Assessment of Catalytic Performance of Fe<sup>2+</sup>, Fe<sup>3+</sup> and Fe<sup>2+</sup>:Cu<sup>2+</sup> InFenton and Photo-Fenton Treatment of Pulp Bleaching Wastewaterr
Bibliographic record
Abstract
Fenton process has been widely studied for the abatement of recalcitrant pollutants, such as adsorbable organic halides (AOX), from industrial wastewaters.In this work, Fenton and photo-Fenton processes were applied to remove AOX from real pulp bleaching wastewater.The catalytic performance of different oxidation states of iron (Fe 2+ and Fe 3+ ) and a combination of Fe 2+ :Cu 2+ were studied.Advantages, limitations, and operating cost of the different solutions studied were discussed.At the optimum operating condition adopted for AOX removal, side effect on organic load (COD and BOD5) was also assessed.Iron catalysts Fe 2+ and Fe 3+ were firstly compared.For that purpose, response surface methodology (RSM) was applied to find the operating conditions yielding maximum AOX removal at minimum cost, for both iron species.After defining the most suitable iron catalyst load, a second round of experiments were conducted, to study Cu 2+ as Fenton (co)catalyst.In those experiments, the optimum load of catalyst previously found was maintained, and different Fe:Cu ratios were studied, namely: 0:100; 25:75, 50:50, 75:25 and 100:0 % (mol/mol).Fe 2+ proved to be a more costeffective iron catalyst than Fe 3+ , with photo-Fenton process allowing for better performance with lower chemical, and lower operating cost.Fe 2+ showed superior catalytic performance than Cu 2+ , yielding around twice the AOX removal.In Fenton process, up to 50 % Cu 2+ was successfully included in the catalyst mixture, with no significant loss of catalytic activity neither increase in operating cost.On the other hand, no synergetic effect between metals was registered.The photolytic regeneration of Cu 2+ was not effective, which may have hindered • OH production in photo-Fenton process, leading to a decrease efficacy with a decrease in the Fe:Cu molar ratio.
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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 | 0.001 | 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".