Closing the loops in the pulp and paper industry with membrance technology
Bibliographic record
Abstract
Membrane technology is a relative new technology in the pulp and paper industry with interesting applications in the water loop, the by-product loop and the chemical loop. The technology was pioneered in the pulp and paper industry by DDS Filtration, now Alfa Laval Nakskov in the 1970'ies and this article reviews the opportunities to use membrane technology in the different loops investigated over the years. The first part of the article focuses on the water loop and demonstrates the use of membrane technology to (1) prepare and purify in-take water, (2) recycle water in processes, and (3) to treat water before discharge. For the water loop, the use of membrane technology to polish evaporator condensate will be given as an application study. In the second part of the article membrane opportunities in the by-products loop of the sulphite and Kraft pulping process will be highlighted. The use of membrane technology for the concentration and purification of spent sulphite liquor in a sulphite pulp mill will be described in application study. The final part of the article will discuss membrane applications in the utilities and chemicals loop. The use of membranes for the Kraft bleach purification and for the paper coating recovery will be presented. Further, an application study will detail the use of membranes for purification of Kraft bleach effluent. Overall, the paper demonstrates potential of membrane technology for the pulp and paper industry ranging from water purification and preparation to the recovery of valuable products.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".