Crude oil removal from water: Influence of organic phase composition and mineral content
Bibliographic record
Abstract
Abstract The effect of organic and inorganic compounds, commonly present in the mineralogy of crude oil and/or added in the washing processes of extracted crude, on the removal efficiency of emulsified oils present in waste washing waters was investigated by means of flocculation. Approximately 90% of the emulsified oil could be removed using an anionic flocculant, providing a residual turbidity below 100 NTU. The yield depended on the nature of the organic and inorganic components present. The higher the chain length of the main organic component, the greater the flocculant concentration required to remove the oil. Several components had an effect of emulsification (e.g., octane, decane), some of which rendered de‐oiling process completely ineffective (e.g., naphthenic acids). Aliphatics were the most difficult to eliminate, requiring flocculant levels in the 200–300 ppm range. This is in contrast to 75–100 ppm levels which were required to remove bi‐ and poly‐cyclic aromatics. Heavy oils were more difficult to remove than light oils. There was a strong effect of the pH of the aqueous phase. The optimum was pH = 2.0. Virtually all inorganic compounds reduced the efficiency of removing oil from water when spiked at 1%. The only exception was sodium carbonate which acted as a de‐emulsifier. Monovalent salts have a minor effect on de‐oiling, with efficiencies remaining at 80%. Divalent chlorides reduced the de‐oiling efficiency to 70% while sulphates had a more severe influence. The de‐oiling efficiency was lowered substantially with the addition of clays, zinc, cadmium, ferric oxide, calcium carbonate, and dibenyhlthiophene.
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 | 0.001 |
| 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 | 0.000 | 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".