Ultrafiltration of oil-in-water emulsion using a 0.04-μm silicon carbide membrane: Taguchi experimental design approach
Bibliographic record
Abstract
ABSTRACT Oily wastewater as a by-product of the oil industry is becoming a major environmental concern. Finding effective means of treating and recycling the produced water is a key solution for the sustainability of the industry. Filtration experiments were performed to evaluate the performance of a new silicon carbide (SiC) ultrafiltration (UF) membrane in the separation of heavy oil from its brine. The Taguchi experimental design allowed for the investigation and determination of the optimal hydrodynamic conditions including transmembrane pressure (TMP), cross-flow velocity (CFV), temperature, and pH on the permeate flux, and also on the fouling resistance. In addition, the operating parameter with the greatest contribution to the permeate flux behaviour was determined using a statistical analysis of variance. The optimal operating conditions were found to be at 50°C, at a TMP of 0.9 bar, at a CFV of 0.5 m/s, and at a pH of 7. The TMP was found to have the utmost contribution to the permeate flux. Rejection capacity was also examined, and the SiC UF membrane achieved over 96% oil rejection, and one of the highest steady permeate flux levels for a UF membrane among what is published in the literature. Furthermore, models were used to investigate the fouling mechanisms involved in UF treatment of oily water. The cake formation model was found to be the best model for the correlation of the permeate flux decline.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".