Products tagged with 'corrosion leak'

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A Prediction of Corrosion-related Leakage on Distribution Pipelines via Machine Learning Method

Product Number: 51323-18972-SG

Author: Zhuoran Zhang, Guanlan Liu

Publication Date: 2023

$20.00

In the United States, there are fuel pipelines spanning more than 2.6 million miles. A major portion of the pipelines is gas distribution lines, where the product is delivered from the pressure regulating station to the customer’s home or facility. The Pipeline and Hazardous Materials and Safety Administration (PHMSA) finalized rules for Distribution Integrity Management Program (DIMP) plans in 2009, enforcing the distribution pipeline operators to assess, report, and manage the risk associated with the pipeline operation. Corrosion threat is one major threat to the pipeline operation and integrity based on CFR 192, CFR 195 and ASME B31.82. A comprehensive understanding and assessment of corrosion risk are indispensable for a safer pipeline operation. This demands a more precise understanding, prediction, and management of the pipeline corrosion

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Enhancing The Reliability Of Ageing Assets In Utility Plants Though The Application Of RTR

Product Number: 51322-17881-SG

Author: Musab M. Talal, Md Anwar Parvez, Faris M Alkordy

Publication Date: 2022

$20.00

A Saudi Aramco facility continues to serve a vital rule in the processing and export of crude oil. This facility is one of the few facilities worldwide that is considered to be fully sufficient with different processes within its boundaries. While processing hydrocarbon throughout the day, this facility also processes utilities to serve the nearby community as well as its own oper ation. The raw water that feeds the utility process arrives from a nearby fields through the 24” supply lines. These lines provide n. the subject RO plant within the utility process with the sufficient amount of water to sustain the huge magnitude of operation.

Products tagged with 'corrosion leak'

A Prediction of Corrosion-related Leakage on Distribution Pipelines via Machine Learning Method

Enhancing The Reliability Of Ageing Assets In Utility Plants Though The Application Of RTR

Association for Materials Protection and Performance