Regeneration of dye-saturated activated carbon through advanced oxidative processes: A review
Bibliographic record
Abstract
Activated carbon (AC) is a porous carbon-rich material that is widely used to remove pollutants, such as synthetic dyes, from contaminated water. Although quite efficient, the use of this technology is limited to the ability of the AC to be regenerated and/or reused. Conventional regeneration procedures are inefficient, requiring the development and/or implementation of new approaches. Advanced Oxidative Processes (AOP) have unique properties that result in high efficiency in wastewater treatment. The use of these technologies in the regeneration of AC has gained considerable prominence due to the ability to remove organic pollutants concentrated in the AC. During this process, the oxidizing species produced interact with the substrates adsorbed on the AC, in a non-selective way, mineralizing them and/or reducing their recalcitrance. Although widely used in wastewater treatment, few reviews focus on the use of AOP as AC regeneration technology, causing an insufficient exchange of information and ideas for strategic development in this area. Therefore, in this review, the authors present an overview of the use of some AOP (Photolysis, Peroxidation, Fenton reaction and Advanced electrochemical oxidative processes) when applied in regeneration of dye-saturated AC, including the mechanisms involved in the different processes, the general aspects that affect individual processes and the different methods established to quantify the effectiveness of regeneration.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".