Corrosion Management Planning: Lessons Learned for Seawater Conveyance in Mining
Bibliographic record
Abstract
Abstract Mining is one of the few industries that can use low quality water for processing. Where mines are in arid climates, such as the Atacama Desert in Chile, there have also been increasing regulatory pressures on the use of fresh and brackish groundwater sources. In the last decade, this has resulted in mines moving towards the use of seawater to supply their operations, which often has to be transported 100s of km inland from the coast. The corrosiveness of seawater introduces an increased corrosion risk for these projects and is not always managed in a proactive way from the design stage. This can have severe implications for the ongoing operational performance of the mine. This paper will present a case study, which will outline the challenges and lessons learned with corrosion in a Mine project in Chile where the internal corrosion of the seawater conveyance line caused a number of technical, project schedule and budget impacts during the commissioning stage of the project. The lessons learned are used to highlight the opportunities for evolving engineering design of mines to incorporate corrosion management from the beginning of a project and allow this specific issue to be controlled in an optimal way.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".