Risk Mitigation of Pipeline Assets through Improved
Corrosion Modeling
by
Richard A. Mullen
B.S., California State University, Sacramento, 2007
Submitted to the Department of Mechanical Engineering and the MIT Sloan School of
w
Management in partial fulfillment of the requirements for the degrees of
Master of Science in Mechanical Engineering
and
Master of Business Administration
=
in conjunction with the Leaders for Global Operations Program at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
June 2015
aL)LL
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@ Richard A. Mullen, MMXV. All rights reserved.
The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electr-om
copies of this thesis document in whole or in part in any medium now known or hereafter created.
redacted
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Author
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Department of Mechanical Engineering and the MIT Sloan School of Management
May 8, 2015
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Ronald Ballinger, Thesis Supervisor
Professor, Materials Science and Engineering, and Nuclear Science and Engineering
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William F. Pounds Professor of Managem
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Georgia Perakis, Thesis Supervisor
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Alexander Slocum, Thesis Reader
Signature redacted Professor, Mechanical Engineering
A p p roved by ..............................................................................
David E. Hardt
Chairman, Committee on Graduate Students, Mechanical Engineering
Ipproved by..................
Signature redacted ..........
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Director, MBA Program, MIT Sloan School of Management
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2
Risk Mitigation of Pipeline Assets through Improved Corrosion Modeling
by
Richard A. Mullen
Submitted to the Department of Mechanical Engineering and the MIT Sloan School of
Management on May 8, 2015, in partial fulfillment of the requirements for the degrees of
Master of Science in Mechanical Engineering
and
Master of Business Administration
Abstract
Infrastructure has to weather the elements and still function. Gas transmission and distribution piping at a utility are no exception. Atmospheric corrosion deteriorates the integrity
of the natural gas system, and utilities need to respond with countermeasures in order to
mitigate the risk. The ability to predict where atmospheric corrosion will cause leaks will
allow for a better allocation of resources in mitigating the risk caused by corrosion.
First a corrosion simulation model was developed to predict the number of leaks in each
geographic area in PG&E's service area. Past meteorological data, past pollution data,
2014 atmospheric corrosion inspections on 2.27 million meters, leak data, and gas system
asset information (meter age, type, etc.) were used. The qualitative observations and a
quantitative model were then coupled in a simulation model to predict the number of leaks
depending on the years between atmospheric corrosion inspections.
Utilizing the output of the corrosion prediction model, an optimization model was developed to determine the atmospheric corrosion inspection frequency that will minimize the
risk of leaks to the system. This model will allow PG&E to understand how reallocating
inspection resources can reduce risk of leaks.
The overall results indicate that data quality plays a very important role in coupling qualitative observations with a quantitative model. From the model developed and analyzed in
this thesis, several opportunities for better data collection were identified. By collecting targeted data on localized corrosion and corrosion rates, qualitative inspections can contribute
greatly to accurately model corrosion where quantitative models are lacking.
Thesis Supervisor: Ronald Ballinger
Title: Professor, Materials Science and Engineering, and Nuclear Science and Engineering
Thesis Supervisor: Georgia Perakis
Title: William F. Pounds Professor of Management Science, MIT Sloan School of Management
Thesis Reader: Alexander Slocum
Title: Professor, Mechanical Engineering
3
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4
Acknowledgments
First I would like to thank my academic advisers, Professor Georgia Perakis and Professor
Ron Ballinger for their technical guidance, invaluable insight, and consistent support through
this project and resulting thesis.
I would also like to thank the leadership and employees at Pacific Gas and Electric for providing such a great and rewarding experience. Specifically, I would like to thank Mallik
Angalakudati and Paul Caffery for initiating a challenging and interesting research project
and for providing expert guidance during the course of the project, and Sara Burke and
Sumeet Singh for providing the in depth understanding, support, and resources necessary to
make this project successful.
Finally, I would like to thank my wife Sunny and kids Dean, Lyla, and Alice for their love,
encouragement, and support during my time in the LGO program at MIT.
5
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6
Contents
Introduction and Background
1.1
Company Overview .....
1.2
1.4
........................
15
Corrosion in the Utility Industry and PG&E . . . . . . . . .
. . . . . . . .
15
1.2.1
Direct Assessment for Atmospheric Corrosion
. . . .
. . . . . . . .
16
1.2.2
Risk Assessment Strategies for Atmospheric Corrosion
. . . . . . . .
17
The Problem and Goal . . . . . . . . . . . . . . . . . . . . .
. . . . . . . .
17
1.3.1
. . . . . . . .
18
. . . . . . . .
19
.
.
.
. . . . . . . .
Important Terminology . . . . . . . . . . . . . . . . .
.
1.3
15
Thesis Overview and Contribution
. . . . . . . . . . . . . .
.
1
2 Background and Literature Review
General Corrosion . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21
2.1.2
Localized Corrosion . . . . . . . . . . . . . . . . . . . . . . . . . . .
22
2.1.3
Environmental Parameters Affecting Atmospheric Corrosion . . . .
22
2.1.4
Defining Atmospheric Corrosion Atmospheres
. . . . . . . . . . . .
24
Corrosion Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
24
2.2.1
International Organization for Standardization (ISO) Model
. . . .
24
2.2.2
Long-Term Atmospheric Corrosion Exposure . . . . . . . . . . . . .
25
2.2.3
Uhlig Corrosion Handbook . . . . . . . . . . . . . . . . . . . . . . .
26
2.2.4
Comparison of Various Models . . . . . . . . . . . . . . . . . . . . .
27
.
.
.
.
.
.
.
.
2.1.1
Corrosion Risk Model
30
3.1
30
O verview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
.
3
21
.
2.2
Atmospheric Corrosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
.
2.1
21
7
3.2
31
3.2.1
Pacific Gas and Electric Customer Meter Asset Data
. . . . . . . . .
31
3.2.2
Pacific Gas and Electric Inspection and Leak Data
. . . . . . . . . .
33
3.2.3
Meteorological Data: Temperature and Relative Humidity
3.2.4
Pollution Data: Chlorides and Sulfur Dioxide
. . . . . .
33
. . . . . . . . . . . . .
34
Model Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
35
3.3.1
Mapping Environmental Parameters to Customer Meters . . . . . . .
35
3.3.2
Assigning Divisions to Corrosion Environments
. . . . . . . . . . . .
36
3.3.3
Base Corrosion Model Validation
. . . . . . . . . . . . . . . . . . . .
37
3.3.4
Attribute Gauge Repeatability and Reproducibility Study
. . . . . .
39
3.3.5
Creating an Expected Data Set . . . . . . . . . . . . . . . . . . . . .
42
Corrosion Prediction Model Structure . . . . . . . . . . . . . . . . . . . . . .
44
3.4.1
Predicting Failures - The Complete Corrosion Prediction Model
. . .
44
3.4.2
Simulating the Start of Corrosion (Cstart) . . . . . . . . . . . . . . . .
45
3.4.3
Localized Corrosion Acceleration Factors (A and H)
. . . . . . . . .
48
3.4.4
Calibrating the Model
. . . . . . . . . . . . . . . . . . . . . . . . . .
48
3.5
M odel R esults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
49
3.6
Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
49
3.6.1
Modeling with the Original Data Set
. . . . . . . . . . . . . . . . . .
50
3.6.2
Modeling with the Expected Data Set
. . . . . . . . . . . . . . . . .
51
3.6.3
Logistic Model Results . . . . . . . . . . . . . . . . . . . . . . . . . .
52
C onclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
54
3.3
3.4
3.7
4
D ata selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Optimizing Inspection Interval
55
4.1
Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
55
4.1.1
Prediction Model Results for Various Inspection Intervals . . . . . . .
55
Optimization Model Development . . . . . . . . . . . . . . . . . . . . . . . .
56
4.2.1
Optimizing Inspection Interval by Minimizing Cost
. . . . . . . . . .
56
4.2.2
Optimizing Inspection Interval by Minimizing Risk
. . . . . . . . . .
58
. . . . . . . . . . . . . . . . . . . . .
59
4.2
4.3
Optimizing Inspection Interval Results
8
4.3.1
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
62
.
61
.
4.4
Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . .
5 Conclusions and Future Work
.
. . . . . . . .
64
5.1.1
Expand Model to Cover All Divisions
. . . . . . . .
64
5.1.2
Process Improvement . . . . . . . .
. . . . . . . .
65
5.1.3
Atmospheric Corrosivity Data . . .
. . . . . . . .
66
5.2
Improving the Optimization Model . . . .
. . . . . . . .
67
5.3
Conclusions . . . . . . . . . . . . . . . . .
. . . . . . . .
68
.
.
.
Improving the Prediction Model . . . . . .
.
5.1
64
9
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10
List of Figures
1-1
Map of PG&E's gas service area . . . . . . . . . . . . . . . . . . . . . . . . .
16
1-2
Natural gas system schematic
. . . . . . . . . . . . . . . . . . . . . . . . . .
17
1-3
Examples of corrosion inspection grades
. . . . . . . . . . . . . . . . . . . .
19
2-1
Comparison of the ISO, Morcillo et al., and Uhlig models in a marine atmosphere..........
27
........................................
2-2
Comparison of the ISO, Morcillo et al., and Uhlig models in a rural atmosphere. 28
2-3
Comparison of the ISO, Morcillo et al., and Uhlig models in a combined
industrial and marine environment. . . . . . . . . . . . . . . . . . . . . . . .
29
3-1
Construction of gas customer meter . . . . . . . . . . . . . . . . . . . . . . .
32
3-2
Distribution of corrosion grades in divisions that were inspected for atmospheric corrosion in 2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
34
3-3
Weather stations in California that are managed by the CA ARB. . . . . . .
35
3-4
Map of air monitoring sites that analyze for chlorides and sulfur dioxide.
. .
36
3-5
Concentration of airborne chlorides in California in 2012
. . . . . . . . . . .
37
3-6
Plots comparing ISO predicted corrosion attack to the actual corrosion attack
in Davis (rural), Martinez (marine/industrial), and Richmond (marine).
