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

1.0

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

ABSTRACT

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.

2.0

INTRODUCTION

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

equipment.

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.

3.0

WORKED EXAMPLE INTRODUCTION

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

1972-01-01

Design Pressure

1.138 MPa (165 psig)

Design Temperature

232 oC (450oF)

Weld Joint Efficiency

0.85

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)

Yes

Outside Diameter (OD)

2,520.95 mm (99.25 inch)

Inside Diameter (ID)

2,479.675 mm (97.625 inch)

Length

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)

NBP

-21.3056oC (-6.3oF)

Auto-Ignition Temperature; AIT

368.9oC (696oF)

Liquid Discharge Coefficient, Cdisch

0.61

Gravitational constant, gc

1 m/s2 (32.2 ft/s2)

Detection/Isolation factor factdi

1.0

Mitigation Factor, factmit

1.0

Inventory Group Mass, lbs

181,528 kg (400,200 lbs)

Management Factor

1.0

The plant information for inspection planning is as follows:

Inspection Planning

RBI Date

2008-05-01

Plan Date

2018-05-01

Financial Risk Target, €/yr

1,000

DF Target

3,000

4.0

PROBABILITY OF FAILURE

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.

4.1

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

mpy)