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The pipeline industry has widely used integrity principles to manage time-dependent and time-independent threats. The detection of time-dependent threats such as corrosion has been accomplished by using inline inspection tool technologies such as ultrasonic and magnetic flux leak inspection tools. However, most facility piping assets can not easily be inspected using in-line inspection methods and must instead be assessed using data collected from operations, such as flow frequency, product type, Cathodic protection record, or Direct Assessment Methods using Non Destructive Testing such as ultrasonic measurements or monitoring of corrosion coupons.
This study focuses on applying machine learning algorithms to predict the corrosion depth of facility station piping assets, as well as comparing the computational accuracy of the predicted corrosion depth based on various machine learning algorithms. Simulated corrosion testing data of facility piping was fit into the following machine learning algorithms: Gradient Boosting(GBM), Artificial Neural Network (ANN), and Random Forest (RF). K-fold cross validation was used to evaluate the models and grid search was applied for the models to refine and calibrate each model. The variable sensitivity analysis was conducted separately for the external and internal corrosion of station piping, and it assisted in limiting the number of independent variables included in machine learning models. This study compares the performance of corrosion depth prediction models for facility station piping and draws conclusions on model performance based on performance evaluation metrics.
Estimating corrosion growth rate for underground pipelines is a non-linear multivariate problem. There are many potential confounding variables such as soil parameters cathodic protection AC/DC interference seasonal / climate conditions and proximity to unique geographic features such as wetlands or polluted environments. The work presented provides an approach for estimating underground corrosion growth rates using a dataset of observations from a North American pipeline operator. Extensive geospatial data is utilized that has been obtained from public and private sources and extrapolated using Inverse distance weighted (IDW) interpolation. This work presents a model using IDW to estimate parameters involving soil interference geography and climate factors for any location in North America.Using this data this work then presents several different machine learning approaches including Generalized Linear Models eXtreme Boosted Trees and Neural Networks. All three provide an accurate estimation for corrosion growth rates for an underground asset at any latitude and longitude pair in North America. Each method comes with potential benefits and pitfalls specifically; trade-offs between model accuracy and transparency. This work presents a framework for comparing geo-spatial and machine learning estimates. Findings and a framework are provided for owners to assess how to think machine learning on their own assets.
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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