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Laboratory soil-box experiments to understand the effect of specific chemical constituents in soil on corrosion in relation to the AC and DC current densities. A field investigation of AC corrosion on an operational pipeline is also presented.
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For over 50 years Control of Pipeline Corrosion, written by A.W. Peabody, has been the most trusted resource in the field of pipeline corrosion. This revised and updated third edition of the classic reference continues the tradition of excellence by providing the most thorough and authoritative coverage of the prevention and control of pipeline corrosion, and now includes a chapter on AC interference.
Peabody's Control of Pipeline Corrosion, Third Edition, edited by Ronald Bianchetti, blends traditional corrosion engineering practices with modern technologies to make this the single source for corrosion control. Chapters have updated information and graphics to expand on the user-friendly nature of the original book.
2018 NACE, 316 pages
According NACE21414 and ISO18086 standards AC corrosion risks on pipelines requires knowledge on the AC and CP current density on a coating defect or coupon. The induced voltage is mainly a result of the pipeline coating properties, connections to grounded structures (anode beds, other pipelines, AC grounding systems, etc.) and the powerline characteristics (AC load, phase arrangement, tower configuration, etc.). A proper mitigation design engineering study should include all these variables.
Estimating corrosion growth rates for underground pipelines is a challenging problem. There are confounding variables with complex interaction effects that may result in unexpected outcomes. For instance, the relationship between soil conditions and AC interference is highly non-linear and challenging to model. This work expands upon prior work using a suite of machine learning tools to estimate corrosion rates. However, instead of estimating a single corrosion growth rate for a single girth weld address (GWA), this work estimates a distribution of potential corrosion growth rates. Modeling distributions provide a more effective risk-measurement framework, especially concerning high volatility or areas of severe tail risk.
This work relies heavily on machine learning and geospatial tools - particularly artificial neural networks and gradient boosted trees to estimate the corrosion rates and non-linear processes. Building upon prior work using data from a North American Operator, the models in this paper use additional variables from recent research in AC interference and microbiologically influenced corrosion to construct a higher accuracy and distribution-based model of pipeline corrosion risk.
With the rapid development of the economy and acceleration of urban modernization, many high-speed railways and buried pipelines have been built in China. Due to the limitation of space or geographical location, high-speed railways are often constructed in parallel or crossing with long-distance pipelines in some locations. The interference of high-speed railways on pipelines is the result of inductive and conductive coupling, which brings about AC corrosion and other safety issues.