Production of high-quality bleached chemi-mechanical pulp from moso bamboo via an improved pre-conditioning refiner chemical alkaline peroxide mechanical pulping process
Bibliographic record
Abstract
Moso bamboo is a fast-growing, abundant resource with significant potential as a sustainable alternative to wood for pulp production. However, traditional kraft pulping of moso bamboo faces challenges such as low yield, high pollutant loads, and inferior pulp quality. This study developed an improved pre-conditioning refiner chemical alkaline peroxide mechanical pulp (P-RC APMP) process integrated with multi-stage hydrogen peroxide (H 2 O 2 ) bleaching to efficiently produce high-quality bleached chemi-mechanical pulp (CMP) from moso bamboo. Key parameters, including pulping energy consumption, bleaching efficiency, fiber morphology, and key strength indices (tensile, tear, and burst), were systematically investigated. Results indicated that moso bamboo exhibited significant potential as a non-wood fiber source for pulp production, with holocellulose (76.4 %) and cellulose (52.6 %) contents comparable to those of masson pine and poplar. However, the comparatively higher density, elevated extractives content, and increased ash levels of moso bamboo relative to conventional wood species presented processing challenges that negatively impacted the pulping efficiency, chemical consumption, and final product quality. The improved P-RC APMP process integrated with 2–3 stages of H 2 O 2 bleaching successfully produced high-quality bleached CMP from moso bamboo, achieving a pulp yield exceeding 75 %, an ISO brightness range of 68.5–73.5 %, and desirable tensile, tear, and burst indices. Meanwhile, it was found that the pulping process exhibited higher energy consumption and bleaching complexity compared to wood-based counterparts, highlighting areas for further investigation. This work demonstrated a viable method to produce high-value bamboo CMP suitable for various fiber-based products, such as cultural paper, packaging materials, tissue grades, and molded products. By diversifying fiber resources, this approach supports sustainable forest conservation and aligns with circular bioeconomy principles.
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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".