Review of Hollow Fiber (HF) Membrane Filtration Technology for the Treatment of Oily Wastewater: Applications and Challenges
Bibliographic record
Abstract
Oily wastewater has been recognized as a threat to the environment due to its hazardous nature and it can negatively affect the ecosystem, and threaten wildlife and human health. Physical, chemical, and biological technologies demonstrated a mixed performance in oily wastewater treatment, and, therefore, a proper treatment technology for oily wastewater needs to be addressed. Membrane filtration using a hollow fiber (HF) membrane is a promising alternative to remove emulsified oil from oily wastewater. This review discusses different sources of oily wastewater, various treatment methods, and membrane technology. The assessment has been focused on the parameters affecting HF membrane performance and applications of HF membrane-based technology to treat oily wastewater. This review paper reveals that HF membrane filtration systems have been previously used for the treatment of oily wastewater in bench-scale studies and few pilot-scale applications, which proved to be favorable in the treatment of recalcitrant wastewater containing oil and high salinity. Limitations associated with membrane fouling and the reduction of membrane permeability and membrane lifespan can be tackled and alleviated through modifying membrane chemistry and adjusting operational parameters. The compilation of studies showed that a low food/microorganism (F/M) ratio, long solid retention time (SRT) with high sludge age, long hydraulic retention time (HRT), and moderate aeration were the preferred operational parameters when treating oily wastewater. Based on this review, future studies should focus on optimizing the hydrodynamic conditions of the HF system, the commercialization of modified HF membranes, and the utilization of green technology in HF membrane construction to broaden HF membrane technology applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".