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, H2O2 dosage, Fe2+ dosage, H2O2/Fe2+ ratio, temperature were investigated. The optimum conditions were at pH 3, H2O2 concentration 3 M, Fe2+ concentration 0.06 M and temperature 30°C. Optimum molar ratio [H2O2/Fe2+] 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 H2O2, dose de Fe2+, rapport H2O2/Fe2+ et température ont été analysés. Les conditions optimales étaient pH 3, concentration de H2O2 3 M, concentration de Fe2+ 0,06 M et température 30°C. Le rapport molaire optimal [H2O2/Fe2+] 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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".