Remediation of paper and pulp industry wastewater by peach stone‐derived activated carbon catalyzed ozonation
Bibliographic record
Abstract
Abstract The recalcitrant nature of paper and pulp wastewater makes its treatment crucial and challenging. This study explores the use of peach stone‐derived activated carbon in a heterogeneous catalytic ozonation process (HCOP) to treat paper and pulp wastewater. The impact of operational parameters, such as ozone dose, initial pH of wastewater, initial COD levels, and catalyst reuse performance, was evaluated on the elimination of colour and COD in wastewater. The research findings show that in HCOP, the maximum removal efficiencies for colour and COD under alkaline pH were 92% and 99%, respectively. Yet, the efficiencies declined to 71.25% and 77% under acidic conditions. However, under optimal conditions (pH 6.8, ozone dose 0.2 mg/mL, catalyst dose 10 g/L, and COD of 400 mg/L), the treatment achieved up to 98% colour and 92% COD reduction. Adding tert‐butyl alcohol (TBA) reduced efficiencies, confirming the role of hydroxyl radicals in the process. Our research underscores the potential of peach stone‐derived activated carbon in HCOP and provides a data‐driven method for process optimization in industrial effluent 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) | 0.000 | 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) | 0.000 | 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".