Effect of wettability on saturation distribution and separation performance in oily wastewater treatment using mixed woven fibre bed coalescers
Bibliographic record
Abstract
Abstract Fibre bed coalescers are widely utilized in oil–water separation applications. Due to the diverse nature of oil–water emulsions and the complexity of the separation process mechanism, the design and operation of fibre bed coalescers still rely heavily on empirical data, warranting further investigation. In this study, two types of polymer fibres with opposite wettability in oil‐in‐water environments were respectively combined with stainless steel fibres to prepare mixed woven fibre beds. The separation performance was compared by examine the saturation distribution in the wettable and nonwettable coalescing media. The effects of key factors influencing the separation process were also explored. The saturation profiles of the wettable and nonwettable fibre beds differed, with the wettable coalescing media showing more stable performance under high inlet oil concentration compared to the nonwettable media. Additionally, an industrial‐scale trial was conducted to treat oily wastewater generated in the process of organosilicon production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".