An overview of metals extraction and recovery from industrial wastewater sludge
Bibliographic record
Abstract
Abstract Industrial wastewater sludge is one of the vital sources of metals, including heavy metals, valuable metals, and precise metals. Apart from metals' necessity and economic value, some are toxic and harmful to the environment. This review explores the technologies currently applied for extracting and recovering heavy metals from industrial wastewater sludge. The technologies have been explained, and the merits and demerits of methods, as reported in past investigations, have been highlighted. The salient findings of this review are that the hydrometallurgical processes using acid leaching (H 2 SO 4 , HNO 3 , HCl, etc.) have been considered for the metal extraction process. Metal dissolution, concentration/purification, and recovery are the main stages of hydrometallurgical processes. The selection of successive metal recovery methods depends on the concentration of metals and chemical characteristics of industrial wastewater sludge. Different metal purification and concentrations were reported, including adsorption, ion exchange solvent extraction, and so forth, while precipitation and electrodeposition were mainly applied for metal recovery from industrial wastewater sludge. In this review, the cost and economic viability of the metal recovery process are also evaluated by previous reported studies. This review may be considered a valuable source of information for environmentally friendly and cost‐effective methods for metal recovery from industrial wastewater sludge.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".