioremediation. Groundwater. Pump and treat, air sparging, soil vapor extraction (SVE), vacuum enhanced recovery, in-situ oxidation, thermal treatment and bioremediation. Selected case studies utilizing several of these remediation technologies will be presented.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".