102 Application of Quantitative Mineralogy to Determine Sources of Airborne Particles at a European Copper Smelter
Bibliographic record
Abstract
Abstract To ensure that regulatory compliance is maintained at mineral processing operations, worker exposure is regularly monitored. Determining the quantity and type of airborne particles permits the operation to identify the sources of dust, implement proper dust suppressant strategies, and ultimately, limit worker exposure. Conventional methods of analysis, such as chemical assay, are unable to rigorously differentiate between phases containing the same elements and may result in ambiguity related to identifying the source of airborne dust. A combination of Quantitative Evaluation of Materials by Scanning Electron Microscope (QEMSCAN) and chemical characterization has been used to evaluate personally taken size selective aerosol samples at key locations throughout a Cu smelter in Europe. Surface samples were also examined using higher resolution measurements. The Cu phases present in the workplace air samples are reflective of the activities performed at specific locations. Near the anode and electric furnace, the majority of Cu in the airborne dust is carried in metals and oxide phases (60-70%), and in the batch preparation area where Cu concentrate is received, significant amounts of Cu are carried in sulfide minerals (chalcocite, chalcopyrite/bornite, >40%). The results of the analysis will aid in the identification of the origin of emissions specific to location and activity, assist personnel in developing the appropriate mitigation strategies to limit workers exposure, and further understanding of the health risks associated with exposure. Additional metals of concern (e.g, Pb, As, and Cd) are also considered throughout this program. Speaker Biography Michelle Kelvin is a geoscientist and environmental mineralogist. She specializes in characterizing dust emissions that originate from industrial operations using modern geochemical methods and X-ray beam technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".