API RP 581 Risk Based Inspection Technology – Demonstrating The Technology Through a Worked Example Problem

L. C. Kaley, P.E.
Trinity Bridge, LLC
Houston, Texas USA
The Joint Industry Project for Risk-Based Inspection (RBI JIP) was initiated and
managed by API within the refining and petrochemical industry in 1992. The
work from the JIP resulted in two publications, API 580 Risk-Based Inspection
released in 2002 and API 581 Base Resource Document – Risk-Based Inspection
originally released in 1996. The concept behind these publications was for API
580 to introduce the principles and present minimum general guidelines for RBI
while API 581 was to provide quantitative RBI methods. The API RBI JIP has
made major advances in the technology since the original publication of these
documents and released the second edition of API 581 - Recommended Practice
for Risk-Based Inspection Technology in September 2008. The second edition is
a three volume set, Part 1 – Inspection Planning Using API RBI Technology, Part
2 – Probability of Failure in API RBI, and Part 3 – Consequence Modeling in API
RBI. This paper provides a step-by-step worked example that demonstrates the
technology documented in API 581, Second Edition.
This document is intended solely for the internal use of Trinity Bridge, LLC. and may not be reproduced or
transmitted by any means without the express written consent of Trinity Bridge, LLC.
All rights reserved.
Copyright 2011, Trinity Bridge, LLC
The API Risk-Based Inspection (API RBI) methodology has been used in the
Refining and Petrochemical industries to manage the overall risk of a plant since
the mid-1990’s as a methodology for focusing inspection efforts on the process
equipment with the highest risk. API RBI provides the basis for making
informed decisions on inspection frequency, the extent of inspection, and the
most suitable type of NDE. In most processing plants, a large percent (80-
90%) of the total unit risk will be concentrated in a relatively small percentage
(10-20%) of the equipment. These potential high-risk components require
greater attention, often through a more extensive inspection or by more
advanced inspection methods. The cost of this increased inspection effort often
is offset by reducing inspection efforts on the larger percentage of lower risk
API 581 provides quantitative RBI methods to establish an inspection program.
The worked example presented in this paper follows the step-by-step procedure
outlined in the document to demonstrate use of the technology developed by
the API JIP. This will enable practitioners to better understand the methodology
and facilitate effective peer review.
The second edition of API 581was published in September 2008 and presents
the API RBI methodology in a three part volume:
Part 1 – Inspection Planning Using API RBI Technology
Part 2 – Determination of Probability of Failure in an API RBI Assessment
Part 3 – Consequence Analysis in an API RBI Assessment
Calculation of risk in API RBI involves the determination of a probability of
failure (POF) combined with the consequence of failure (COF). Failure in API
RBI is defined as a loss of containment from the pressure boundary resulting in
leakage to the atmosphere or rupture of a pressurized component. As damage
accumulates in a pressurized component during in-service operation the risk
increases. At some point, the risk tolerance or risk target is exceeded and an
inspection of sufficient effectiveness to determine the damage state of the
component is recommended. It is important to note that the inspection itself
does not reduce risk; however, it reduces the uncertainty in the component
condition by allowing better quantification of the damage present.
The following worked example follows API RBI’s step-by-step procedure to
calculate risk for a drum that is susceptible to internal corrosion and in-service
stress corrosion cracking. . API RBI typically involves evaluating the risk
associated with releases from four representative hole sizes. This example
demonstrates the calculation of POF for each of these representative hole sizes.
COF will be calculated using the Level 1 consequence model for one of the
representative hole sizes; however consequence results for all four hole sizes
will be used in the final consequence and risk calculations. Inspection planning
will be demonstrated using a simplified approach to demonstrate the technology
but without the iterative calculations typically required to plot risk over time and
to determine the impact of inspection on risk.
All figure, table and equation numbers used in this paper correspond to the
actual numbers in API 581 for an easy cross reference with the document.
