Effect of Truncated Demand and Capacity Distributions on Reliability of Pipelines

2003 ◽  
Author(s):  
Ziad Nakat ◽  
Robert Bea
Keyword(s):  
Pressure Relief ◽  
Physical Constraints ◽  
Demand Distribution ◽  
On Demand ◽  
Truncated Distributions ◽  
Pressure Relief Valves ◽  
Analysis Of Results ◽  
Management Policies ◽  
Comparison And Analysis ◽  
Demand And Capacity

Reliability of pipelines has been considered assuming different probability continuous distributions of demand and capacities. These distributions in reality can be truncated at the tails by physical constraints such as pressure relief valves (demand truncation), and hydro-testing (capacity truncation). This paper describes the effect of truncations on the reliability of pipelines. The effect of truncation by relief valves on the demand distribution is studied first; the effect of truncation by hydro-testing on the capacity distribution is studied second; and last, the combined effect of truncation on demand and capacity is studied. A comparison and analysis of results is presented to assess the importance of truncated distributions of demand and capacity on the reliability of pipelines. The results show that truncated distributions can have large effects on the reliability and should be accounted for, since they can alter significantly inspection and management policies.

The Effects of Maintenance Actions on the Average Probability of Failure on Demand of Spring Operated Pressure Relief Valves

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Julia V. Bukowski ◽  
William M. Goble ◽  
Robert E. Gross ◽  
Stephen P. Harris
Keyword(s):  
Probability Of Failure ◽  
Pressure Relief ◽  
Periodic Inspection ◽  
On Demand ◽  
Plant Safety ◽  
Safety Integrity Level ◽  
Proof Testing ◽  
Average Probability ◽  
Pressure Relief Valves ◽  
Inspection And Maintenance

The safety integrity level (SIL) of equipment used in safety instrumented functions is determined by the average probability of failure on demand (PFDavg) computed at the time of periodic inspection and maintenance, i.e., the time of proof testing. The computation of PFDavg is generally based solely on predictions or estimates of the assumed constant failure rate of the equipment. However, PFDavg is also affected by maintenance actions (or lack thereof) taken by the end user. This paper shows how maintenance actions can affect the PFDavg of spring operated pressure relief valves (SOPRV) and how these maintenance actions may be accounted for in the computation of the PFDavg metric. The method provides a means for quantifying the effects of changes in maintenance practices and shows how these changes impact plant safety.

scholarly journals The Effects of Maintenance Actions on the PFDavg of Spring Operated Pressure Relief Valves

2014 ◽  
Author(s):  
Julia V. Bukowski ◽  
William M. Goble ◽  
Robert E. Gross ◽  
Stephen P. Harris
Keyword(s):  
End User ◽  
Pressure Relief ◽  
Periodic Inspection ◽  
On Demand ◽  
Plant Safety ◽  
Safety Integrity Level ◽  
Proof Testing ◽  
Average Probability ◽  
Pressure Relief Valves ◽  
Inspection And Maintenance

The safety integrity level (SIL) of equipment used in safety instrumented functions is determined by the average probability of failure on demand (PFDavg) computed at the time of periodic inspection and maintenance, i.e., the time of proof testing. The computation of PFDavg is generally based solely on predictions or estimates of the assumed constant failure rate of the equipment. However, PFDavg is also affected by maintenance actions (or lack thereof) taken by the end user. This paper shows how maintenance actions can affect the PFDavg of spring operated pressure relief valves (SOPRV) and how these maintenance actions may be accounted for in the computation of the PFDavg metric. The method provides a means for quantifying the effects of changes in maintenance practices and shows how these changes impact plant safety.

Statistical Performance Evaluation of Soft (Elastomer) Seat Pressure Relief Valves

2013 ◽  
Author(s):  
Robert E. Gross ◽  
Stephen P. Harris
Keyword(s):  
South Carolina ◽  
Performance Evaluation ◽  
The United States ◽  
Probability Of Failure ◽  
United States Department ◽  
Relief Valve ◽  
Pressure Relief ◽  
On Demand ◽  
Pressure Relief Valves ◽  
Statistical Performance Evaluation

Risk-based inspection methods enable estimation of the probability of failure on demand for spring-operated pressure relief valves at the United States Department of Energy’s Savannah River Site in Aiken, South Carolina. This paper presents a statistical performance evaluation of soft seat spring operated pressure relief valves. These pressure relief valves are typically smaller and of lower cost than hard seat (metal to metal) pressure relief valves and can provide substantial cost savings in fluid service applications (air, gas, liquid, and steam) providing that probability of failure on demand (the probability that the pressure relief valve fails to perform its intended safety function during a potentially dangerous over pressurization) is at least as good as that for hard seat valves. The research in this paper shows that the proportion of soft seat spring operated pressure relief valves failing is the same or less than that of hard seat valves, and that for failed valves, soft seat valves typically have failure ratios of proof test pressure to set pressure less than that of hard seat valves.

Dynamic Behavior of Transportation Pressure Relief Valves Under Simulated Fire Impingement Conditions

1999 ◽  
Vol 122 (1) ◽  
pp. 60-65 ◽  
Author(s):  
A. J. Pierorazio ◽  
A. M. Birk
Keyword(s):  
Dynamic Testing ◽  
Test Facility ◽  
Time Varying ◽  
Relief Valve ◽  
Pressure Relief ◽  
Flow Rates ◽  
Depth Analysis ◽  
Pressure Relief Valves ◽  
Accident Conditions ◽  
Insight Into

This paper presents the results of the first full test series of commercial pressure relief valves using the newly constructed Queen’s University/Transport Canada dynamic valve test facility (VTF) in Maitland, Ontario. This facility is unique among those reported in the literature in its ability to cycle the valves repeatedly and to measure the time-varying flow rates during operation. This dynamic testing provides much more insight into valve behavior than the single-pop or continuous flow tests commonly reported. The facility is additionally unique in its simulation of accident conditions as a means of measuring valve performance. Specimen valves for this series represent 20 each of three manufacturers’ design for a semi-internal 1-in. 312 psi LPG relief valve. The purpose of this paper is to present the procedure and results of these tests. No effort is made to perform in-depth analysis into the causes of the various behaviors, nor is any assessment made of the risk presented by any of the valves. [S0094-9930(00)01201-4]

Clustering Bathymetric Data for Electronic Navigational Charts

2016 ◽  
Vol 69 (5) ◽  
pp. 1143-1153 ◽  
Author(s):  
Marta Wlodarczyk–Sielicka ◽  
Andrzej Stateczny
Keyword(s):  
Clustering Algorithm ◽  
Search Algorithm ◽  
Clustering Algorithms ◽  
Data Set ◽  
Bathymetric Data ◽  
Large Sets ◽  
Analysis Of Results ◽  
Comparison And Analysis ◽  
Self Organising Map ◽  
Source Of Information

An electronic navigational chart is a major source of information for the navigator. The component that contributes most significantly to the safety of navigation on water is the information on the depth of an area. For the purposes of this article, the authors use data obtained by the interferometric sonar GeoSwath Plus. The data were collected in the area of the Port of Szczecin. The samples constitute large sets of data. Data reduction is a procedure to reduce the size of a data set to make it easier and more effective to analyse. The main objective of the authors is the compilation of a new reduction algorithm for bathymetric data. The clustering of data is the first part of the search algorithm. The next step consists of generalisation of bathymetric data. This article presents a comparison and analysis of results of clustering bathymetric data using the following selected methods:K-means clustering algorithm, traditional hierarchical clustering algorithms and self-organising map (using artificial neural networks).

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