. .
39
3-7
Prediction model results compared to current failure rate . . . . . . . . . . .
50
3-8
Georgraphic visualization of MAPE . . . . . . . . . . . . . . . . . . . . . . .
52
4-1
Optimization model sensitivity analysis . . . . . . . . . . . . . . . . . . . . .
62
5-1
Atmospheric corrosion monitor sketch . . . . . . . . . . . . . . . . . . . . . .
67
11
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12
List of Tables
2.1
Summary of select numerical models proposed by Morcillo et al.
. . . . . .
26
3.1
Divisions classified by corrosive atmosphere . . . . . . . . . . . . . . . . . . .
38
3.2
Summary of corrosion grade criteria . . . . . . . . . . . . . . . . . . . . . . .
41
3.3
Average environmental parameters for each division . . . . . . . . . . . . . .
43
3.4
Linear regression parameters . . . . . . . . . . . . . . . . . . . . . . . . . . .
44
3.5
Prediction model results with mean absolute percent error
. . . . . . . . . .
51
3.6
Available explanatory variables for logistic model
. . . . . . . . . . . . . . .
51
3.7
Logistic model formulation with original data
. . . . . . . . . . . . . . . . .
52
3.8
Logistic model formulation with expected data . . . . . . . . . . . . . . . . .
53
3.9
Comparison of logistic regression model results between actual data and expected data ........
. .. . ...... .. ....... .....
............
53
4.1
Inspection frequency optimized by cost . . . . . . . . . . . . . . . . . . . . .
59
4.2
Inspection frequency optimized by division . . . . . . . . . . . . . . . . . . .
60
4.3
Inspection frequency optimized by corrosive environment . . . . . . . . . . .
61
5.1
Proposed inspection criteria . . . . . . . . . . . . . . . . . . . . . . . . . . .
66
13
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14
Chapter 1
Introduction and Background
1.1
Company Overview
Pacific Gas and Electric Company (PG&E) is a large utility operating an extensive transmission and distribution system for both electricity and natural gas. Their operations cover
approximately 70,000 square miles of California north of Bakersfield as shown in Figure 1-1.
PG&E provides natural gas and electric service to approximately 16 million people.
PG&E Gas Operations maintains and operates over 42,000 miles of distribution pipeline
and over 6,400 miles of transmission pipeline. They have over 4.3 million natural gas customer accounts.
1.2
Corrosion in the Utility Industry and PG&E
A study published by the National Association of Corrosion Engineers states that metallic
corrosion is the third largest cost to the US economy and that the direct and indirect costs
of corrosion in the US are about 6.2% of the Gross Domestic Product. The utility industry's
share is approximately 34.7% of the total cost [1].
Corrosion of transmission and distribution assets is a cause for serious concern for utilities.
At PG&E, corrosion is the root cause of many of the largest risks affecting their natural gas
infrastructure. While complete eradication of corrosion is impossible, corrosion mitigation
is key to maintaining the safety of gas pipelines [2]. There are several methods that PG&E
15
Figure 1-1: Map of PG&E's gas service area
uses to mitigate the effects of corrosion of their gas assets including direct assessment, in-line
inspections, cathodic protection, corrosion inhibiting coatings, and risk assessment strategies.
There are two main types of corrosion that degrade the natural gas infrastructure: galvanic and atmospheric. Galvanic corrosion is actively protected against by cathodic protection (on buried assets) while atmospheric corrosion is monitored by direct assessment. A
schematic of a basic natural gas network is shown in Figure 1-2. The focus of this thesis
will be the effect of atmospheric corrosion on gas customer meter sets, which is the interface
between the natural gas distribution and the customer's home.
1.2.1
Direct Assessment for Atmospheric Corrosion
Currently, federal regulation requires all above ground utility gas assets to be inspected every
36 months for atmospheric corrosion [3]. While this time-frame is conservative in many cases,
it is not optimal and treats all of PG&Es gas assets the same. California has a very wide
range of corrosive atmospheres; by treating all assets the same, wasted resources may be
expended in some areas and system safety may be decreased in others.
16
Source PHMSA
Figure 1-2: Schematic of the natural gas collection, transmission, and distribution systems.
This thesis focuses on atmospheric corrosion on customer meters.
1.2.2
Risk Assessment Strategies for Atmospheric Corrosion
Risk assessment strategies play an important role in managing corrosion at PG&E. A robust
risk management system coupled with asset integrity models are utilized to allocate resources
for maintenance and repair of gas assets.
Other strategies, such as corrosion prediction
models, have not been developed at PG&E. Were such models to be developed and validated,
they could be used to optimize inspection intervals.
Using validated corrosion prediction
models could allow PG&E to provide safe and cost effective transportation and distribution
of natural gas.
1.3
The Problem and Goal
Risk reduction of the gas system is a top priority at PG&E. Atmospheric corrosion at customer meter sets has been identified as an area where risk could be reduced by implementing
a corrosion modeling strategy.
PG&E needs to understand the effects of atmospheric corrosion on their gas meter sets
for two reasons:
e PG&E needs to understand how the atmospheric corrosion related risk varies depending
on geographic location.
e PG&E needs to evaluate whether the required 36 month inspections are providing
adequate system safety.
17
A method to analyze the corrosion of the gas meter sets is by means of a quantitative
corrosion prediction model. The ability to predict where corrosion will cause system failures
(leaks) and optimize the results could allow for better allocation of limited company resources
to minimize system risk.
The goal of this thesis is to model atmospheric corrosion and assess the effect of modifying
the atmospheric corrosion inspection cycle frequency on the failure rate of the 4.3 million
gas customer meter sets. This analysis is for investigative purposes and does not suggest
that PG&E will deviate from fulfilling the federally mandated requirement of 36 month
atmospheric corrosion inspections.
1.3.1
Important Terminology
Divisions
PG&E's service area is divided into 18 geographic areas called divisions. These divisions
provide a useful grouping of meters throughout this thesis.
Atmospheric Corrosion Inspection Grades
During the atmospheric corrosion inspections conducted in 2014, "grades" were assigned to
both the meter and the riser (focusing on the soil-to-air transition). The grades rated the
severity of the corrosion using a three level methodology: no corrosion, low corrosion, and
high corrosion. Examples of these grades for the meter are found in Figure 1-3. Further
discussion of the corrosion grades takes place in Chapter 3.
Failure
It is important to note that a "failure" in the context of this thesis is defined as a leak in
a customer meter set attributed to atmospheric corrosion. The vast majority of these leaks
release very little gas into the atmosphere and there is no explosive hazard.
18
Figure 1-3: Examples of corrosion inspection grades. From left to right: No corrosion, low
corrosion, high corrosion.
Failure Rate
The purpose of this thesis is not to perform an in depth risk assessment. PG&E has a robust
risk management system, and this work only addresses a single facet of the risk management
process [4]: the probability of failures, or leaks, attributed to atmospheric corrosion. It is
taken as given that the risk management group at PG&E has a full risk assessment structure
with which an advanced corrosion model can be added to. For this thesis, the probability of
failure, or failure rate, will be used to describe the risk of failure per year and is calculated
by the following equation:
FailureRate(%/year)=Failures(meters/year)
TotalMeters(meters)
1.4
(1.1)
Thesis Overview and Contribution
Current atmospheric corrosion research is introduced in Chapter 2. The basics of both
general and localized corrosion are discussed, as well as the environmental parameters that
have the greatest effect on atmospheric corrosion rates. Several corrosion models developed
by researchers are presented and compared.
This thesis presents the methodology developed to predict gas customer meter failures
in Chapter 3. First, the available data is discussed, and then the focus is shifted on to how
19
the data was used to predict customer meter failures. A significant amount of research has
been performed on the mechanism and physics of corrosion to assist in developing corrosion
prediction models. However, much of this research has been limited to corrosion experiments
and discounts the vast amount of qualitative corrosion data that industry collects.
This work focuses on a modeling methodology that pairs an existing corrosion model
with the qualitative observations collected by PG&E in order to better define how corrosion
progresses within a their service area. The presented corrosion prediction model could give
PG&E a better understanding of how the corrosivity of an atmosphere and time can affect
customer meter set failures.
Predicting where failures will occur is the basis on which an optimization model to
minimize the customer meter set failure rate. This is presented in Chapter 4. The potential
waste caused by the existing policy of inspecting gas customer meters every 36 months is
analyzed by optimizing the inspection interval while maintaining the current failure rate
and minimizing the cost. Several optimized inspection frequencies are then presented that
minimize the customer meter set failure rate while varying the budgetary constraint.
The model and analysis presented in this thesis gives PG&E a framework through which
they can analyze how the corrosivity of a region within their service area affects the failure
rate of customer meter sets. It also provides a model that PG&E may use to determine the
optimal atmospheric corrosion inspection frequency for customer meter sets to minimize the
risk of leaks.
20
Chapter 2
Background and Literature Review
2.1
2.1.1
Atmospheric Corrosion
General Corrosion
Atmospheric corrosion is an electrochemical process, and as such, it requires the presence
of an electrolyte. A film electrolyte tends to form on metallic surfaces under atmospheric
exposure after a certain critical humidity level is reached [5]. In the absence of atmospheric
pollutants, carbon steel corrodes as described by the following chemical equations:
The iron (Fe) in the steel is the reducing agent and gives up electrons (e):
2Fe
-
2Fe2+ + 4e
(2.1)
Oxygen (02) is the oxidizing agent and gains electrons, which, in the presence of water(H 2 0),
forms hydroxide ions (OH-):
02-+
2H20 + 4e -÷ 40H-
(2.2)
The combined reaction shows that the reduced iron reacts with the hydroxide ions to
form ferrous hydroxide (Fe(OH)2 ):
2Fe2 + + 40H 21
2Fe(OH)2
(2.3)
In the presence of oxygen, the ferrous hydroxide oxidizes and forms rust (Fe2 03) [6].
