The Treatment of High Concentration Dyeing Wastewater with Pulsed Current Electrocoagulation
Bibliographic record
Abstract
In this study, a small pulsed current electrocoagulation device was used to treat high concentration dyeing wastewater from a specific dyeing mill, and the effects of the electrode materials, reaction time, voltage, pH value, and aeration on the results of the treatment were examined. The results showed that under the following operational conditions: electrode materials were iron electrodes, time period was 15 min, voltage was 120 V, and initial pH was approximately 6, the removal rates of the COD, ammonia nitrogen, and color were 79.45%, 23.89%, and 87.50%, respectively. On this basis, a pulsed current electrocoagulation device, with a handling capacity of 0.5 m3/h, was used to conduct a pilot plant test for a period of one month. The results showed that the effluent quality (COD 1217.4 mg/L and NH4+-N 358.2 mg/L on average) of the high-concentration dyeing wastewater, whose COD and NH4+-N concentrations were 5328 mg/L, 595 mg/L, respectively after the treatment of a pulsed current electrocoagulation reactor, was superior to the effluent quality (COD 1400 mg/L and NH4+-N 450 mg/L) of the mill’s actual pre-treatment system (flocculation-anaerobic treatment-acidification), and fully reached the influent requirements of the subsequent aerobic treatment. The results of this study showed that pulsed current electrocoagulation reactors may be effectively used for the pre-treatment of high concentration dyeing wastewater due to the observed advantages, such as good treatment effects, small investment, and economical space occupation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".