SOFTWARE-BASED PIPELINE LEAK DETECTION* Presented by: Miguel J. Bagajewicz , James Akingbola**, Elijah Odusina** and David Mannel** University of Oklahoma School of.
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SOFTWARE-BASED PIPELINE LEAK DETECTION*
Presented by: Miguel J. Bagajewicz , James Akingbola**, Elijah Odusina** and David Mannel**
University of Oklahoma School of Chemical, Biological and Material Engineering
(*) This work was done as part of the capstone Chemical Engineering class at the University of Oklahoma
(**) Capstone Undergraduate students
Abstract: Pipeline leak detection has been a focus of numerous research in industry. There are several methods based on expensive hardware. As an alternative, less costly
software based methods have been proposed. These methods make use of the measured flows and pressures to infer through data reconciliation and bias detection
methodologies whether a leak or a bias is present. In this presentation, the Generalized Likelihood Ratio (GLR) method proposed by Narasimhan and Mah (1987) is adapted
to combined leak detection and instrument bias identification. The methodology is entirely implemented within a simulator.
Introduction
Hardware Method
Pipelines used as bulk carriers of crude oil and natural
gas
Used in water distribution systems
Results of Leakages include:
Loss of product
Environmental hazards
Loss of life
Multi-hop Sensor Wireless Network
Professor Sridhar Radhakrishnan
Solution
Problem: Develop continuous real-time
Multi-hop wireless sensor network with
appropriate sensor fusion technologies.
monitoring of pipelines to determine leak and
other structural damages.
FIBER OPTIC
METHOD
ACOUSTIC EMISSION
METHOD
in both human, property, and environmental
damage.
Limitations
System costs are usually high because of
the amount of instrumentation.
Current Solution: Low flying aircraft, visual
High complexity of installation
Weaknesses: Expensive, non-continuous
inspection, and use of pigs for internal
monitoring.
VAPOR SENSING
METHOD
Generalized Likelihood Ratio
2
Generalized Likelihood Ratio
Research Issues
• Communication in the presence of
unreliable sensors
• Power aware strategies
• Optimal sensor configuration and data
fusion
4
3
Overall Power vs. Simulated Magnitude
(3% Process Variance)
Error vs. Simulated Magnitude
(3% Meter Variance)
Procedure
This is a statistical method modeled
after the flow conditions in the
pipeline
A mathematical model that describes
effects of leaks on the flow process
is derived.
Can detect leaks in pipeline branch,
location in the branch and
magnitude of the leak.
Can differentiate various types of
gross errors
Formulate the hypotheses
for gross error detection
without leaks and biases,
Ho and with leaks and
biases, H1
Use the likelihood ratio test
statistics, λ to test the
hypothesis for gross errors
Determine the magnitude of
gross errors, b
3
Advanced pipeline network used to test the GLR method
6
5
Simulation Procedure
Limitations
Highly dependent on instrument
sensitivity
Smaller leaks typically takes
longer time to detect
Does not give magnitude of the
leak
Importance: Failure of gas pipelines will result
monitoring, fail-first, fix-later solutions.
1
Software Method
Gradient Intersection Method
Simulation Results
7
Simulation Results
8
Future Research
Explore More Complex Networks
The likelihood ratio was tested on a sample pipeline network using PRO II simulation
Multiple Leak Detection
REFERENCES
Test pipeline network
Test specification
9
Alan S. Willsky, and Harold L. Jones. "A Generalized Likelihood Ratio Approach to the Detection and Estimation of
Jumps in Linear Systems." IEEE (1975): 1-5
H. Prashanth Reddy. Leak Detection in Gas Pipeline Networks Using Transfer Function Based Dynamic Simulation
Model. Madras, India: Department of Civil Engineering Indian Institute of Technology Madras Chennai, 2006.
Miguel J. Bagajewicz and Emmanuel Cabrera. "Data Reconciliation in Gas Pipeline Systems." Ind. Eng. Chem. Res 42,
No.22(2003): 1-11.
Misiunas, Dalius. Failure Monitoring and Asset Condition Assessment in Water Supply Systems. ISGN 91-88934-40-3.
Lund, Sweden: Department of Electrical Engineering and Automation Lund University, 2005.
Mukherjee Joydeb, Shankar Narasimhan, and "Leak Detection in Networks of Pipeline by the Generalized Likelihood
Ratio Method." Ind. Eng. Chem. Res 35(1996): 1-8.
