Evaluation of Membrane Fouling for In-Line Filtration of Oil Sands Process-Affected Water: The Effects of Pretreatment Conditions
Bibliographic record
Abstract
Membrane filtration is an effective reclamation option for oil sands process-affected water (OSPW). However, fresh OSPWs contain suspended solids and inorganic constituents in suspended and dissolved forms that can severely foul membranes. Pretreatment of OSPW with coagulation-flocculation (CF) was investigated to determine the effects of different coagulant aids (anionic, cationic, and nonionic polymers) on membrane surface properties and fouling. Our results showed that CF pretreatment effectively enhanced nanofiltration (NF) and reverse osmosis (RO) membrane permeate flux and salt rejection ratio through reducing membrane fouling. It was shown that coagulants and coagulant aids applied to OSPW feedwater can affect membrane physicochemical properties (surface hydrophilicity, zeta potential, and morphology), membrane performance, and the fouling indexes. Membrane rejection of ionic species increased significantly with the inclusion of an anionic coagulant aid and slightly with a cationic coagulant aid. Among three coagulant aids tested, anionic coagulant aids led to the most enhanced membrane performance through increasing membrane surface negativity and decreasing the formation of a fouling layer. Conversely, although cationic coagulant aids were the most effective in reducing OSPW turbidity, the application of cationic coagulant aids promoted the adsorption of foulants on membrane surfaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 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".