Removal of Chromium, Nickel, Zinc and Turbidity from Industrial Wastewater by Electrocoagulation Technology (Case Study: Electroplating and Galvanized Wastewater of Industrial Zone in Boomhen)
Bibliographic record
Abstract
Background and purpose: Heavy metals such as chromium, nickel, and zinc are the most common contaminants found in the plating wastewater that cause environmental pollution due to nonbiodegradability. This study investigated the removal of chromium, nickel, zinc and turbidity from wastewater of plating and galvanized industries by electrocoagulation technology. Materials and methods: This study was conducted at laboratory scale using four parallel aluminum electrodes (5cm × 10cm dimensions, 1 mm thickness). In each electrode, 6 holes (0.7 cm) were considered. Wastewater samples were first collected as grab sampling and finally changed to combined samples. This study was performed in pH= 3 and pH= 7.2 for plating wastewater and pH= 9 for galvanized wastewater and reaction times of 20, 40, 60 and 80 minutes. Results: Nickel and chromium contents in plating wastewater were 45 and 50 mg/l, respectively. Zinc concentration in galvanized wastewater was determined 210 mg/l. Turbidity in plating and galvanized wastewaters were 250 and 200 NTU, respectively. The maximum removal of nickel and chromium was observed in contact time= 80 min and pH=4, while it was seen in pH=9 for zinc (98 %, 95.6%, and 94%, respectively). The maximum removal of turbidity in Ni – Cr wastewater and galvanized wastewater was 97% and 95.3%, respectively. Conclusion: Electrocoagulation technology was found as an effective, economical and rapid method for infiltration of toxic wastewater in plating and galvanized industries.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".