Challenges and solutions in PGM furnace operation: high matte temperature and copper cooler corrosion
Bibliographic record
Abstract
Synopsis The key aspects of process metallurgy that distinguish platinum group metal (PGM) concentrate smelting from that of other base metal sulphide concentrates are presented. These differences include considerably higher input chrome and magnesia contents that directly raise the slag liquidus temperature and have the potential to increase accumulations of refractory spinels. Most importantly, the higher processing temperature required for PGM smelting and the resulting very high matte superheat lead to considerably more onerous smelting conditions than those typical of other smelting operations. This has presented challenges to furnace design and integrity, especially when coupled with the progressive intensification of smelting, involving doubling, and then redoubling, of furnace power inputs over the past 20 years. These power increases have been enabled by increasingly more advanced furnace cooling and structural technologies. Key technologies include strong constant-force spring-loaded bindings acting in three dimensions to minimize infiltration of superheated matte into brick joints, and robust well-cooled tapholes for reliably tapping the superheated matte. The result has been substantially improved productivity, and reduced smelting capital cost outlay per unit of production. A significant challenge, which was not anticipated, presented itself in the form of insidious corrosion of the furnace lining, and especially high-intensity copper cooling elements. Investigation of corrosion in related industries eventually identified ‘chlorideaccelerated sulphidation’, and this term has been retained as it generically describes the most pertinent aspects of the accelerated low-temperature wear of copper coolers observed in PGM smelting. In addition to discussing the corrosion mechanism, this paper describes a number of solutions that were developed jointly by Anglo American Platinum and Hatch to address the copper corrosion problem. First, new monitoring technologies allowed furnaces to be operated more safely for a longer period of time. Second, a system for replacing corroded coolers from outside the furnace during a fast ‘hot’ shutdown minimized the impact on furnace operating factor and hearth life. Finally, a corrosionresistant graphite-protected cooler design significantly improved furnace campaign life, and heralds a more lasting solution to cooler corrosion in PGM furnaces.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".