2.1.2
Localized Corrosion
Localized corrosion is important and is the cause of all of the atmospheric corrosion failures
on PG&E's gas assets. The problem is complex, and a summary of the existing literature
is not the goal of this section. The considerations that were taken in our general analysis of
the localized corrosion problem are presented here.
Localized corrosion encompasses several types of corrosion such as pitting, crevice corrosion, intergranular attack, and stress corrosion cracking. The types that are of most concern
in the context of atmospheric corrosion of gas customer meter sets, pitting and crevice corrosion, have a similar mechanism. Pitting corrosion is aggravated by the presence of chlorides
or other halides, and occurs within or above a critical electrochemical potential range [6].
Pitting corrosion is difficult to model because all environmental and chemical interactions
are not fully understood
[5].
Accelerated corrosion caused by localized corrosion is a greater
cause of concern than general corrosion[7].
2.1.3
Environmental Parameters Affecting Atmospheric Corrosion
The main meteorological parameters that affect atmospheric corrosion are relative humidity
(or time of wetness) and temperature, and the presence of airborne pollutants, such as sulfur
dioxide and chlorides, cause other reduction reactions to occur that accelerate the corrosion
process [6].
Relative Humidity
Water, as an aqueous film, is necessary for atmospheric corrosion to occur. For atmospheric
corrosion, this water film is provided primarily by moisture in the air. The corrosion rate
of steel depends on the time of wetness, which is defined as the period during which the
metal is exposed to a relative humidity above the critical humidity level [5]. Researchers
define the critical humidity level for steel as low as 60% to as high as 80% , depending on
22
the level of pollutants found in the atmosphere
[8],
[9], [10]. The ISO has developed a model
that uses annual average humidity as a proxy for time of wetness when defining a corrosive
atmosphere [11].
Temperature
Temperature effects on corrosion is complex since relative humidity is affected so strongly
by it. For a constant relative humidity, increasing temperature would increase the rate of
electrochemical reactions [6]. Raising the temperature, however, will cause relative humidity
to decrease and the evaporation of the electrolyte layer to increase. This effect is dominant
at temperatures greater than about 100 Celsius, so corrosivity decreases as temperature
increases in this temperature range [11].
Sulfur Dioxide
Airborne sulfur dioxide (SO 2) primarily comes from the combustion of sulfur containing
fossil fuels. The sulfur dioxide accelerates corrosion by reacting with the iron to form ferrous
sulfate. The hygroscopic nature of sulfate promotes condensation and thus increases the
time of wetness of the steel [12]. The sulfate also impairs the protective nature of corrosion
product film [6].
Chlorides
Airborne chlorides (CI-) are typically found near the ocean. They are carried by the wind
and deposited on metal surfaces. Similar to S02, they are hygroscopic and they decrease
the protection of corrosion product film [5]. Researchers have found that the corrosion layer
on carbon steel cannot prevent chlorides from reaching the substrate steel, thus accelerating
corrosion [13].
23
2.1.4
Defining Atmospheric Corrosion Atmospheres
Rural/Urban
A rural atmosphere contains organic and inorganic dusts which combine with moisture to
create a corrosive atmosphere that is typically milder than any other location. In an urban
atmosphere, low levels of sulfur dioxide are found due to the concentration of automobiles,
though this does not have a large impact on the corrosiveness of the atmosphere [14].
Industrial
An industrial atmosphere is characterized by pollution primarily in the form of sulfur compounds that combine with rain, fog, or dew to create a corrosive film on exposed steel [14].
Sulfur dioxide is the component of highest concern [6].
Marine
A marine atmosphere contains chlorides typically carried by winds from oceans and higher
humidity levels, thus increasing the corrosivity of the atmosphere [14].
2.2
Corrosion Modeling
Multiple linear regression analysis is used extensively to model corrosion. Many studies have
identified that atmospheric corrosion follows a linear bilogarithmic, or a power law model
[12], [15], [16]. Other researchers have created location specific models that are valid for only
a tight band of environmental parameters [17], [5]. Three models will be discussed here to
highlight the range of goals of various researchers, from a general model to more location
specific models.
2.2.1
International Organization for Standardization (ISO) Model
One of the most extensive studies to classify atmospheric corrosion was conducted by the
International Organization for Standardization (ISO). The study, called ISO CORRAG, went
24
on for 8 years with 53 test sites in 14 countries on 4 continents [18]. The subsequent analysis
of the data resulted in the published standards ISO 9223-9226.
The following equations were developed to predict the corrosiveness of the atmosphere
and the extent of corrosion attack during long term exposure to the atmosphere [11],
D =
crr
1. 77P e (0.02RH -0.054(T-
(2.4)
,where
rcorrtb
10))
102S.
[19]:
62
(0.0 33 RH+0.040T)
D is the total corrosion attack (mm)
reorr is the corrosion rate in the first year of exposure (mm/year)
t is the total exposure time (years)
b is the metal-environment-specific time exponent
T is the annual average temperature (OC)
RH is the annual average relative humidity
(%)
Pd
is the annual average SO 2 deposition rate (mg/M 2 /day)
Sd
is the annual average Cl deposition rate (mg/rm 2 /day)
The two variables in equation (2.4) determined in the modeling analysis, rcorr and b,
were determined from their relationship to atmospheric conditions. While rcorr is sensitive
to changing environmental parameters and thus has an equation, b is not as sensitive to
environmental conditions so a constant value is given [11].
2.2.2
Long-Term Atmospheric Corrosion Exposure
In 1995, a group of researchers from Centro Nacional de Investigaciones Muetalurgicas (National Center of Metallurgical Research) in Spain reviewed long-term (greater than 10 years)
atmospheric corrosion data in Spain and compared the results to similar long-term studies performed worldwide. Morcillo et al. modeled their data according to an exponential
function by transforming both the exposure time and corrosion penetration logarithmically
[17]:
C = At" ,where
25
(2.5)
C is the total corrosion loss (fm)
A is the corrosion loss after one year (pm)
n is a constant
A summary of some of their location specific models are listed in Table 2.1. The locations
were chosen by environmental similarities with locations in California. Point Reyes, CA is one
of the only extensively utilized corrosion testing sites in California [14], so the comparison to
other similar sites is necessary to analyze the usefulness of these models to predict corrosion
in California.
Atmosphere
A(pm)
Location
n
Cabo Negro, Spain
52
0.86
Point Reyes, CA
96
0.98
Industrial
Bilbao, Spain
71
0.75
Rural/Urban
State College, PA
Madrid, Spain
45
0.41
45
0.23
Marine
Table 2.1: Summary of location specific atmospheric models with similar environments to
locations within California derived from long term atmospheric corrosion data as proposed
by Morcillo et al. [17].
2.2.3
Uhlig Corrosion Handbook
In the Uhlig Corrosion Handbook, several models are presented to describe how corrosion
progresses. The most general was developed from exposure tests at seven sites throughout
Japan in the 1960s using multiple linear regression. The model calculates a constant corrosion
rate as a function of the environmental parameters [5]:
CorrosionRate= 0.00464(0.484T + 0.701RH + 0.0750 + 8.202SO 2 - 0.022p - 52.67) (2.6)
CorrosionRateis in mm/year
T is annual average temperature (OC)
26
RH is annual average relative humidity
(%)
Cl is annual average airborne chlorides (ppm)
SO 2 is annual average airborne sulfur dioxide (mdd)
p is average precipitation (mm/month)
2.2.4
Comparison of Various Models
Several of the attempts by researchers and organizations to model corrosion were evaluated
in this work. Figures 2-1 through 2-3 show results from these models and illustrates that
predicting corrosion with one of these models is difficult without real data to validate it
since the model predictions are very different from one another even when environmental
parameters are similar.
COMPARISON OF MODELS FOR A MARINE ENVIRONMENT
-
MortdoPoint
eyes)
-Mo
lo(Cato
Negro)
-
UNig
--
D
0.4
Q35
0.3
S0.25
0.2
0.15
0Q05
0
0
1
3
2
T15W
4
(YEARS)
Figure 2-1: Comparison of the ISO, Morcillo et al., and Uhlig models in a marine atmosphere.
In the marine model comparison shown in Figure 2-1, the Point Reyes, CA model stands
out while the other three models group together. The Point Reyes model was included in the
comparison since it is the only site in California with the data necessary to create the model
27
[17]. While not representative of corrosion in the rest of California, it shows how rapidly
corrosion can progress in one of the most corrosive environments in the world [14].
COMPARISON OF MODELS FOR A RURAL ENVIRONMENT
-
Morcillo(Stte College)
-
Mocilo (Madrd)
UNig
-
-133
0.4
0.35
0.3
0.2S
0.2
0.
0.05
0
0
1
2
3
4
5
YEARS
Figure 2-2: Comparison of the ISO, Morcillo et al., and Uhlig models in a rural atmosphere.
In the rural/urban model comparison shown in Figure 2-2, the Uhlig model stands out
while the other three group together. As corrosion progresses, a passive oxide layer is created.
Without the presence of high airborne chloride or sulfur dioxide concentrations to attack this
layer, the metal is better protected from further corrosion [6]. The linear nature of the Uhlig
model cannot capture this affect.
In the industrial model comparison shown in Figure 2-3, all three models agree within
approximately 20% after 5 years. This comparison also shows that the combination of marine
and industrial environments represents the most corrosive environment.
The ISO model was chosen to predict the corrosion throughout PG&E's service area
because it matches closest with regional corrosion data within California. Further discussion
on the choice of using this model is in Chapter 3.
28
COMPARISON OF MODELS FOR INDUSTRIAL/MARINE ENVIRONMENT
-
Morcilo (Bilbao, mVyr)
-U
nig(nm/year)
-ISO
(rn/year)
0.4
0.35
0.3
025
0.2
p0,15
al
015
0
0
1
2
3
4
5
YEARS
Figure 2-3: Comparison of the ISO, Morcillo et al., and Uhlig models in a combined industrial
and marine environment.
