Life-Cycle Cost Evaluation of Corrosion Mitigation Strategies in the Mining Industry
Bibliographic record
Abstract
Abstract Corrosion-related challenges are usually addressed during the detailed engineering phase to meet the specified service life of the asset, but a comprehensive strategy to lower corrosion costs is rarely implemented. A life-cycle cost (LCC) analysis is often used to optimize the design and consider direct and indirect costs. The approach allows to quantify the capital and operating costs and costs associated with the failure of assets and potential implications associated with safety and environmental damage. The present paper explores the cost of corrosion in the mining industry and attempts to identify pathways for design optimization. The current work examines the corrosion costs associated with the lithium processing industry using an LCC analysis. The direct cost of corrosion was determined by quantifying the cost of all corrosion-related activities and design, and the indirect cost of corrosion was estimated through industry-accepted models. The effectiveness of corrosion mitigation strategies was evaluated by examining the sum of present value of money. Net present value of money over the design life of the investment and/or the shortest payback period, and/or the highest return on investment (ROI) were briefly discussed as the alternative approaches for identifying the most economic corrosion control system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".