A Comprehensive Review on Pulp and Paper Industries Wastewater Treatment Advances
Bibliographic record
Abstract
The pulp and paper industry generates vast amounts of wastewater, and its character heavily depends on various factors (raw material, the undertaken process, the final product, etc.). The wastewater from this sector, which originates from several sources in each mill and are mostly combined, is polluting and hazardous. This paper presents a state-of-the-art review of the physical, chemical, biological, and advanced hybrid treatment techniques, concerning their effectiveness in removing specific pollutants, namely, chemical oxygen demand, lignin, color, and adsorbable organo-halogens. Throughout the manuscript, at the end of each section, a conclusive comparison has been presented and the proper method is introduced. Furthermore, numeric data regarding the effectiveness of each technique toward each pollutant are gathered from the literature and are available in the Supporting Information of the paper. Biological treatment processes using anaerobic–aerobic treatment mostly cure organic biodegradable contaminants (75–90% COD removal). Moreover, biological treatment using a consortium of microorganisms can potentially increase color removal efficiency (from 65 to 97%). Hybrid treatment is also among the candidates for color removal. To treat complex matters (lignin and AOX), physical and chemical treatments have shown promising performance, but they are generally expensive and impractical to treat huge amounts of wastewater. For the treatment of high molecular weight contaminants (lignin) advanced oxidation processes (AOPs), including ozonation and Fenton-based treatment, have shown great performance (90–99%); however, they are limited due to their maintenance and operation costs. To overcome these challenges, source separation of the wastewater streams in the pulp and paper industry is recommended. AOPs or membrane technologies or hybrid processes are suggested for the bleaching effluent (80% AOX removal), which is relatively low in amount, and a combination of conventional treatment processes would be preferred to treat wastewater streams that are more biodegradable. The biological performance can also be enhanced using granular activated carbon on the sequence. Finally, for treating black liquor, adsorption processes have proven to be the prime candidate.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".