29
Chapter 3
Corrosion Risk Model
3.1
Overview
In this chapter, the data used for the corrosion prediction model is discussed. The data was
gathered from both public sources and internal PG&E databases. With the data, the qualitative observations of the atmospheric corrosion inspections with a quantitative corrosion
model developed by the International Organization for Standardization (ISO) are coupled to
simulate the corrosion that occurs on gas customer meter sets throughout PG&E's service
area. A multiple logistic regression model technique is also explored. Both the simulation
model and the multiple logistic regression were created using the software R.
In developing this model, two major assumptions are made:
* There is no restoration work done between atmospheric corrosion inspections.
e There is no federal requirement mandating 36 month inspections.
The first assumption makes modeling atmospheric corrosion easier since we discount any
repairs made to the meters outside of the atmospheric corrosion inspection interval.
In
reality, PG&E personnel visit between two and five percent of their gas customer meter sets
per year for any number of reasons, e.g., establishing service, customer call, or disconnecting
service. If the customer meter set is in a state of corrosion, it is sanded and then painted
with a fresh coat of a corrosion inhibiting paint.
30
The second assumption removes the constraint imposed on the atmospheric corrosion
inspection program which allows us to consider the effect of corrosion beyond the time
requirement of the federally mandated inspections. Extending the inspection interval beyond
36 months is not currently an option for PG&E, but evaluating corrosion beyond three years
could give greater insights into the corrosion effects of different geographic areas within
PG&E's service area.
3.2
Data selection
In order to construct the model, a data set was created by utilizing PG&E customer meter
asset information, historical leaks caused by atmospheric corrosion, qualitative observations
from atmospheric corrosion inspections, and environmental data. Since atmospheric corrosion depends on environmental factors and asset construction, understanding these parameters are vital in order to predict where future failures may occur.
3.2.1
Pacific Gas and Electric Customer Meter Asset Data
In order to predict corrosion on gas customer meters, there is a need to understand the
construction of the asset. A typical gas customer meter set is shown in Figure 3-1. The part
of the system that is subject to atmospheric corrosion is from the soil to air transition of the
riser to where the gas pipe enters the house. This is divided into two regions, with the service
valve serving as the dividing line. Different groups within PG&E own the two regions. The
pipes, valves, and fittings are all low carbon steel, while the meter has an aluminum housing.
The steel pipes are typically 40 gauge pipes with a nominal wall thickness of 2.79 mm (0.11
inches). Most of the new installations have plastic risers, but those risers not taken into
account in this thesis since steel pipes are utilized downstream of the service valve.
PG&E has approximately 4.3 million gas customer meters that are inspected at least
every 36 months for an atmospheric corrosion inspection and every 60 months for a leak
survey. Additionally, service personnel visit approximately 5% of the meters each year for
other reasons. Each meter has a series of approximately 40 characteristics, such as meter
type, model, flow rate, and age.
31
Figure 3-1: Construction of the customer meter. Two grades are given in the inspection
process, one for the riser (including the service valve), and one for everything downstream
of the service valve.
32
3.2.2
Pacific Gas and Electric Inspection and Leak Data
PG&E performs atmospheric corrosion inspections on approximately one third of their customer meter sets annually. Prior to 2014, the inspection results only consisted of a "yes" or
"no" on whether there was corrosion present on each meter set, and the results are stored
on paper maps located in each of the 17 district offices. In 2014, atmospheric corrosion
inspections were completed on 2.3 million customer meter sets with a new procedure seeking to capture more data about corrosion. The inspection results have more detail with a
"no"
,
"low", and "high" corrosion grades, and all of the inspection results are maintained
electronically.
The grading scheme appears to have some ambiguity based on an Attribute Gauge R & R
analysis. This ambiguity may leave the inspector to make a judgment call in some instances,
which leads to subjectivity in the data set. More details are discussed in the Attribute Gauge
R & R section.
These inspection results are the qualitative observations that are coupled with existing
research and quantitative understanding of the corrosion process in order to create the backbone of the prediction model. The distribution of corrosion grades for the 12 divisions in
which atmospheric corrosion inspections were completed is shown in Figure 3-2.
Gas system leak data is generated from several activities. Leaks are discovered by leak
surveys (federally mandated 60 month inspections), customer calls, atmospheric corrosion
inspections, or by an inspector at the customer meter for another reason. The data on leaks
caused by atmospheric corrosion is used to calibrate and validate the prediction model.
3.2.3
Meteorological Data: Temperature and Relative Humidity
Temperature and relative humidity are the meteorological parameters that have the greatest
impact on atmospheric corrosion. The California Air Resources Board (CA ARB) manages
523 weather monitoring stations within PG&E's service area. They are distributed around
the state as shown in Figure 3-3. Each weather station has at least 10 years of historical
hourly data [20].
33
%
1-0
90%
70%
6o%
50%
40%
30%
20%
10%
0%
OE
ANZA
04AS.0
EAST BAY
FRSNIO
fAh
N
NORM COAST
PENINSULA
SACPAWMTO
SAN JOSE
ERRA
STOCKTON
YCSE
E
Figure 3-2: Distribution of corrosion grades in divisions that were inspected for atmospheric
corrosion in 2014.
3.2.4
Pollution Data: Chlorides and Sulfur Dioxide
Airborne Chlorides
Chlorides influence corrosion more than any other ion. There are two methods for collecting
atmospheric chloride measurements: wet and dry deposition. For our model, we are concerned with the dry deposition measurements. Dry deposition collects aerosol particles and
then dissolves the particles in a known volume of deionized water to determine their composition [21]. The most comprehensive publicly available databases for atmospheric chloride
concentration are the IMPROVE databases run by Colorado State University in conjunction
with the National Park Service [22] and the National Atmospheric Deposition Program run
by the University of Illinois [23]. These database contains chloride data from the 13 air
quality monitors within PG&E's service area, shown in Figure 3-4, that monitor airborne
chloride composition.
34
Figure 3-3: Weather stations in California that are managed by the CA ARB.
Airborne Sulfur Dioxide
Sulfur dioxide also accelerates atmospheric corrosion.
It comes primarily from the com-
bustion of sulfur containing petroleum based fuels. Airborne sulfur dioxide levels within
California are also collected via the dry deposition method. The California Air Resources
Board monitors sulfur dioxide within PG&E's service area from 14 different locations which
are shown in Figure 3-4, most of which are around the bay area's 5 oil refineries [20].
3.3
3.3.1
Model Development
Mapping Environmental Parameters to Customer Meters
For this analysis, each of the 2.3 million meters in the prediction model takes on the meteorological parameters of the nearest weather station in order to map temperature and relative
humidity over the gas distribution network.
In order to map chlorides to each of the meters, interpolation of the values consistent
with the data obtained from the IMPROVE database was performed. The contour map in
Figure 3-5 shows the approximate values of airborne chlorides across the state of California.
California has very few major sources of atmospheric sulfur dioxide. The bay area has five
35
Figure 3-4: Map of air monitoring sites that analyze for chlorides (white) and sulfur dioxide
(black).
oil refineries that produce levels high enough to be of concern. To map sulfur dioxide levels
to each of the meter, each meter was assigned the value of the nearest measuring station.
If the distance to the nearest measuring station was greater than 30 miles, the meter was
assigned the minimum measured value to be conservative. The CA ARB has concluded that
areas without monitors do not have significant levels of sulfur dioxide since there are no
facilities that emit large quantities of the pollutant
3.3.2
[24].
Assigning Divisions to Corrosion Environments
Three environment classifications that are used when analyzing atmospheric corrosion were
previously discussed: rural/urban, marine, and industrial.
Each division within PG&E's
service area was assessed based on average annual values for the environmental factors that
are considered and similar divisions were grouped into each of the following classifications:
Rural/Urban, Marine, and Marine/Industrial. Table 3.1 summarizes the environmental classification of each division. All divisions with high sulfur dioxide measurements, indicating
an industrial environment, also had high chloride measurements, so they were grouped into
a marine/industrial classification.
36
Cr
(mg/L)
2!1.0
0.8
0.6
0.4
0.2
0
Figure 3-5: Concentration of airborne chlorides in California in 2012 [23].
3.3.3
Base Corrosion Model Validation
The qualitative observations obtained through the atmospheric corrosion process cannot
define the mechanism of corrosion, so a corrosion model is needed to fill that gap.
In
Chapter 2, We described three models that have been used to describe how atmospheric
corrosion progresses; models proposed by Moticillo et al., by Uhlig, and by the ISO. Most
of the models that have been developed are only useful where the research was performed
and the data was collected. The ISO model, however, has been proposed to be a general
standard for corrosion worldwide.
After the above mentioned models were explored, the model developed by the International Organization for Standardization (ISO) was chosen to describe how corrosion progresses within California. This model was chosen because it most accurately follows the
limited research data that exists within PG&E's service area. The ISO model has also been
shown to accurately predict corrosion in low carbon steels [25].
PG&E performed a corrosion study in the 1950s and 1960s, mainly focusing on the coast
and bay area since more extreme corrosion occurs there. They also chose a single site inland
in a rural atmosphere. Over the 5 years of the study, they made annual measurements of the
37
Atmosphere
Division
Marine
Diablo
North Coast
San Jose
Marine/Industrial
East Bay
Mission
Peninsula
Rural/Urban
De Anza
Fresno
Sacramento
Sierra
Stockton
Yosemite
Table 3.1: Divisions classified by corrosive atmosphere.
total corrosion attack at each location [26]. Using this information, we are able to compare
what the ISO model predicts to the actual corrosion attack.
Figure 3-6 shows the comparison between the ISO model and the results from the PG&E
study in a graphical form for rural, marine/industrial, and marine environments. Since the
ISO model is a general model designed to predict corrosivity worldwide, it over predicts the
corrosion in the lower corrosive rural environment, and under predicts in extreme corrosion
environments. While the predictions are more conservative than the actual corrosion attack
in Davis and Richmond, the prediction in Martinez is within 1% of actual corrosion attack
after 5 years. Erring on the side of caution is desired so as to not underestimate the effect
of corrosion.
