Screening of chemical pulp, revisiting technology options, and the state‐of‐the‐art equipment—A critical review
Bibliographic record
Abstract
Abstract Screening of pulp can be expressed as a process where solid contaminants are removed from a pulp slurry without an excessive loss of fibres of acceptable quality. The impurities originate from the feedstock, or they are generated during the transport, handling, storage, or pulping process. The most typical impurities include bark, fibre bundles or shives, knots, plastic, rubber, and sand. Moreover, there are also other objectives for screening, such as the improvement of the pulp quality, savings in bleaching chemical consumption, and protection of process equipment. In industrial applications, pulp screening is typically carried out with more than one screen for a more beneficial screening result. The pulp screening equipment can be divided into different types by the separation technique employed, such as atmospheric or pressurized screens, centrifugal screens, vibrating screens, etc. In addition, there are other types of related equipment that are common in the pulp screening process, such as knotters, refiners, and hydrocyclones. The main objective of this review is to discuss the advantages and disadvantages of modern screening equipment as well as introduce contemporary screening strategies for chemical pulp mills.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".