Oily Sludge Degradation Study Under Arid Conditions Using Landfarm and Bioreactor Technologies
Bibliographic record
Abstract
Abstract Saudi Arabia is a major oil exporting country in the world with average production of approximately eight million barrels of crude oil every day. As a result of its operation, Saudi Aramco generates more than 30,000 cubic meters of oily sludge every year. The main disposal method for oily sludge in the Kingdom is landfarming, which is an attractive method due to the hot arid climatic conditions. Although landfarming technology was introduced to Saudi Arabia in 1982, the decision to use this technology was based on information obtained through studies conducted in other countries mainly in relatively cold climatic conditions. No scientific studies and/or research were conducted within the Kingdom of Saudi Arabia to support this decision. A field-scale study was conducted in the Juaymah area in the Eastern Province of Saudi Arabia to study the degradation of petroleum hydrocarbons under natural and enhanced conditions using landfarm and bioreactor technologies. The results presented in this paper are based on a comprehensive field experiment conducted under Saudi Arabian environmental conditions. Details of experimental setup and conceptual framework of degradation process based on field observations are presented. The paper also addresses kinetics of oily sludge degradation in landfarm and bioreactor cells under natural and enhanced conditions in the presence of water, nutrients and tilling. The results of this study revealed that weathering (evaporation) and not biodegradation was the dominant degradation mechanism. The results also showed that tilling was the main operating parameter responsible for achieving the highest percentage of reduction (76%) in the O&G concentrations in landfarms. The analytical results also revealed that due to the method of air addition, the bioreactor system was not effective in achieving a high percentage of O&G reduction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".