Even though it is more conservative in rural settings, The ISO model accurately predicts
total corrosion attack when compared to experimental data within PG&E's service area. For
this reason the ISO model is used to model corrosion progress within PG&E's service area
for the corrosion prediction simulation model.
38
Comparison of the ISO Model to Measured Corrosion Rates in Various
Atmospheres
0.35
0.3
0.25
0.2
.2
0
0.15
0.1
0.05
0
0
1
3
2
5
4
6
Time (years)
-h-ISO (RuraJ
-+-
Measured (RuraQl
-*- ISO (Marine/Industrial)
--
Measured (Marine/Industrial)
-Ar- ISO (Marine)
---
Measured (Marine)
Figure 3-6: Plots comparing ISO predicted corrosion attack to the actual corrosion attack
in Davis (rural), Martinez (marine/industrial), and Richmond (marine).
3.3.4
Attribute Gauge Repeatability and Reproducibility Study
Corrosion physics is well understood, and with that understanding, a correlation between
failure rate and the environmental parameters within a division was developed. However, a
correlation between the atmospheric corrosion inspection grading and environmental factors
was not present as would be expected. To address this, an Attribute Gauge Repeatability
and Reproducibility study was performed to investigate possible variability in the inspection
data. Attribute Gauge R & R studies are used in process improvement applications where
the evaluated measurement is subjective. Since this study was done as an appendage of this
project to investigate the data, the discussion will briefly touch on the process and focus
on the results of the study. The analysis of the study was performed in Minitab using the
Attribute Gauge R & R tool.
39
Study Design
This study was designed in with assistance from one of PG&E's process improvement experts. As we designed the study, a way to test the atmospheric corrosion inspection process
(measurement system) for repeatability could not be found; sending the inspector repeatedly
to the same house multiple times to inspect the same meter would not provide good data.
With this in mind, the study was designed only to measure reproducibility.
A subset of meters in four cities representing the environmental variability of California
were identified. Each sample size gave a 95% confidence level that a representative sample
of the entire population was used.
Expert inspectors were chosen, then trained on the
inspection process and grading system. The inspectors reinspected the previously identified
meters. Their reinspection was to be the standard to which the previous inspections were
compared.
Study Results and Discussion
The results of the inspections were analyzed by comparing the expert inspections (or standard) with the atmospheric corrosion inspections performed in 2014. It was discovered that
the inspectors agreed with the standard in only 63% of the inspections. The Kappa statistic,
a measurement of agreement between raters taking into account agreement by chance, is 0.17
on a scale of -1 (agreement is completely random) to 1 (agreement is real). These results
suggest that there is poor agreement between the inspector and the standard (or expert) and
that there may be room in the atmospheric corrosion inspection procedure for subjectivity.
While these results suggest that there is an opportunity to improve the data through
training and procedural improvements, it does not invalidate the data. Corrosion is happening. However there is a lack of clarity into what serious corrosion is and what is not. With
this understanding of the data, it is easier to interpret the current atmospheric corrosion
data to create a model that couples the qualitative observations with an established corrosion model. With these results, the procedure was analyzed as a source of the subjectivity
in the data.
Table 3.2 shows a summary of the corrosion grading criteria from the 2014 atmospheric
40
corrosion inspection procedure. By comparing the written grading criteria between low and
high corrosion, it is apparent that there is ambiguity. The first example of ambiguity is "flaking rust is present but not dominant (low corrosion descriptor)" and "metal surfaces under
general attack but metal loss is not advanced (high corrosion descriptor)." These descriptors sound very similar when discussing general corrosion, so general corrosion that matches
both could be graded as either "low" or "high". The second example is "no appreciable pitting or wall loss (low corrosion descriptor)" and "superficial localized pitting (high corrosion
descriptor)." These descriptors are similar when determining minor localized corrosion, so
localized corrosion that matches both descriptors could be graded as either category. These
examples show that the inspectors may have to make a judgment call in corrosion grades
for some of the customer meters. This ambiguity suggests that the atmospheric corrosion
inspection procedure and process may need to be revised to remove the subjectivity from
the data.
Grade
None
Description
No observable rust.
Painted surfaces are smooth and unbroken with glossy finish.
Flaking rust is present but not dominant.
Low
Paint is compromised and flaking.
No appreciable pitting or wall loss.
Metal surfaces under general attack but metal loss is not advanced.
Superficial localized pitting.
High
Active corrosion present and advanced.
Metal surfaces are pitted and gouged.
Painted surfaces are completely compromised.
Table 3.2: Summary of PG&E corrosion grading criteria for the 2014 atmospheric corrosion
inspections.
41
3.3.5
Creating an Expected Data Set
Based on the Attribute Gauge R & R study, it was shown that the atmospheric corrosion
inspection data may be subjective and that the development of an expected data set is
necessary to accurately simulate the start of corrosion. No correlation was found between
the number of high corrosion events in a division and the average environmental parameters
as would be expected. Several divisions are not consistent with the rest of the data, however,
when comparing the trend in high corrosion events-versus several of the average atmospheric
parameters. The average environmental parameters for each division are shown in Table 3.3.
Three of the 12 data points were removed from the subsequent regression analysis because:
" Diablo (marine environment): fewer than 1% meters in this division were reported
to have high corrosion while divisions with similar chloride levels had 5 to 10% of
inspected meters have high corrosion. Also, fewer than 20% of the total meters have
corrosion reported on them.
" East Bay (marine/industrial environment): approximately 20% of the meters inspected
were reported to have high corrosion while divisions with similar chloride and relative
humidity levels all were reported to have less than 10%.
" San Jose (marine environment): fewer than 1% of meters were reported to have high
corrosion, far fewer than any other division, while having the second highest chloride
level. Also, fewer than 20% of the total meters have reported corrosion.
The remaining data points show that the number of reported high corrosion events trend
upwards as chlorides, relative humidity, and sulfur dioxide increase, as we would expect from
the review of corrosion research covered in Chapter 2. With the three previously mentioned
data points removed, the remaining data was transformed by using a power transformation
model to increase linearity:
in(Y) = #o + f1ln(X)
42
(3.1)
Relative Humidity (%)
Temperature (0 C)
Cl (mg/m 2 d)
S02 (mg/m 2d)
Diablo
71.1
12.7
16.6
13.7
North Coast
74.2
15.1
15.2
21.7
San Jose
72.2
13.0
19.1
19.7
East Bay
70.8
14.3
15.2
54.2
Mission
72.3
13.8
18.2
34.7
Peninsula
75.0
12.0
31.9
72.8
De Anza
69.8
15.2
9.7
18.6
Fresno
64.5
15.0
3.0
23.8
Sacramento
62.6
16.1
4.9
15.2
Sierra
63.0
15.9
3.0
0.9
Stockton
59.0
17.9
3.0
18.8
Yosemite
59.8
16.0
3.0
1.3
Division
Table 3.3: Average environmental parameters for each division.
It was discovered that the expected high corrosion events can be described as a function of
relative humidity, airborne chloride deposition, and the interaction between the two:
NHC,i = Goe(01 ln(Sd,i) +021n( RHj )1n(Sd,W)
NHC
(3.2)
is the expected number of high corrosion events in division i
G, is the regression model intercept term eo
RH is the annual average relative humidity in division i
Sd
(%)
is the annual average Cl deposition in division i (mg/m2 /day)
This linear regression results in the highest R2 value while maintaining the p-values of
the coefficients sufficiently low to be significant. A low p-value indicates that the change
in the parameters will have a significant effect on the output. Table 3.4 shows the p-values
for the coefficients; the interaction between the chlorides and relative humidity is shown to
be borderline significant, however including this interaction increases both the R2 and the
accuracy of the model. The linear regression has an R2 of 0.7392.
43
Interaction
Coefficient
P (>t)
G,
Intercept
-0.115
0.0056
01
Cl
-13.010
0.0093
/2
C1 and RH
3.166
0.0768
Table 3.4: Linear regression parameters.
This regression removes some of the subjectivity from the data resulting from the inspection and smooths out the expected high corrosion events across the rest of the divisions.
The regression predicts the number of high corrosion events in the nine divisions used in the
regression analysis with only a 19% error. As expected, the regression corrects the number
of high corrosion events the three divisions removed by increasing the high corrosion events
in Diablo by 648%, increasing the high corrosion events in San Jose by 1133%, and decreasing the high corrosion events in East Bay by 46% The data from this regression analysis is
closer to what could be expected as some of the subjectivity in the inspection procedure is
removed.
3.4
Corrosion Prediction Model Structure
The corrosion prediction model simulates atmospheric corrosion for each meter within PG&E's
system individually, then aggregates the results to predict the number of failures in each division. The results are a simulation that may be used to estimate where and when failures
could occur.
3.4.1
Predicting Failures - The Complete Corrosion Prediction
Model
A prediction model was developed by coupling the qualitative observations of the atmospheric
corrosion inspections with the ISO general corrosion model in order to simulate the total
corrosion penetration on each customer meter set. As discussed in chapter 2, the ISO general
corrosion model is defined as [11], [19]:
44
D = rcorrt
rr
1. 7 7 P52
, where
e(O.02RH-0.054(T 10))
+ 0.
0 O.62 e(0. 033RH+0.040T)
D is the total corrosion attack (mm)
Tcorr
is the corrosion rate in the first year of exposure (mm/year)
t is the total exposure time (years)
b is the metal-environment-specific time exponent
T is the annual average temperature (OC)
RH is the annual average relative humidity
(%)
Pd
is the annual average SO 2 deposition (mg/m2 /day)
Sd
is the annual average Cl deposition (mg/M 2 /day)
The total corrosion for each meter is calculated by the following equation:
D= Cstartrcorr(tintervai - time)b A
= Cstartrcorr(tintervai - time)b
Cstart
(if H = 1)
(if
H
= 0)
(3.3)
is the discrete random variable simulating the start of corrosion (values 1 or 0)
tinterval
is the time of the atmospheric corrosion inspection interval (years)
time is the year in which corrosion was simulated to start (years)
A is the localized corrosion acceleration factor
H is the discrete random variable simulating if localized corrosion occurs
The simulated meter will fail if D is greater than the wall thickness of the low carbon
steel pipes. Each parameter will be discussed in the following sections.
