Beyond HAZOP and LOPA: Four different company approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For operations where application of standards, regulations, and/or Recognized and Generally Accepted Good Engineering Practices may not be sufficient to address a particular company's risk, several options exist. For qualitative assessment of process hazards, Hazard and Operability Studies (HAZOP) and What‐If reviews are two of the most common petrochemical industry methods used. Up to 80% of a company's process hazard analysis (PHA) may consist of HAZOP and What‐If reviews (Nolan, Application of HAZOP and What‐If Safety Reviews to the Petroleum, Petrochemical and Chemical Industries, William Andrew Publishing/Noyes, 1994, p. 1). After the PHA, Layer of Protection Analysis (LOPA) is commonly used throughout industry to evaluate the required safety integrity level for instrumented protection layers in a semiquantitative manner (Dowell, International Conference and Workshop on Risk Analysis in Process Safety, CCPS/AIChE, 1997). HAZOP, What‐If, and LOPA are all straightforward methods and are relatively easy to perform. However, much like a hammer, they are not always the best or most appropriate tool for a given job. At times, more advanced methodologies such as Fault Tree Analysis, Quantitative Risk Assessment, Event Tree, Failure Mode, and Effects Analysis and Human Reliability Analysis are necessary to properly assess risk. However, these more advanced tools come with a price. They are often more expensive, time consuming, and require a higher level of expertise. The decision to use these higher level methodologies is not taken lightly and different companies use different criteria for determining when to take this next step. This article will present approaches by four companies, BASF, Celanese, The Dow Chemical Company, and Eastman Chemical Company. Each company will outline criteria used to determine when to go beyond HAZOP, What‐If, and LOPA and will present examples where more advanced techniques were used. The intent of this article is to provide readers with real world examples that demonstrate the appropriate application of the “right” tool and to illustrate what criteria can be used to make informed decisions regarding selection of a PHA methodology. © 2016 American Institute of Chemical Engineers Process Saf Prog 36: 38–53, 2017
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it