The enhancement of black liquor treatment by applying a natural flocculant and converting its sludge to a high‐benefit product
Bibliographic record
Abstract
Abstract Black liquor (BL) as lignocellulosic‐based industrial wastewater is largely attained from a chemical pretreatment process. When treated with the commonly used coagulant polyaluminum chloride (PACl), BL was decolourized but its pH decreased drastically from 13 to 4–5. Chitosan as a natural flocculant was added to the BL treatment process to support the PACl. The combination of coagulant‐flocculant (PACl‐Chitosan) effectively generated sludge, rejuvenated the treated BL pH level to neutral, and decolourized and reduced several parameters required for the treated BL disposal. To establish a sustainable recycling process and enhance the BL treatment, the generated sludge as a potential source was recovered and converted to a carbonaceous adsorbent (CA) by applying a two‐stage carbonization process, heat and steam carbonization, during which the temperature and time in the first stage of the heat‐carbonization process differ. The first BL sludge‐based CA (BLS‐CA 1) is produced by employing the first stage heating at 575 °C for 180 min while the second black liquor sludge‐based CA (BLS‐CA 2) is produced by employing the first stage heating at 450 °C for 60 min. Both CAs were able to adsorb about 99 % methylene blue (MB) at MB concentration 100 mg/L for 16 h though they have smaller surface areas than commercially activated carbon, which is only able to adsorb about 70 % MB at the same concentration and adsorption time. This study demonstrates a potential way to reduce the emerging problem from the generated sludge through a sustainable recycling process as well as BL treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".