Spectrochemical analysis of molten copper-nickel-iron matte at 1100 °C using laser-induced breakdown spectroscopy
Bibliographic record
Abstract
The first step in the pyrometallurgical process for extracting copper and nickel from ores consists in melting the ore and skimming the silicates and other oxides that float on the surface. The denser mixture of molten metals and sulfur found at the bottom is called matte. Our industrial partners want to know the concentration of copper, nickel and cobalt in the matte, in real time and in situ in the furnace or immediately at the exit of the furnace when it is tapped. Knowing these concentrations would allow increasing production efficiency in the following processing step, whereby iron and sulfur are oxidized inside a second furnace called the converter. We are developing a laser-induced breakdown spectroscopy (LIBS) sensor to measure online the concentrations of copper, nickel, cobalt, iron and sulfur in the molten matte. The measurement is made through a tube in which an inert gas flows. This allows measuring below the surface slag and impurities. Alternately, we are also investigating direct measurements onto the surface which could be advantageous when the molten matte is flowing in a channel. The advantages of LIBS technology are speed, lower operation costs than the current sampling technique, and improved safety because LIBS does not require manual sampling of molten metals. Preparatory measurements taken in the laboratory at temperatures of approximately 1100 °C will be presented. From an analytical point of view, one difficulty is that there is no major element that can be used to normalize line intensities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".