Synergism of enzymes in chemical pulp bleaching from an industrial point of view: A critical review
Bibliographic record
Abstract
Abstract Enzymes are biological catalysts and are very specific, catalyzing either a single chemical reaction or a limited number of closely related reactions. For example, xylanases are enzymes that catalyze the cleavage of polymeric xylan and thereby break down this hemicellulose. The first xylanase enzyme preparations used in the bleaching process of chemical pulp also contained cellulase, which catalyzes the hydrolysis of cellulose. This obviously had an adverse effect on pulp yield and quality. Unfortunately, this setback gave enzyme‐assisted pulp bleaching a negative reputation. At a later stage, enzyme producers managed to engineer enzyme production strains that generated cellulase‐free xylanase preparations. However, due to the initial negative experiences with the earlier enzyme mixtures, only a limited number of companies in the pulp industry have seriously considered using these so‐called second‐generation enzymes in their bleach plants. It is apparent that these improved enzyme preparations would bring about significant benefits in terms of chemical cost savings and effluent quality. In addition to xylanase‐aided bleaching, it is possible to improve the effectivity further by adding other enzymes, such as lipase and esterase, to create an enzyme cocktail. This may be particularly beneficial in the bleaching of hardwood pulp, such as white birch, which often encounters complex and troublesome problems with wood extractives. By adding different types of enzymes at more than one position in the fiberline, even further improvements are possible. The main objective of this review is to discuss the advantages of incorporating modern enzyme preparations in the bleaching of chemical pulp.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".