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Applying Machine Learning Techniques To Identify And Predict Behavior Of Rectifier And Groundbed State Change

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Impressed current rectifiers are the backbone of a pipeline operator’s cathodic protection (CP) systems. A rectifier’s ability to protect a large length of electrically continuous pipeline considerably improves efficiencies and reduces material costs as compared to galvanic systems. However, like galvanic anodes, impressed current anodes are a consumable asset, and require replacement at the end of their service life to ensure that the rectifier can continue to adequately protect the pipeline.

Product Number: 51322-17833-SG
Author: Matt Barrett, Will Maize, Tony da Costa
Publication Date: 2022
Industries: Cathodic Protection , Corrosion Monitoring and Control , Coatings and Linings , Coatings
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$20.00
$20.00

We explore how rectifier voltage and current measurements can inform pipeline engineers and technicians on the health, performance and operation of their cathodic protection (CP) assets, and predict the future operation of existing and newly installed cathodic protection systems. We leverage years of data from monitoring units installed on CP rectifiers combined with site specific details describing the site and its CP system provided by pipeline operators to train a machine learning model.

The study includes current and historical data from hundreds of unique rectifier locations across Canada which have been historically monitored using a remote monitoring unit (RMU). RMU readings are analyzed and grouped by long term resistance trends. Contextual data is collected for each site. This data describes the cathodic protection relevant details of the site, including details of the pipe, rectifier, groundbed and soil.

A machine learning model has been developed which accepts the contextual data associated with the rectifier and will predict the long-term rectifier resistance trend.

We explore how rectifier voltage and current measurements can inform pipeline engineers and technicians on the health, performance and operation of their cathodic protection (CP) assets, and predict the future operation of existing and newly installed cathodic protection systems. We leverage years of data from monitoring units installed on CP rectifiers combined with site specific details describing the site and its CP system provided by pipeline operators to train a machine learning model.

The study includes current and historical data from hundreds of unique rectifier locations across Canada which have been historically monitored using a remote monitoring unit (RMU). RMU readings are analyzed and grouped by long term resistance trends. Contextual data is collected for each site. This data describes the cathodic protection relevant details of the site, including details of the pipe, rectifier, groundbed and soil.

A machine learning model has been developed which accepts the contextual data associated with the rectifier and will predict the long-term rectifier resistance trend.

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