Removal of metals in leachate from sewage sludge using electrochemical technology
Bibliographic record
Abstract
Heavy metals in acidic leachates from sewage sludge are usually removed by chemical precipitation, which often requires high concentration of chemicals and induces high metallic sludge production. Electrochemical technique has been explored as an alternative method in a laboratory pilot scale reactor for heavy metals (Cu and Zn) removal from sludge leachate. Three electrolytic cell arrangements using different electrodes materials were tested: mild steel or aluminium bipolar electrode (EC cell), Graphite/stainless steel monopolar electrodes (ER cell) and iron-monopolar electrodes (EC-ER cell). Results showed that the best performances of metal removal were obtained with EC and EC-ER cells using mild steel electrodes operated respectively at current intensities of 0.8 and 2.0 A through 30 and 60 min of treatment. The yields of Cu and Zn removal from leachate varied respectively from 92.4 to 98.9% and from 69.8 to 76.6%. The amounts of 55 and 44 kg tds(-1) of metallic sludge were respectively produced using EC and EC-ER cells. EC and EC-ER systems involved respectively a total cost of 21.2 and 13.1 CAN dollars per ton of dry sludge treated including only energy consumption and metallic sludge disposal. The treatment using EC-ER system was found to be effective and more economical than the traditional metal precipitation using either Ca(OH)2 and/or NaOH.
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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) | 0.000 | 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".