COD reduction of petrochemical industry wastewater using Fenton's oxidation
Bibliographic record
Abstract
Abstract Reduction of chemical oxygen demand (COD) value of petrochemical industry wastewater (COD ∼11 500 mg/L) by Fenton's oxidation has been investigated. Batch tests were conducted on the effluent samples to determine the optimum process conditions. Fenton's oxidation process was found to effectively reduce the COD by 97.5% in 100 min. Effects of different process parameters: pH, H 2 O 2 dosage, Fe 2+ dosage, H 2 O 2 /Fe 2+ ratio, temperature were investigated. The optimum conditions were at pH 3, H 2 O 2 concentration 3 M, Fe 2+ concentration 0.06 M and temperature 30°C. Optimum molar ratio [H 2 O 2 /Fe 2+ ] was 50:1. At optimum conditions, 97.5% COD reduction was achieved for the typical effluent sample from nearby industry manufacturing mainly PET resins. On a analysé la réduction de la valeur demande chimique en oxygène (DCO) des eaux usées de l'industrie pétrochimique (DCO ∼11 500 mg/L) par oxydation de Fenton. Des essais par lots ont été réalisés sur les échantillons d'effluent dans le but de déterminer les conditions de processus optimales. On a découvert que le processus d'oxydation de Fenton a réduit efficacement la DCO de 97,5% en 100 min. Les effets de différents paramètres de processus, soit pH, dose de H 2 O 2 , dose de Fe 2+ , rapport H 2 O 2 /Fe 2+ et température ont été analysés. Les conditions optimales étaient pH 3, concentration de H 2 O 2 3 M, concentration de Fe 2+ 0,06 M et température 30°C. Le rapport molaire optimal [H 2 O 2 /Fe 2+ ] était de 50:1. À des conditions optimales, on est parvenu à une réduction de DCO de 97,5% pour l'échantillon d'effluent typique de l'industrie à proximité qui fabrique principalement des résines de PET. Can. J. Chem. Eng. © 2010 Canadian Society for Chemical Engineering
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".