3.4.2
Simulating the Start of Corrosion (Cstart)
All customer meter sets are installed with a corrosion inhibiting coating, so corrosion does
not start immediately after installation. The start of corrosion is simulated as a binomial
45
approximation by first calculating the probability of corrosion starting each year. The model
then simulates each meter set over a range of eight years to simulate when corrosion starts.
Calculating the Probability that Corrosion Starts
The probability of corrosion starting for each meter set each year was estimated as a binomial
approximation:
E(X)
=
np
(3.4)
E is the expected number of events (or number of high corrosion events, NHC,i)
n is the total number of events (or the total number of meters, TotalMetersj)
p is the probability of a successful result (or the probability of corrosion starting on a
customer meter set, p(start)j)
This approximation is used since each customer meter set has started corroding or it
hasn't. The following assumptions are made for determining E(X) from our existing data:
" The number of corrosion starts in a division each year are equal to the expected high
corrosion events in that division each year.
" Expected high corrosion events in a division each year are only dependent on environmental conditions.
The first assumption allows the number of corrosion starts in any given year to be estimated based on the available 2014 atmospheric corrosion inspection data.
The second assumption is necessary since average environmental data is the only data
that is available on a division basis. The age of a meter would also play a role in the corrosion
start since corrosion inhibiting coatings deteriorate over time, but the average age of meters
in each division is about equal. Therefore, at a division level, the average environmental
parameters are the only distinguishing features.
After determining the expected number of high corrosion events in each of the divisions,
E(X) (NHc,i), from our expected data set, the probability that corrosion starts each year in
46
each of the divisions is calculated by rearranging the binomial distribution equation:
p(Start)i -
(3.5)
NHC,i
TotalMetersi
Simulating Corrosion Start Time
It is important to know not only that corrosion starts, but when corrosion starts in order
to build a time component into the model. The reference point (time = 0) is defined as
being at 2011. This aligns the three year point in the model with the atmospheric corrosion
inspections performed in 2014. Each customer meter's start time is simulated over a range
of 8 years from time = -2 to time = 6. This allows some meters to start corrosion before
the previous inspection in 2011 since the corrosion would have been mild and not necessarily
repaired after the 2011 inspection based on PG&E's current remediation guidelines.
The start of corrosion is simulated by assigning discrete variables through utilizing a
random number generator. We first generate a random number in the software R with
the runif command. This generates a pseudo random number from a uniform distribution
between the numbers of 0 and 1. If the number is less than the p(Start) defined in equation
3.5, then corrosion starts:
if rand < p(Start)
1
CStart =
else
Cstart
0
Where CStart indicates the start of corrosion if equal to 1.
This simulation is repeated from time
=
-2
until
CStart
is 1 or time = 6.
If CStart
remains equal to 0, it indicates that no corrosion starts on that meter over the eight year
period.
47
3.4.3
Localized Corrosion Acceleration Factors (A and H)
After corrosion starts, it can progress as general corrosion or localized corrosion. Predicting
localized corrosion falls outside of the scope of this project, and localized corrosion is very
difficult to model and predict [5]. For ease of calculations, the following assumptions are
made for the localized corrosion factor:
e Localized corrosion progresses at the same rate regardless of environmental factors
e Incidents of localized corrosion happen across all divisions at the same rate regardless
of environmental factors
These assumptions are very rough, but the data gives no insight into how localized
corrosion affects the meters in each division. With these assumptions, a localized incident
probability of 0.5 is used, or localized corrosion occurred on 50% of the meters on which
corrosion started. The random discrete variable H is used to identify the meters on which
localized corrosion occurs.
Localized corrosion progresses faster than general corrosion, so acceleration of the corrosion process needs to be addressed. This factor, A, will be used to calibrate the model by
targeting the current failure rates within the divisions.
3.4.4
Calibrating the Model
In order to calibrate the model, the localized corrosion acceleration factor A was varied,
training the model on a subset of 20,000 meters from each division for a total of 240,000
meters. Once the localized corrosion acceleration factor that best sorts the training data
was determined, the model is exercised to simulate each of the 2.3 million customer meters.
This results in a simulation of the overall state of corrosion throughout PG&E's service area.
The number of simulated failures in division i is the sum of the number of meters in which
D is greater than the wall thickness of the low carbon steel pipes.
A study to calculate the pitting corrosion acceleration factor was performed in Spain.
Groups of samples were exposed to atmospheric corrosion from 1.6 to 10 years and the
ratio between the max pit depth and the average corrosion within a group was then used to
48
determine the acceleration of corrosion due to pitting. The acceleration factors in the study
ranged from 2.84 to 7.99 [7].
The localized corrosion acceleration factor A that calibrated the model was 8.4. This
seems to be a reasonable value as it is consistent with the scope of the the experimental
range determined by the above mentioned study.
3.5
Model Results
The corrosion prediction model out of sample results are given in Figure 3-8. It should be
mentioned that the failure rate in many of these divisions can be significantly affected by
only a couple of failures since there are relatively few failed meters.
From the results, three divisions stand out: De Anza, North Coast, and Stockton. Two
of the three, De Anza and Stockton, can be explained with analysis of the failure data. De
Anza had far fewer failures than would be expected based on environmental parameters, and
Stockton had far more failures than would be expected based on the environmental data.
The actual failure rate numbers and mean absolute percent error (MAPE) are given in
Table 3.5, and a visualization of the MAPE is presented in Figure ??. For the total error, a
weighted MAPE was used since each division has a different number of meters. Overall, the
model has poor results with a WMAPE of 51%. If De Anza and Stockton are removed from
the results, however, we achieve a more reasonable WMAPE of 37%. The model is adequate
in predicting the failure rate in 10 of the 12 divisions.
3.6
Logistic Regression
Another method that was investigated uses logistic regression. The output variable for the
model is binary; the customer meter set either fails (Y = 1) or it doesn't (Y = 0). The
logistic, or logit, model is defined as the probability of Y given X, where X is a matrix of
explanatory variables.
3
e(1 o+11i)
1 + e(3o+1xi)
P(Y= 1=X)
49
(3.6)
N Actual
0 Predicted Failure Rate
Failure Rate
0.200%
0.150%
66 0.100%
0.050%
0.000%
Division
Figure 3-7: Prediction model results compared to current failure rate.
3.6.1
Modeling with the Original Data Set
In order to find the best model, a full (saturated) model was created with all of the available
explanatory variables listed in Table 3.6. The age of the meter refers to the time that the meter has been in service at the installed site, the corrosion zone is a qualitative determination
of high or medium corrosion zones along the coast based on electrical system maintenance
records, and corrosivity is the amount of corrosion as predicted by the ISO model.
The original data set was divided into two to create a training data set (10% of the data)
and a testing data set (90% of the data). The model was created with the training set. To
determine the best model, the least significant variables were removed one at a time. The
best attempt of the model is shown in Table 3.7, along with the standard error, odds ratio
(OR), and p-value for each coefficient. The odds ratio represents the odds that a failure will
occur with an increase in the independent variable compared to the odds that a failure will
occur without an increase in the independent variable.
50
Divisio n
Environment
De Anz a
Actu al Failure Rate
Predicted Failure Rate
MAPE
Rural/Urban
0.012%
0.033%
174%
Diablo
Marine/Industrial
0.023%
0.019%
-19%
East Bc y
Marine/Industrial
0.091%
0.103%
13%
Fresno
Rural/Urban
0.017%
0.008%
-54%
Mission
Marine
0.027%
0.020%
-25%
North C oast
Marine
0.119%
0.196%
64%
Peninst la
Marine/Industrial
0.047%
0.041%
-13%
Sacramento
Rural/Urban
0.006%
0.005%
-10%
San Jose
Marine
0.019%
0.019%
54%
Sierra
Rural/Urban
0.006%
0.004%
-40%
Stockto nt
Rural/Urban
0.041%
0.005%
-88%
Yosemi te
Rural/Urban
0.007%
0.01%
56%
Totals
0.035%
0.038%
51%
Table 3.5: Prediction model results with mean absolute percent error.
Potential Explanatory Variables
Age of Meter
Chlorides
Inspection Grade
Elevation
Relative Humidity
Corrosion Zone
Temperature
Corrosivity
Sulfur Dioxide
Table 3.6: Available explanatory variables for logistic model.
3.6.2
Modeling with the Expected Data Set
In order to investigate whether the logistic model could be improved, the process of creating
the model was repeated with the expected data set constructed with equation (3.2). The
model structure for the second logistic model attempt is shown in Figure 3.8. The changes
in the data for this model include the expected grade instead of the inspection grade and a
log transformation of the meter age.
51
2 under -49
EJ -49--30
o -30--19
* -19--12
E -12-5
* 5-54
54-59
* 59-64
* over 64
Figure 3-8: Geographic visualization of MAPE by division.
Term
#
Estimate
Standard Error
OR
p-value
-15.64
1.13
-
<.001
Relative Humidity
.737
.186
2.09
.006
Inspection Grade
.110
.086
1.11
<.001
Corrosivity
.873
.029
1.09
.022
Age of Meter
-. 161
.0028
.85
<.001
Intercept
Table 3.7: Logistic model formulation with original data.
3.6.3
Logistic Model Results
Both logistic regression models results are compared to the actual data in Table 3.9. In the
first model attempt, an accurate model of the system failure rate could not be determined
because the inspection data was so subjective. Pairing the qualitative data with the corrosion
model gave extremely inaccurate results. The results show the model predicting the entire
original data set. The model estimated zero failures in six of the divisions, severely over-
or under-estimated the failure rate in three of the divisions, and reasonably predicted the
failure rate (with less than 50% error) in the remaining three divisions.
Dramatic improvement is shown in the second logistic regression model as shown in
Table 3.9. The same trend is encountered with De Anza and Stockton as with the corrosion
prediction model because of their abnormal failure rate when compared to environmental
parameters. The remaining 10 predictions all have a reasonable MAPE.
