Crediting check valves as <scp>IPLs</scp>? Testing protocol to better understand check valve reliability
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
Abstract Conventional process safety wisdom assumes that check valves are not reliable safeguards. Experience indicates that check valves are prone to failure and that they may fail undetected. Therefore, the conservative assumption is that check valves may be listed in process hazard analyses as safeguards, but they are rarely considered to meet the standards required of an independent protection layer (IPL). Independent protection layers must be effective, independent, and auditable. Although independence is readily achievable by check valves, confirming and routinely auditing effectiveness is rarely pursued. And maintenance practices for check valves are often insufficient. Little data is available from operating companies regarding failure and leakage rates for different check valve types in various service applications or at various stages of service life. This paper examines a testing protocol that was put in place in 2014 for the purpose of testing check valves in order to apply layer of protection analysis (LOPA) credit to these valves for reverse flow scenarios. In order to understand check valve performance expectations, leakage allowances for new check valves are reviewed. Industry guidance and standards regarding consideration of check valves as safeguards or IPLs are also discussed. The analysis of new valve standards and the assessment of process safety requirements are the basis for establishing the pass/fail thresholds for the tests. The goal of sharing this information is that the discussion will stimulate others to consider the opportunity and the need to set‐up similar testing and to begin gathering and sharing a larger body of data on check valve performance in various applications. Accumulation of check valve performance data and sharing of that data should lead to better understanding of check valve performance by type, size, age, and service. Better performance may be achieved where maintenance is improved and where learnings are applied to selection and design. In instances where requirements are met and credit is due, check valves may be credited in PHA and LOPA.
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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it