Impact of acid washing and chelation on Mg(OH)2-based hydrogen peroxide bleaching of mixed hardwoods CMP at high consistency
Bibliographic record
Abstract
The removal of transition metal ions is crucial for improving the efficiency of subsequent peroxide bleaching. Acid-washing and chelation have been proposed for such a purpose. However, their influences on the Mg(OH)2-based peroxide bleaching of hardwood pulps at a high consistency have not been well documented in the literature. In this work, we studied the influence of acid-washing using sulfuric acid or chelation using diethylenetriaminepentaacetic (DTPA) on the Mg(OH)2- or NaOH-based hydrogen peroxide bleaching efficiency, effluent properties of bleaching filtrates, and paper properties. The results showed that for Mg(OH)2-based peroxide bleaching, the pulp yield and water retention value of acid-washed pulp were higher than those of the chelated pulp; the chemical oxygen demand (COD) and turbidity of the bleaching filtrates for the acid-washed pulp were lower than those of the chelated pulp. The bleached acid-washed pulp had lower strength properties than bleached chelated pulp did. Additionally, at a high pulp consistency (25%), the Mg(OH)2-based process had a higher bleaching efficiency and superior bleaching effluent properties, but a lower strength properties, in comparison with the NaOH-based process.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".