Innervated Pipelines: A New Technology Platform for In-Situ Repair and Embedded Intelligence



Program:
REPAIR
Award:
$999,999
Location:
Pittsburgh,
Pennsylvania
Status:
ACTIVE
Project Term:
01/01/2021 - 12/31/2021
Website:

Critical Need:

Legacy pipe malfunctions create operating risk and legal liability for utilities, negatively impact system owners’ financial performance, and are costly to gas consumers. Today, repairing legacy pipes involves excavating and replacing them, typically with high-density polyethylene pipe. Replacement costs range from $1-10 million per mile, depending on the pipe’s location (rural vs. urban), complexity of the excavation, and costs for restoring roads. Utilities also incur costs whenever gas service is disrupted for repairs. REPAIR seeks to eliminate the highest cost components, excavation and restoration, by rehabilitating pipes without their removal—in essence, by automatically constructing a new pipe within the old.

Project Innovation + Advantages:

The University of Pittsburg team will pursue a new vision for in-situ repair and rehabilitation of pipelines with value added embedded sensing to complement existing non-destructive evaluation (NDE) and in-line inspection techniques. The team will demonstrate robotically deployable cold spray-based processes for producing a metallic pipe within the original structure and explore the feasibility of embedded fiber optic sensors within the newly constructed internal pipe. Acoustic NDE methods will be coupled with embedded fiber optic sensors as well as machine learning-based frameworks to identify, localize, and classify pipeline defects such as corrosion and other operational conditions that require maintenance and repair. Cold spray-based metallic coating offers a scalable and practical solution to confined space repair and is anticipated to meet necessary standards and regulatory approvals. Sensor embedding with the cold spray repair process combined with information available through in-line inspections, mapping, and NDE in the context of artificial intelligence (AI)-based classification frameworks can ultimately reduce downtime associated with major repairs and avoid costly, catastrophic failures. If successful, the team anticipates potential for additional efforts focused on robotic deployment strategies for cold spray rehabilitation and sensor embedding as well as integration of the AI-classification framework, new fiber optic sensor capabilities, and other available information into a digital twin model of the pipeline network.

Potential Impact:

REPAIR seeks to eliminate the highest pipe rehabilitation costs, excavation and restoration, by repairing pipes without removal.

Security:

REPAIR projects should improve the sustainability of domestic natural gas distribution by economically rehabilitating legacy pipes. The project also provides additional real-time information that assists in threat identification.

Environment:

REPAIR will produce new real-time sensor information and data management/visualization tools using AI-based analytics to minimize leaks, catastrophic failures, and repair downtime.

Economy:

REPAIR program innovations will accelerate legacy pipeline replacement while reducing cost to utilities and gas customers and provide for economical repairs through new integrated monitoring tools and techniques.

Contact

ARPA-E Program Director:
Dr. Jack Lewnard
Project Contact:
Dr. Paul Ohodnicki
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.gov
Project Contact Email:
pro8@pitt.edu

Partners

Pacific Northwest National Laboratory

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Release Date:
02/18/2020