Fundamental study on extracting lead from cerussite concentrate in methane sulfonic acid based solution
Bibliographic record
Abstract
Cerussite (PbCO₃) concentrates may be recovered from oxidized lead ores. These concentrates suffer from the intrinsic fuel shortage via traditional smelting route and may lead to widespread contamination in shipment. Therefore, a hydro-electrometallurgical process to treat cerussite concentrate by methane sulfonic acid (MSA) is proposed to solve the above problems. The leaching of cerussite concentrate by MSA was first studied. The parameters of stirring speed, temperature, acid concentration, particle sizes and solid concentration were considered. It was found that stirring speed, temperature, particle size and proton concentration had a significant influence on the kinetics while solid concentration showed no effect on the final lead extraction under the experimental conditions. The leaching results indicated that MSA is favourable to treat cerussite concentrate and the lead extraction could achieve the theoretical maximum in just 10 min at ambient conditions. The lead content in solution obtained at high solid concentration was sufficiently high, and easily met the concentration requirements for the subsequent electrolysis process. After leaching, the residue was subjected to a desulfurization treatment to recover the remaining lead in residue. Using desulfurization agent Na₂CO₃, the remaining lead, mainly in anglesite, was transformed to PbCO₃ that was followed by a re-leaching treatment with MSA. An overall lead recovery of 98% was finally obtained. In the electrowinning process from MSA based electrolyte, the individual and synergistic effects of two ligninsulfonate salts and two glycol-type agents on lead deposit quality were first investigated. Compared to the other three additives, the individual use of calcium ligninsulfonate most benefited the morphology of lead deposit. The operating parameters in the lead electrowinning process (i.e. temperature, current density, concentrations of lead ion and protons) had a widely acceptable range. The cathodic current efficiency and specific energy consumption in most tests were around 99% and 0.53Wh/kg, respectively. The SEM micrographs showed that the lead deposits obtained under the optimal conditions were compact and even. These results for leaching and electrolysis made the MSA system competitive to the comparable fluoborate and fluosilicate systems. Finally a simplified flowsheet to extract lead from cerussite concentrate in MSA based solution was proposed.
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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".