Ultrafiltration and nanofiltration of E-stage bleaching plant effluents of a sulphite pulp mill
Bibliographic record
Abstract
This work addresses the assessment of ultrafiltration (UF) and nanofiltration (NF) for the treatment of a bleaching plant effluent (BPE) from aPortuguese sulphite pulp mill (Caima-Indústria de Celulose,S.A.) with the two-fold objective of water consumption minimization and by-products valorization. This is to be achieved through the recycling of UF/NF permeates as process water and the concentration of lignin and hemicelluloses for biorefinery purposes.Besides physical-chemical analysis, the effluent was characterized by size exclusion chromatography (SEC) to yield the molecular mass distribution of the hemicelluloses and lignin and accordingly to select six flat sheet alkali resistant membranes: two tight UF membranes from ALFA LAVAL with molecular weight cut-off of 1kDa (ETNA01PP) and 2kDa (GR95PP) and four NF membranes: ALFA LAVAL NF99HF, KOCH MPF36, NADIR NP010, NADIR NP030.Parametric studies were carried out in permeation cells with a membrane surface area of 0.00196 m2 to investigate the influence of cross flow velocity (CFV) and transmembrane pressure (TMP) on permeate fluxes and retention coefficients. Concentration experiments were carried out in a LabStak® M20 with a total membrane surface area of 0.072 m2.Nanofiltration membrane ALFA LAVAL NF99HF was the most suitable one when operating at 70°C, with a CFV of 0.8 m/s and a TMP of 13 bar. An average permeate flux of 24.2 Lh-1m-2, lignin and hemicelluloses rejection coefficients between 94 and 97%, and an increase in total solids content from 3% to 7%(w/w) for a volumetric concentration factor of 3.1 were obtained.The SEC analysis yielded an average molecular mass of 1.32 kDa for lignin and 0.88 kDa for hemicelluloses. These close values made it difficult to envisage their fractionation in a single NF stage. Further studies should be carried out for the processing of the present concentrates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".