Analysis of Wastewater Membrane Pollutants in Joint Station and Research on Biological Control Technology
Bibliographic record
Abstract
In view of the serious membrane pollution and short stable operation period of the existing heavy oil wastewater treatment process in a combined station of an oil field, the organic pollution is determined to be the main cause of the membrane pollution on the basis of the analysis of the wastewater quality and membrane pollutants. In view of the characteristic pollutants in heavy oil wastewater, the efficient degrading bacteria were screened by restrictive culture technology, and the strains were identified as Pseudomonas and Bacillus. The effects of environmental conditions on the growth of bacteria and the degradation of heavy oil by bacteria were investigated. In the existing process, biological treatment unit was added, simulation process was established, and long-term operation experiment was carried out. The results show that after biological treatment, the oil content of wastewater is less than 1 mg/L, COD is less than 100 mg/L, suspended matter is less than 1 mg/L, and the median particle size is 0.92 um, which is better than the first kind of water injection index (SY/T 5329-2012). Biological treatment effectively degraded organic pollutants in heavy oil wastewater and delayed membrane fouling. The loss rate of membrane flux was less than 15%. The microscopic morphology of membrane surface also showed that membrane fouling was effectively suppressed.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".