COD Removal of Edible Oil Content in Wastewater by Advanced Oxidation Process
Bibliographic record
Abstract
Different Advanced Oxidation Processes (Photo Fenton process, Fenton process and H<sub>2</sub>O<sub>2</sub>/UV) were studied in order to reduce COD from oily compounds aqueous solution using batch system. To get the optimum condition, different variables were studied for each of these processes; such as pH, time, concentration of H<sub>2</sub>O<sub>2</sub>, concentration of oil, concentration of FeSO<sub>4</sub>·7H<sub>2</sub>O and temperature as parameters. It was found that the optimal pH value for the three processes was 3 and the optimal temperature was 30<sup>o</sup>C for Photo-Fenton and UV/H<sub>2</sub>O<sub>2</sub> system and 20<sup>o</sup>C for Fenton process. Photo-Fenton process gave a maximum COD reduction of 80.59 % (COD from 2684 to 521 mg/l), Fenton gave 53.22 % (COD from 2587-1130) and the combination of UV/H<sub>2</sub>O<sub>2</sub> gave a COD reduction of 22.69 % (COD from 2450 to 1894). The percentage of removal found was after the total reaction time (180 min.). The optimum chemical reagents for Photo-Fenton, Fenton and UV/H<sub>2</sub>O<sub>2</sub> were as the following H<sub>2</sub>O<sub>2</sub> = 800 mg/l, 1500 mg/l and 2000 mg/l, Fe<sub>2</sub>SO<sub>4</sub>·7H<sub>2</sub>O = 60 mg/l, 100 mg/l.
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 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 | 0.001 | 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".