Efficiency of Removing Chromium from Plating Industry Wastewater using the Electrocoagulation Method
Bibliographic record
Abstract
Aims Chromium is one of the most important metallic pollutants in plating industry wastewater. This toxic metal is a serious threat to human health and to the environment due to its cumulative effects and non-degradability. This research intended to study the effects of pH, contact time, and voltage on the degree of chromium removal from wastewater of plating industry by using the electrocoagulation method. Materials & Methods This laboratory research conducted from late May to late November 2012. A 1000cc reactor at laboratory scale was used that included 4 aluminum electrodes of 90% purity, dimensions of 5 by 10cm, and thickness of 1mm, with parallel arrangement. Synthetic chromium-bearing wastewater was prepared at the initial concentration of 50mg/l. The process is done at pH values of 3, 7, and 9, electric potentials of 20, 30, and 40 volts contact durations of 20, 40, 60, and 80 minutes. Findings The degree of chromium reduction did not change linearly with time in the solution and strongly depended on the pH. The efficiency of chromium removal in the samples had an ascending trend with increases in voltage from 20 to 30 and 40 volts. The degree of chromium removal increased at longer contact times. Conclusion Lower pH, more contact time and higher voltages are effective factors in the chromium removal from wastewater by coagulation method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".