Application of heterogeneous catalytic ozonation process for treatment of high toxic effluent from a pesticide manufacturing plant
Bibliographic record
Abstract
Background: The discharge of untreated wastewater containing toxic and resistant compounds into the environment is a serious threat for ecosystems. Therefore, this study was conducted to evaluate the treatment of poison production factory wastewater using heterogeneous catalytic ozonation process (COP). Methods: Magnetic carbon nanocomposite was used as a catalyst at concentrations of 1, 2, and 4 g/L. Its effect on improving the treatment process was evaluated at reaction time of 30, 60, 90, and 120 minutes. At the end of each experiment, parameters including total organic carbon (TOC), chemical oxygen demand (COD), biological oxygen demand (BOD 5 ), pH, electrical conductivity (EC), and turbidity were measured. Results: It was revealed that in single ozonation process (SOP), the maximum removal efficiencies of TOC, COD, and BOD 5 were achieved at reaction time of 120 minutes as 56%, 40%, and 11.7%, respectively. By adding the catalyst to the wastewater, the treatment process was improved, so that the maximum removal efficiencies of COD (91%), TOC (73%), and BOD 5 (74%) were obtained at catalyst concentration of 4 g/L. Under this condition, BOD 5 /COD ratio increased from 0.22 to 0.64. Also, the results of analysis of ozone consumption per each mg of reduced COD showed that its amount sharply decreased from 2.1 mgO 3 / mg COD removal in the SOP, to 0.34 mgO 3 /mg COD removal in the COP. The results of kinetic reaction analysis also revealed that the rate constant increased from 0.007 to 0.02 min-1. Conclusion: According to the results, it can be concluded that the COP at a catalyst concentration of 4 g/L, by decomposing resistant compounds and increasing the biodegradability, can be used as a suitable pretreatment method for biological processes.
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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".