Dipl.-Physiker Ralf Tetzner. "Model-Based Pipeline Leak detection and localization." FACHBERICHTE 42(2003):
455-460
PRO II Simulation
10
11
12
SOFTWARE-BASED PIPELINE LEAK DETECTION*
Presented by: Miguel J. Bagajewicz , James Akingbola**, Elijah Odusina** and David Mannel**
University of Oklahoma School of Chemical, Biological and Material Engineering
(*) This work was done as part of the capstone Chemical Engineering class at the University of Oklahoma
(**) Capstone Undergraduate students
Abstract: Pipeline leak detection has been a focus of numerous research in industry. There are several methods based on expensive hardware. As an alternative, less costly
software based methods have been proposed. These methods make use of the measured flows and pressures to infer through data reconciliation and bias detection
methodologies whether a leak or a bias is present. In this presentation, the Generalized Likelihood Ratio (GLR) method proposed by Narasimhan and Mah (1987) is adapted
to combined leak detection and instrument bias identification. The methodology is entirely implemented within a simulator.
Introduction
Hardware Method
Pipelines used as bulk carriers of crude oil and natural
gas
Used in water distribution systems
Results of Leakages include:
Loss of product
Environmental hazards
Loss of life
Multi-hop Sensor Wireless Network
Professor Sridhar Radhakrishnan
Solution
Problem: Develop continuous real-time
Multi-hop wireless sensor network with
appropriate sensor fusion technologies.
monitoring of pipelines to determine leak and
other structural damages.
FIBER OPTIC
METHOD
ACOUSTIC EMISSION
METHOD
in both human, property, and environmental
damage.
Limitations
System costs are usually high because of
the amount of instrumentation.
Current Solution: Low flying aircraft, visual
High complexity of installation
Weaknesses: Expensive, non-continuous
inspection, and use of pigs for internal
monitoring.
VAPOR SENSING
METHOD
Generalized Likelihood Ratio
2
Generalized Likelihood Ratio
Research Issues
• Communication in the presence of
unreliable sensors
• Power aware strategies
• Optimal sensor configuration and data
fusion
4
3
Overall Power vs. Simulated Magnitude
(3% Process Variance)
Error vs. Simulated Magnitude
(3% Meter Variance)
Procedure
This is a statistical method modeled
after the flow conditions in the
pipeline
A mathematical model that describes
effects of leaks on the flow process
is derived.
Can detect leaks in pipeline branch,
location in the branch and
magnitude of the leak.
Can differentiate various types of
gross errors
Formulate the hypotheses
for gross error detection
without leaks and biases,
Ho and with leaks and
biases, H1
Use the likelihood ratio test
statistics, λ to test the
hypothesis for gross errors
Determine the magnitude of
gross errors, b
3
Advanced pipeline network used to test the GLR method
6
5
Simulation Procedure
Limitations
Highly dependent on instrument
sensitivity
Smaller leaks typically takes
longer time to detect
Does not give magnitude of the
leak
Importance: Failure of gas pipelines will result
monitoring, fail-first, fix-later solutions.
1
Software Method
Gradient Intersection Method
Simulation Results
7
Simulation Results
8
Future Research
Explore More Complex Networks
The likelihood ratio was tested on a sample pipeline network using PRO II simulation
Multiple Leak Detection
REFERENCES
Test pipeline network
Test specification
9
Alan S. Willsky, and Harold L. Jones. "A Generalized Likelihood Ratio Approach to the Detection and Estimation of
Jumps in Linear Systems." IEEE (1975): 1-5
H. Prashanth Reddy. Leak Detection in Gas Pipeline Networks Using Transfer Function Based Dynamic Simulation
Model. Madras, India: Department of Civil Engineering Indian Institute of Technology Madras Chennai, 2006.
Miguel J. Bagajewicz and Emmanuel Cabrera. "Data Reconciliation in Gas Pipeline Systems." Ind. Eng. Chem. Res 42,
No.22(2003): 1-11.
Misiunas, Dalius. Failure Monitoring and Asset Condition Assessment in Water Supply Systems. ISGN 91-88934-40-3.
Lund, Sweden: Department of Electrical Engineering and Automation Lund University, 2005.
Mukherjee Joydeb, Shankar Narasimhan, and "Leak Detection in Networks of Pipeline by the Generalized Likelihood
Ratio Method." Ind. Eng. Chem. Res 35(1996): 1-8.
Dipl.-Physiker Ralf Tetzner. "Model-Based Pipeline Leak detection and localization." FACHBERICHTE 42(2003):
455-460
PRO II Simulation
10
11
12