52
#
Term
Estimate
Standard Error
OR
p-value
Intercept
-2.71
.396
-
.095
Expected Corrosion Grade
.299
.337
1.35
.197
Corrosivity
.121
.0023
1.13
<.001
Chlorides
-.0465
.012
.95
<.001
log(Age of Meter)
.0292
.095
1.03
.324
Table 3.8: Logistic model formulation with expected data.
The model based on the expected data has far fewer zero predictions than the model
based on the original data. To compare the total error, weighted mean absolute error is used
since each division has a different number of meters. An improvement of the total weighted
mean absolute percent error (WMAPE) from 128% to 60% is achieved. Also, if De Anza
and Stockton are removed as was done with the previous model, the error decreases even
more to about 44%.
District
Actual
Original Data
MAPE
Expected Data
MAPE
De Anza
0.012%
0%
100%
0.059%
387%
Diablo
0.023%
0%
100%
0.023%
2%
East Bay
0.091%
0.626%
588%
0.136%
49%
Fresno
0.017%
0%
100%
0.008%
53%
Mission
0.027%
0.014%
50%
0.013%
54%
NorthCoast
0.119%
0.142%
20%
0.093%
22%
Peninsula
0.047%
0.117%
149%
0.069%
48%
Sacramento
0.006%
0.001%
91%
0.009%
9%
San Jose
0.019%
0%
100%
0.008%
48%
Sierra
0.006%
0.008%
37%
0.007%
22%
Stockton
0.041%
0%
100%
0.003%
94%
Yosemite
0.007%
0%
100%
0%
100%
0.035%
0.043%
128%
0.033%
60%
Totals
Table 3.9: Comparison of logistic regression model results between actual data and expected
data.
53
3.7
Conclusion
By developing the corrosion prediction model, the atmospheric corrosion inspection data
was identified to be somewhat subjective. In order to remove some uncertainty, an expected
data set was constructed through a multiple linear regression analysis after removing some
of the data points that were inconsistent with the other high corrosion data. This analysis
allowed for the creation of a data set that is more in line with what we would expect with
corrosion on the meters.
A corrosion prediction model has been developed by coupling the qualitative observations
of the atmospheric corrosion inspections to a qualitative model developed by the ISO. This
model gives us a better understanding of how corrosion progresses within all of PG&E's service area, not just where the corrosion experiments in research and experimental literature
have taken place. This model could allow PG&E to predict the number of failures in each
division for a given time between inspections. Removing the subjectivity from the atmospheric corrosion inspection data through process improvements will be essential to further
refine this model.
The use of a logistic model was also investigated. Removing some of the subjectivity in
the data set led to marked improvements in the quality of a logistic model. Removing more
subjectivity from the atmospheric corrosion inspection data through process improvements
may yield further gains in predicting the probability of failure through the use of logistic
regression.
The corrosion prediction model as presented gives a more accurate picture of the corrosion
on PG&E's gas system than the logistic model, though it is significantly more complicated.
With further refinement of the atmospheric corrosion inspection data, the logistic regression
may be a better option for the model because of it's simplicity.
54
Chapter 4
Optimizing Inspection Interval
4.1
Overview
Currently, PG&E inspects each customer meter set every 36 months as required by federal
regulation. The drawback of this blanket inspection interval is that it manages all of the
customer meter sets as if they are all at equal risk when it comes to atmospheric corrosion.
In reality, there is a wide range in the corrosivity of atmospheres throughout PG&E's service
area which should result in less risk in the more benign areas.
The model developed in Chapter 3 predicts the failure rate within each division based
on environmental parameters coupled with qualitative observations during atmospheric corrosion inspections. The purpose of the optimization model is to determine if there is a more
optimal inspection frequency in order to continue to minimize the frequency of leaks caused
by atmospheric corrosion but, at the same time, allow extension of the inspection interval
where warranted.
4.1.1
Prediction Model Results for Various Inspection Intervals
The corrosion prediction model was validated with the data from the inspection interval of
three years. The model cannot be validated for data outside the three years since the data
set only has the single year of atmospheric corrosion inspections. However, the prediction
model was based on the quantitative ISO model, so it can be assumed that the results outside
55
of the three year snapshot also have merit.
The results of the prediction model show that the failure rate increases slightly more
than linearly as time between inspections increases. However, with the time frame that we
are considering, year = 0 through 6, the failure rate can be estimated to be linear over time.
For the formulation of the optimization model, we assume that the failure rate for a division,
Fi, is proportional to the inspection time interval,
4.2
tintervali.
Optimization Model Development
The inspection frequency can be optimized for each of the divisions. Two different variations
of the optimization model are considered; first, the inspection frequency is optimized by
minimizing cost to investigate the wasted resources by adhering to the current requirement
of inspecting the gas customer meters every 36 months. Then the inspection frequency is
optimized by minimizing the probability of failure. As discussed in Chapter 1, we are only
analyzing the frequency aspect of managing a risk, not a complete risk management analysis.
For this analysis, the probability of failure, or failure rate, is defined as:
F~l~t
V'~
-Failures(meters/year)
Failure Rate (%/year ) =Falrs(ers/a)
TotalMeters(meters)
4.2.1
Optimizing Inspection Interval by Minimizing Cost
The inspection frequency is first optimized by minimizing cost. This model investigates the
waste that is generated by the current policy of inspecting each meter every 36 months.
With this model, the current failure rate is assumed to be acceptable since it is the failure
rate obtained by complying with the current federal regulation. The optimization model is
developed with the following information:
Decision variables:
tinterval,i
is the inspection interval for division i.
Data: Cm is the cost of inspection on a per meter value, Nmi is the number of meters in
division i, and
Rmax
is the maximum current risk of failure in a division.
56
Other Variables: F is the number of failures in division i, B is the annual inspection
budget, and Ri is the probability of failure in division i.
We define total probability of failure (Rtotai) and annual cost per division (Ci) as:
Rotai =
Ei Fmi
where
Fi O( tinterval,i
(4.1)
(4.2)
Ci = CmNmi
tinterval,i
The objective of model A is to
minimi ze
Ci
subject to the following constraints:
(4.3)
Rtotai
Rcurrent
tinterval,i
< 6
Vi
(4.4)
tintervali
;> 0
Vi
(4.5)
Vi
(4.6)
<< Rmax
Nm,i
Constraint (4.3) ensures that the total risk of the inspection program does not exceed
the risk of the current process. Constrains (4.4) and (4.5) ensure that the inspection interval
falls within the bounds of the corrosion prediction model. Recall that corrosion on each
meter is simulated to start up to tinterval equal to 6 as an upper bound on the simulation
model. Constraint (4.6) ensures that the risk of failure in any division does not exceed the
maximum risk of failure in the current process.
57
4.2.2
Optimizing Inspection Interval by Minimizing Risk
Optimizing by Division
To investigate the best way to minimize system risk, a model was developed that determines
the optimal inspection frequency for each division in PG&E's service area. This model will
give PG&E the ability to understand the impact of changing the customer meter set inspection frequency on system safety and was developed with the following information:
Decisions: tinterval,i is the inspection interval for division i.
Data: C
is the cost of inspection on a per meter value, Nm,i is the number of meters in
division i, and Rmax is the maximum current risk of failure in a division.
Other Variables: Fi is the number of failures in division i, B is the annual inspection
budget, and Ri is the probability of failure in division i.
The objective of model B is to
minimize
Rotal
subject to the following constraints:
ZCi
B
(4.7)
i
6
Vi
(4.8)
tinterval,i ;> 0
Vi
(4.9)
F.
Ni < Rmax
Nm,z
Vi
(4.10)
tinterval,i <
Constraint (4.7) ensures that the total cost of the inspection program does not exceed
the budget. Constrains (4.8) and (4.9) ensure that the inspection interval falls within the
bounds of the corrosion prediction model. Constraint (4.10) ensures that the risk of failure
in any division does not exceed the maximum risk of failure in the current process.
58
Optimizing by Environment
The formulation for this optimization model is similar to the previous model so it is not
presented here. The only difference is the grouping of meters; instead of being grouped
by division i, the inspection interval is optimized by grouping the meters by the type of
corrosion environment j. Recall that each division was assigned a corrosive environment in
Chapter 3.
4.3
Optimizing Inspection Interval Results
The optimization results for minimizing the annual cost is shown in Table 4.1. If the same
failure rate as with the current data is maintained, optimizing the inspection frequency
shows significant cost savings. This savings, 35% of the current budget, can be seen as
wasted resources by the current requirement of inspecting each meter every 36 months.
Division
Inspection Frequency
De Anza
4
Diablo
5
East Bay
3
Fresno
6
Mission
6
North Coast
2
Peninsula
4
Sacramento
6
San Jose
6
Sierra
6
Stockton
6
Yosemite
6
65%
% of current budget
Table 4.1: Inspection frequency optimized by division and comparing various budget levels.
The optimization results for minimizing the failure rate is shown in Table 4.2.
The
optimization results are compared to the current process across various cost levels. The two
outliers, De Anza and Stockton, were discussed in Chapter 3 while presenting the corrosion
59
prediction model results. The optimization model treated these two divisions as we would
expect; De Anza meters would be inspected at a higher frequency due to the model predicting
significantly higher number of failures than is seen in the data, and Stockton meters would be
inspected at a much lower frequency due to the model predicting significantly lower number
of failures than is seen in the data.
If the current budget was maintained, the probability of failure could be reduced by
almost 25%. The maximum benefit comes with an increase of the allowed budget; a 25%
increase in the annual budget achieves a 44% reduction in the probability of failure. The
results also show that the failure rate is reduced, even with a budget cut.
