Process Safety Management Enforcement Trends and Best Practices
Bibliographic record
Abstract
Chemical safety and process safety continue to garner significant media attention around the United States as chemical and process safety events continue to occur. OSHA’s Process Safety Management (PSM) standard and EPA’s Risk Management Program (RMP) standard provide a framework for preventing unwanted release of highly hazardous chemicals. It is also important to note that these are performance standards, meaning that regulated processes are told what the goal (unwanted release) is, not how to meet it. OSHA began a PSM National Emphasis Program (NEP) for Petroleum Refineries in June of 2007 and later implemented an additional PSM National Emphasis Program for Chemical Facilities in November of 2011. Enforcement data shows key trends and areas where both EPA and OSHA focus their efforts. This paper will review the key enforcement trends for PSM and RMP regulated processes, provide some best practices for ensuring process safety is a way of life, and review actual and potential regulatory activity resulting from Executive Order 13650 Improving Chemical Facility Safety and Security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.066 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| 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".