A risk‐based methodology to estimate shutdown interval considering system availability
Bibliographic record
Abstract
This article presents a risk‐based methodology to estimate shutdown inspection and maintenance interval considering system availability. Most inspection and maintenance activities are performed when the plant/unit is in the operational state. However, some inspection and maintenance activities require the plant to be in a nonoperational or shutdown state. In most cases, operating companies adopt a shutdown schedule based on the original equipment manufacturer's (OEM) suggested recommended periods. However, this may not be the best strategy as OEM recommended duration is general and may not reflect the current state of operation. The proposed methodology is unique in the sense that it identifies a shutdown interval by identifying the critical equipment in terms of risk considering availability and safety of the operating unit. It optimizes process plant shutdown interval to minimize the risk (in dollar terms). The Markov process is used to establish the state diagram to calculate system availability. The proposed methodology is comprised of three steps namely, risk‐based equipment selection, shutdown availability modeling of a complex system using the Markov process, and risk‐based shutdown inspection and maintenance interval modeling. It can be applied to process plants such as those for liquefied natural gas processing, petrochemicals, and refineries. The key elements for the success of the proposed methodology are the plant‐specific data and identification of critical equipment. © 2014 American Institute of Chemical Engineers Process Saf Prog 34: 267–279, 2015
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".