Removal of Lead from Battery Effluent by Electrocoagulation
Bibliographic record
Abstract
The efficiency of iron electrode based electrocoagulation (EC) technique at laboratory scale to remove lead (Pb) from battery industrial effluent in Bangladesh is investigated. Different combinations of voltage (15, 30 and 45), effluent pH (1, 3, 5, 7 and 9) and electrolysis time (15, 30, 45 and 60 minutes) at the EC reactor was examined for searching the ideal operating conditions of maximum lead removal. Initial battery effluent pH of 3, electro coagulating at 30 V for 15 minutes would be the optimum conditions for treatment where 99.9% Pb removal was achieved. Treated effluent quality was compared with national environmental standard to discharge into surface water bodies and found physico-chemical parameters (TDS, TSS, DO and pH) were within prescribed limit except electrical conductivity.Journal of Bangladesh Academy of Sciences, Vol. 39, No. 2, 125-134, 2015
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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 | 0.000 |
| 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".