Membrane treatment of alkaline bleaching effluents from elementary chlorine free kraft softwood cellulose production
Bibliographic record
Abstract
This paper reports experimental results on the sequential use of ultrafiltration (UF), nanofiltration (NF) and reverse osmosis (RO) to fractionate alkaline extraction bleaching effluents from kraft cellulose production. The aim was to unveil the way key pollutants are distributed when subjected to sequential UF/NF/RO membrane separation processes. Alkaline bleaching effluents were obtained from a local pinewood-based mill, featuring elementary chlorine free bleaching to produce high-brightness cellulose. The experimental system was based on a laboratory-scale membrane system, DSS LabStak® M20 Alfa Laval, using Alfa Laval UF and NF/RO membranes, operated at a constant transmembrane pressure (6 bar for UF membranes and 32 bar for NF/RO membranes), at 25°C. Results show that 78% chemical oxygen demand (COD) and total phenols, 82% adsorbable organic halogens (AOX) and 98% colour were retained by UF membranes which have molecular weight cut-off (MWCO) above 10 kDa. In all, 16% of original COD, total phenols and AOX, and the remaining 2% colour were retained by UF membranes within the 1 to 10 kDa MWCO range. Chloride ions were significantly present in all UF permeates, and RO was required to obtain a high-quality permeate with a view to water reuse. It is concluded that UF/NF/RO membranes offer a feasible option for water and chemicals recovery from alkaline bleaching effluents in kraft pulp production.
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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) | 0.000 | 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) | 0.001 | 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".