COMBINING ULTRAFILTRATION PROCESS WITH COAGULATION PRETREATMENT FOR PULP MILL WASTEWATER TREATMENT
Bibliographic record
Abstract
Ultrafiltration combined with coagulation pretreatment was used to treat two kraft pulp mill wastewaters from first-stage caustic extraction and alkaline bleaching operations, respectively. Both alum and ferric chloride were tested using standard jar apparatus at different dose, pH and ionic strength conditions. Ultrafiltration tests were conducted using a crossflow flat-sheet membrane apparatus operated in the constant transmembrane pressure mode to examine the effects of membrane material, crossflow velocity and transmembrane pressure in terms of permeate flux and treated effluent quality. The results showed that coagulation with both alum and ferric chloride greatly reduced the permeate flux decline rates. In comparison with alum, greater permeate fluxes were obtained with the use of ferric chloride. Among the process parameters examined, coagulant dose was identified as the most important factor affecting the permeate flux. In addition, colour and COD removals were achieved largely by coagulation for alkaline bleaching wastewater while by membrane filtration for caustic extraction wastewater, highlighting the different mechanisms underlying contaminant removal.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".