Percent of Current Budget
Division
Current
100%
110%
125%
De Anza
3
2
2
1
2
3
Diablo
3
4
2
2
4
4
East Bay
3
2
2
1
2
2
Fresno
3
6
4
4
6
6
Mission
3
3
2
2
4
5
North Coast
3
1
1
1
1
2
Peninsula
3
2
2
3
3
3
Sacramento
3
5
4
5
6
6
San Jose
3
4
6
6
5
5
Sierra
3
6
6
5
5
6
Stockton
3
5
6
6
6
6
Yosemite
3
4
6
6
5
6
0.035%
0.022%
0.021%
0.019%
0.024%
0.029%
Total Risk
90%
75%
Table 4.2: Inspection frequency optimized by division and comparing various budget levels.
Table 4.3 shows that when the divisions are grouped into their assigned corrosive environments, failure rate reduction for maintaining the same budget is about equal to that
of treating each division individually. A much larger budget increase than in the division
groupings is necessary to reduce the failure rate further.
60
Percent of Current Budget
Environment
Current
100%
140%
175%
Rural/Urban
3
6
6
6
6
Marine
3
2
2
1
3
Marine/Industrial
3
2
1
1
2
0.035%
0.027%
0.023%
0.019%
0.034%
Total Risk
90%
Table 4.3: Inspection frequency optimized by corrosive environment and comparing various
budget levels.
4.3.1
Sensitivity Analysis
In order to determine which divisions in the optimized inspection schedule would be most
affected by a change from the optimal condition, a sensitivity analysis was performed. The
analysis was performed by maintaining all values of the optimized solution constant, then
changing one value at a time to determine the change in the probability of failure. The
inspection interval for each division was both extended and abbreviated by 1 year to get a
range of how the probability of failure would be affected.
Figure 4-1 shows the results of the sensitivity analysis. The dotted line is the optimized
results from minimizing the probability of failure. The range above and below the dotted line
is the affect of changing the inspection frequency from the optimized results. For example,
the model calculated an optimized inspection frequency of 2 years for East Bay. When
the optimized result was changed to 3 years, the probability of failure increases to almost
.0260%. When the same value is reduced to 1 year, the probability of failure decreases to
below .0200%.
Changing the inspection interval in East Bay and North Coast have the greatest effect
on the total probability of failure. These two divisions share the following three features:
marine atmosphere, high population of meters, and small optimized inspection frequency.
The other divisions that have a medium impact have one or two of those attributes, while
all of the divisions where there is relatively no change to the probability of failure do not
have any of those attributes.
61
Optimization Model Sensitivity Analysis
0.0270%
0.0260%
0.0250%
0.0240%
S0.02230%
0.0210%
0.0200%
0.0190%
0.0180%
001~
Figure 4-1: Sensitivity of optimization model results when the inspection frequency is
changed from the optimal solution for each division.
4.4
Conclusions
The work in this chapter shows the wasted resources generated with the current inspection
requirement. The probability of failure can be redistributed across all of the divisions while
maintaining the overall failure rate in such a way that 35% of the budget can be saved.
Although deviation from the current 36 month inspection interval not possible, it may not
be the optimal inspection interval for all divisions.
Because of the different corrosive environments within PG&E's service area, different
inspection cycles will have varying effects on the overall failure rate attributed to atmospheric
corrosion. The results show that while maintaining the current annual budget, a simple policy
of grouping the meters into the defined corrosive environments to set the inspection interval
may be the best option. The failure rate reduction is about the same while maintaining
the current budget for both optimization scenarios. The sensitivity analysis shows that the
divisions in a marine atmosphere with a high population of meters may have the greatest
impact on minimizing the probability of failure. Since extending the inspection interval
62
beyond 36 months is not possible, PG&E may be able to effect the largest decrease in the
probability of failure by focusing on increasing inspections on those highly sensitive divisions.
63
Chapter 5
Conclusions and Future Work
5.1
Improving the Prediction Model
The corrosion prediction model that was developed through pairing qualitative observations
with quantitative modeling can help PG&E understand the corrosion process throughout
their service area. There are several improvements to these models that would be of significant value to PG&E.
5.1.1
Expand Model to Cover All Divisions
The current model covers 12 of PG&E's 19 divisions. The corrosion start simulation portion
of the model was based on using the 2014 atmospheric corrosion inspection data.
The
seven divisions that were excluded from the model did not have sufficient data points to be
included. Those seven divisions had fewer than 5% of their meters inspected in 2014.
The current inspection interval is performed in accordance with the 36 month federal
requirement, so the remaining seven divisions will be fully inspected by 2016. Once collected,
this atmospheric corrosion inspection data may be utilized to expand a model to cover all of
PG&E's service area.
64
5.1.2
Process Improvement
As shown in both the corrosion start time simulation and the logistic model, the poor quality
of the atmospheric corrosion inspection data was detrimental to the modeling efforts. When
the data was modified to what could be expected given corrosion physics, both models
improved significantly. It was also shown, through an attribute gauge R & R study, that
the current inspection process yields very subjective data. Two process improvements are
proposed to remove some of the subjectivity from inspection process and improve the ability
of PG&E to use the data in future modeling efforts: use the attribute gauge R & R study
process to align the inspectors, and improve the inspection criteria. The first will make the
data more precise, while the second will make the data more accurate.
Attribute Gauge R & R
The atmospheric corrosion inspection process does not require quantitative measurements;
the inspectors make a subjective judgment based on the inspection procedure and training.
Because of the qualitative nature of the data, the attribute gauge R & R study can be used
to identify weaknesses and refine the measurement process to ensure agreement between
inspectors. By focusing on removing the subjectivity from the data through inspector agreement, a much more precise picture can be created of the overall state of the corrosion on the
gas system.
Inspection Criteria
The current process requires that the inspector grade the meter and riser on a three level
scale: no corrosion, low corrosion, and high corrosion. As discussed in Chapter 3, there are
several corrosion types that could satisfy either grading criteria. This introduces subjectivity
into the process. It is proposed that more data is collected during the inspection process.
Table 5.1 shows an example of the type of data that will better define the corrosion state of
each customer meter set by giving a grade for both general and localized corrosion.
By collecting data on the level of general corrosion, the effectiveness of the corrosion
inhibiting coating can be ascertained. Collecting specific data that identifies the location
65
Meter Grading Criteria
General Corrosion
Localized Corrosion
None
No general corrosion
Mild
Paint is compromised, flaking rust is Mild
present
Superficial localized pitting
Advanced
Painted surfaces are completely com- Severe
promised, active corrosion is present.
Metal surfaces are pitted and gouged.
None
No localized corrosion
Table 5.1: Proposed inspection criteria for each meter to include both general and localized
corrosion grades.
and severity of localized corrosion will enable PG&E to develop a more accurate localized
corrosion model to improve the overall corrosion prediction model.
5.1.3
Atmospheric Corrosivity Data
The corrosion prediction model depended on the ISO corrosivity model to define the corrosion
process throughout PG&E's service area. Since the model is a general in nature, it cannot
be expected to accurately model corrosion in all locations in California. The model was
shown to be conservative especially in rural atmospheres, and becoming less conservative
as the corrosivity of the atmosphere increases. To make a corrosion prediction model more
accurate, a better understanding of how corrosion progresses is necessary. A method to map
corrosion throughout PG&E's service area is by deploying atmospheric corrosion monitors
in locations across California. The real time corrosion data that these sensors collect can
then be correlated with actual customer meter inspections in the same area.
An atmospheric corrosion monitor such as the one pictured in Figure 5-1 can be used
to gather real time corrosion rate data. This specific monitor was developed in Japan by
industry and their national association of corrosion engineers. It is a simple galvanic cell that
converts the electrical current measurement between the substrate steel and the silver conductive paste in the presence of a water film to a corrosion rate measurement. By placing the
sensor with a weather monitoring station, the weather effects on location specific corrosion
rates can be determined. This sensor can also be incorporated into a wireless transmitting
device to enable remote data collection. Several institutions in Japan have had success in
researching the accuracy of the sensor [27], [281, and industries such as automotive [29] and
66
energy distribution [30] are having success utilizing this atmospheric corrosion monitor to
determine corrosivity and corrosion rates.
Figure 5-1: Atmospheric corrosion monitor sketch.
The deployment of the atmospheric corrosion monitors would benefit both the electric
and gas organizations within PG&E. The electric side of the business has the locations
(substations, transmission and distribution towers) that would allow for easy deployment of
these monitors, and PG&E already monitors its own weather station network. By utilizing
these or other atmospheric monitoring devices, both the electric and gas organizations can
understand the corrosivity of their service area better. Benefits not only include corrosion
modeling, but also asset maintenance scheduling and material choice for repairs and new
installations.
5.2
Improving the Optimization Model
The data used to develop the corrosion prediction model does not have a time component.
The data only shows snapshot in time; the atmospheric corrosion inspection data for 2014
was the only data available due to a change both the procedure and database management
for the inspection process. Without the corrosion over time data, there is no way to validate
the predictions of the future corrosive state of the gas system. At least two data points for
each meter are needed to allow further calibration of ISO corrosion model.
With corrosion over time data, the model can be validated with a time component.
This will allow PG&E to be more confident in the optimization models ability to accurately
calculate and optimize the inspection frequency to minimize risk. With the current schedule,
67
data the second data point for the 2.3 million meters inspected in 2014 will be collected in
2017. There won't be a complete two point data set for all 4.5 million customer meters until
2019.
5.3
Conclusions
The goal of the project was to couple qualitative data to a quantitative model.
While
the development of the corrosion prediction model was challenging because of the poor data
quality, it was shown that by improving the data the model can be improved. Several process
improvements were identified that could enable PG&E to improve corrosion data quality for
future corrosion modeling efforts. In order to use qualitative observations in a quantitative
way, the subjectivity in the process and collected data needs to be minimized. It will require
an analysis of the methods of collecting the qualitative data to ensure that the data points
are clearly defined.
Industries throughout the US and the world collect millions of qualitative data points
on atmospheric corrosion every year.
However, thorough analysis of the data collection
processes is necessary to fully understand the data and remove any subjectivity. This project
has shown that the ability to utilize these qualitative observations in quantitative corrosion
modeling could improve corrosion risk assessment strategies in order to mitigate the effects
of corrosion on metallic assets.
68
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