Recovery of softwood lignosulphonates and hemicellulose sugars from spent organosolv liquor
Bibliographic record
Abstract
In this thesis, the effect of pH and temperature into membrane filtration of spent organosolv liquor lignosulphonates (LS) and hemicellulose sugars was studied. The effect of filtration conditions was monitored in terms of membrane permeability, selectivity and fouling. Viability of a membrane process was considered on an industrial point of view according to results of laboratory experiments. \n \nThe filtration experiments were carried out by using DSS LabStak® M20-0.72 cross-flow unit with a stack of either three, four or seven membranes. The membranes used in experiments were Alfa Laval’s GR95PP (2 kDa, PES), UFX5 pHt (4 kDa, PSU), and RC70PP (10 kDa, RCA), Nadir’s NP010 (1.0–1.2 kDa, PES), and UH004P (4 kDa, PES), GE membranes/Suez’s GE (1 kDa, CPA) and GK (3.5 kDa, TFC PA), and Synder Filtration’s NFG (0.6–0.8 kDa, TFC PA). The filtration temperatures were 32, 45 and 60 °C, pH-values 0.88, 4.59, 4.70 and 6.33, filtration pressures 1–4 and 4–16 bar, and cross-flow velocity was 0.8 m s-1. \n \nMembrane permeability seemed to increase with pH and temperature – with PES membranes being the most promising in terms of pH-response. The permeability of NP010 increased almost tenfold at pH 6.33 compared to pH 0.88. In terms of membrane selectivity, the membrane MWCO seemed to affect the results more than pH and temperature. Overall, the best selectivity was achieved with the tightest membranes. \n \nOverall, membrane filtration of spent organosolv liquor’s LS and hemicellulose sugars seemed viable. However, due to the trade-off between membrane permeability and selectivity more experimenting is needed so that the process can be optimized.
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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".