Analysis of spring operated pressure relief valve proof test data: Findings and implications

Abstract

Ten years of research into spring operated pressure relief valve (SOPRV) proof test data are reviewed. Previously unpublished results are also presented. The principle findings are: proof test data alone are insufficient to properly analyze SOPRV safety behavior; root cause analysis (RCA) of all failed SOPRV is also required; probability of initial failure is a real and concerning phenomenon in both new (not previously installed) as well as used (previously installed) SOPRV but does not affect all SOPRV equally; SOPRV failure rates and safety performances depend on many variables including set pressure, SOPRV design and manufacturing specifics, and end user safety and maintenance practices; certain SOPRV require shorter proof test intervals (2–3 years) to maintain desired safety ratings but others can endure much longer proof test intervals with only small additional safety risks. Understanding all of the parameters which impact SOPRV safety performance and collecting safety performance data (including RCA of failed SOPRV) at the individual facility level are the keys to optimizing commissioning and pre‐installation proof testing and proof test intervals, and potentially to extending turnaround times. © 2018 American Institute of Chemical Engineers Process Saf Prog 37: 467–477, 2018

How extreme failure‐data filtering leads to poor process safety

“…While SRSite originally classified failures as random or systematic (from SRSite's viewpoint), they always counted all real field failures in their failure rate estimations. However, they later performed data analysis 10 based on a different failure classification scheme. In this new scheme, they classified failures as those that they could not practically influence and those that they could practically influence.…”

SIL‐3, SIL‐2, and unicorns (there is a high probability your SIL 2 and SIL 3 SIFs have no better performance than SIL 1)

“…In addition, for critical steps such as re-opening and car-sealing the block valves under a relief valve after the relief valve is returned from maintenance, the error probability is about 0.01 (1/100) to 0.04 (1/15) 15 ; plus, the average probability of being in a "fail to function" state at time zero for a relief device is between 0.01 (1/100) and 0.02 (1/50). [16][17][18] Both of these tasks have multiple checks and have procedures (similar to what is done when servicing a SIF and when using bypasses for an SIF) and yet the observed human error probability remains between 0.01 and 0.02. Coupling represents the probability of repeating an error (or repeating success) on a second identical task, given that an error was made on the first task.…”

Mathematical Modelling and Dynamic Analysis of a Direct‐Acting Relief Valve Based on Fluid‐Structure Coupling Analysis

“…Direct-acting relief valve is widely applied for pressure adjustment in fuel pump [1][2][3][4][5]; when it is used underwater for torpedo, its stability is generally affected by the ocean environment. Specifically, as there is pressure fluctuation at the relief valve due to the influences of water depth, the valve element can lose its stability via Hopf bifurcation [6][7][8] and can further enter into chaos via grazing bifurcation due to the axial impact between the valve element and valve seat [6,[9][10][11][12][13][14].…”