Effectiveness Assessment of Pipeline Cathodic Protection (CP) Systems Using Remote Sensing, Advanced Modeling, and Data Analytics

Overview

Fast Facts
Project No.	1018
Contract No.	693JK32350006CAAP
Research Award Recipient	Rutgers, The State University 3 Rutgers Plaza New Brunswick, NJ 08907
AOR/TTI	Zaid Obeidi Nusnin Akter
Researcher Contact Info	Dr. Hao Wang, Associate Professor, Department of Civil & Environmental Engineering 848-445-2874 hwang.cee@rutgers.edu Dr. Nenad Gucunski (Co-PI): Professor and Chair in the Department of CEE at Rutgers University. Dr. Roger Wang (Co-PI): Assistant Professor at Rutgers University Dr. Qixin Zhou (Co-PI): Associate Professor in Department of Chemical, Bimolecular, and Corrosion Engineering at The University of Akron

Technology and Commercialization
Technology Demonstrated	TBD
Commercialized (in whole/part)	TBD
Commercial Partner	Empty Value
Net Improvement	Empty Value

Financial and Status Data
Project Status	Active
Start Fiscal Year	2023 (09/30/2023)
End Fiscal Year	2026 (09/29/2026)
PHMSA $$ Budgeted	$999,742.00

Main Objective

The main objective is to develop an innovative method for assessing the effectiveness and protection level of CP systems through the integration of remote inspection, advanced simulation, and data analytics.

Public Abstract

This project aims to develop an integrated method to assess CP effectiveness and determine adaptive CP requirements. The proposed approach leverages remote sensing techniques and advanced simulation models to predict pipe-to-soil potential and corrosion rate while reducing the need for field testing.

The project consists of the following tasks and objectives:

Developing and validating an electro-chemical-soil coupled model to predict CP potential and corrosion rate
Comparing two criteria for CP effectiveness assessments (CP potential and corrosion rate) that consider spatial and temporal variations of soil condition and pipe coating damage
Developing remote inspection methods of soil corrosivity and corroded pipe using ground-penetrating radar (GPR) and hyperspectral imaging (HSI) with deep learning algorithms
Analyzing the relationship between CP history, inline inspection (ILI), and pipeline operation data through data analytics
Evaluating the reliability of the proposed assessment method in field conditions. The project outcome could help pipeline operators more efficiently determine CP system effectiveness and evaluate corrosion risk under variable environmental conditions.

Anticipated Results: The outcome of this study will create new knowledge for assessing CP system and protection level with reduced field testing.

Potential Impact on Safety: To reduce the risks of pipeline incidents due to external corrosion and improve pipeline integrity management.