The pressure vessel for the risk determination is V-07, a Debutanizer Overhead
Accumulator in a Saturated Gas plant. The drum operates at a temperature of
C (121
F) and a pressure,
, of 0.696 MPa (101 psig) and contains a
mixture of propane and butane with 0.11% H2S. The drum operates with
approximately a 50% liquid level.
The mechanical design basis of the drum is as follows:
Fabrication date
Design Pressure
1.138 MPa (165 psig)
Design Temperature
232 oC (450oF)
Weld Joint Efficiency
Material of Construction
ASTM 285 Gr. C 1968
Allowable Stress
94.8 MPa (13,750 psi)
Furnished thickness
20.637 mm (0.8125 inch)
Corrosion Allowance
3.175 mm (0.125 inch)
Post Weld Heat Treatment (PWHT)
Outside Diameter (OD)
2,520.95 mm (99.25 inch)
Inside Diameter (ID)
2,479.675 mm (97.625 inch)
9.144 m (30 ft)
Operating conditions allow aqueous conditions to occur with a localized
measured corrosion rate of 0.29 mmpy (11.4 mpy). In addition, stress
corrosion cracking caused by wet H2S is possible with a susceptibility of Low.
Inspection history from 2003-04-04 (B effectiveness level) revealed some
localized corrosion and a measured thickness of 19.05 mm (0.75 inch). No
history of inspection for wet H2S cracking has been conducted on this drum.
The process fluid in the drum has the following properties:
Vapor Density, ρV
13.8529 kg/m3 (0.8652 lb/ft3)
Liquid Density, ρl
538.4125 kg/m3 (33.612 lb/ft3)
-21.3056oC (-6.3oF)
Auto-Ignition Temperature; AIT
368.9oC (696oF)
Liquid Discharge Coefficient, Cdisch
Gravitational constant, gc
1 m/s2 (32.2 ft/s2)
Detection/Isolation factor factdi
Mitigation Factor, factmit
Inventory Group Mass, lbs
181,528 kg (400,200 lbs)
Management Factor
The plant information for inspection planning is as follows:
Inspection Planning
RBI Date
Plan Date
Financial Risk Target, €/yr
DF Target
The probability of failure used in API RBI is computed from Equation (2.1).
P t
gff D t F
In this equation, the probability of failure,
P t
( )
is determined as the product
of a generic failure frequency ,
a damage factor,
D t
( )
and a
management systems factor, FMS .
The gff for different component types was set at a value representative of the
refining and petrochemical industry’s failure data (Table 4.1) and is the rate of
failure prior to damage occurring in-service due to the operating environment.
The gff is provided for four discrete hole sizes by equipment type (i.e. vessels,
drums, towers, piping tankage, etc.), covering the full range of consequence
model release scenarios (i.e. small leak to rupture).
Adjustments are applied to the gff in order to account for damage mechanisms,
operating environment and mechanical integrity management practices within a
plant. The DF is based on the active damage mechanisms (local and general
corrosion, cracking, creep, etc.), based on original design and the current
condition of the component assessed during inspection. The DF modifies the
industry gff to calculate the POF for the specific component being evaluated.
The management systems adjustment factor, FMS , is based on the influence of
the facility’s management system on the mechanical integrity of the plant
equipment. This factor accounts for the probability that accumulating damage
which results in loss of containment will be discovered in time and is directly
proportional to the quality of a facility’s mechanical integrity program. The
factor is the result of an audit of a facilities or operating unit’s management
systems that affect plant risk.
Calculation of Thinning Damage Factor
The following example demonstrates the steps required for calculating the
thinning damage factor:
1) Determine the number of historical inspections and the inspection
effectiveness category for each: A 1B level inspection was performed on
2003-04-04 with a measured thickness of 19.05 mm (0.75 inch)
2) Determine the time in-service, age, from the last inspection reading, trd to
the RBI and Plan Dates.
age @ RBI Date= 5.08 years
age @ Plan Date= 15.08 years
3) Determine the corrosion rate of the base material, Cr,bm : 0.29 mmpy (11.4