Treatment of commercial industrial wastewater using electrocoagulation: Understanding effect of operating conditions
Bibliographic record
Abstract
Abstract The wastewater generated from various stages in pharmaceutical industries such as production, washing processes, and formulation contains a wide range of hazardous chemicals. The objective of this study is to investigate the application of electrocoagulation (EC) for the treatment of real industrial pharmaceutical wastewater obtained from various processing stages. Aluminium and stainless steel (SS) electrodes were used to conduct the treatment. The effects of important operating conditions for EC such as current density (1.08–3.33A/cm 2 ), electrode spacing (varied from 4 to 7 cm), pH (from 4.0 to 10.0), and poly aluminium chloride (PAC) dose (chemical oxygen demand [COD]: PAC ratio as 1:0.025–1:0.3) on the COD removal efficiency have been studied. TDS and electrical conductivity were also monitored during the treatment. The optimum COD reduction was observed at a current density of 1.33 A/cm 2 , an electrode spacing of 5 cm, a pH of 7, and a COD: PAC dosage ratio of 1:0.2. The maximum COD reduction recorded for samples A, B, C, and D corresponding to various stages were 62%, 71%, 51%, and 86%, respectively. It was also concluded that the COD reduction was improved after the addition of external coagulant as PAC. The research work has clearly confirmed that the EC process can be considered as an effective method of treatment for COD reduction.
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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) | 0.001 | 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".