Improving Process Hazard Analysis (PHA) outcomes to better manage critical controls in mining industry: From <scp>PHA</scp> to verification in the field
Bibliographic record
Abstract
Abstract Process safety management in the mining and metals industry is relatively new compared to other high‐hazard process industries such as oil and gas or chemicals. Practices are less mature and are developing, transferring from other industries, and using the International Council on Mining and Metals (ICMM) guidance to manage critical controls. This paper shares how Rio Tinto, a leading mining and metals company, has improved its critical controls management practices. Focus is put on how the company has improved the quality of its process hazard analyses (PHAs) and critical controls management activities through a series of actions. This covers clarification of the methodology used, development of training packages, revision of the methodology used to identify critical controls, development of a PHA facilitation approval process and a PHA facilitation approval committee, implementation of a training and coaching program for internal candidates, and development of assurance activities to monitor effectiveness of the process. These actions have resulted in very encouraging results in terms of overall quality of the PHAs, effectiveness of the PHA approval process to support the new methodologies introduced, and the development of internal facilitation capabilities.
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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 | 0.034 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".