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The Role of the Soil-Plant-Atmospheric System in Underground Corrosion Rates

Picture for The Role of the Soil-Plant-Atmospheric System in Underground Corrosion Rates
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Product Number: 51321-16422-SG
Author: Thomas Hayden/Alfonso Garcia/Jerry Dewitt/Joseph Mazzella/Len Krissa
Publication Date: 2021
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Modeling corrosion for underground assets is a heavily studied area for many reasons; it's exciting, high impact, and challenging work. Many confounding variables make the analysis complex, such as cathodic protection systems and interaction effects with the climate and soil. This paper addresses the role of the soil-plant-atmospheric system (SPAS) in corrosion growth by presenting datasets, a statistical analysis, and predictive models. Using data from the International Soil Reference and Information Centre (ISRIC) and a North American pipeline operator, this research offers a correlation analysis, showing the impact of soil features and the critical role of cathodic protection systems. Incrementally, by adding variables and non-linear modeling, this paper shows a machine-learning powered framework for estimating corrosion growth rates with features from the soil-plant-atmospheric system, while controlling for confounding variables.

Key words: Pipelines, Corrosion Growth Rates, Soil, Atmospheric

Modeling corrosion for underground assets is a heavily studied area for many reasons; it's exciting, high impact, and challenging work. Many confounding variables make the analysis complex, such as cathodic protection systems and interaction effects with the climate and soil. This paper addresses the role of the soil-plant-atmospheric system (SPAS) in corrosion growth by presenting datasets, a statistical analysis, and predictive models. Using data from the International Soil Reference and Information Centre (ISRIC) and a North American pipeline operator, this research offers a correlation analysis, showing the impact of soil features and the critical role of cathodic protection systems. Incrementally, by adding variables and non-linear modeling, this paper shows a machine-learning powered framework for estimating corrosion growth rates with features from the soil-plant-atmospheric system, while controlling for confounding variables.

Key words: Pipelines, Corrosion Growth Rates, Soil, Atmospheric

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Picture for Estimating Corrosion Rates for Underground Pipelines: A Machine Learning Approach
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Estimating Corrosion Rates for Underground Pipelines: A Machine Learning Approach

Product Number: 51319-13456-SG
Author: Joseph Mazzella, Len Krissa, Thomas Hayden, Haralampos Tsaprailis
Publication Date: 2019
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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.
Picture for 98667 TECHNIQUES FOR ASSESSMENT OF SOIL
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98667 TECHNIQUES FOR ASSESSMENT OF SOIL CORROSIVITY

Product Number: 51300-98667-SG
ISBN: 98667 1998 CP
Author: C. L. Durr, J. A. Beavers
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Picture for 51318-10839- Influence of Soil Fungi on Corrosion of Mild Steel Plates
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51318-10839- Influence of Soil Fungi on Corrosion of Mild Steel Plates

Product Number: 51318-10839-SG
Author: Dharani, R. / Deepalakshmi, R. / Padma Devi, S.N. / Nithya Meenakshi S. / Nalini, D.
Publication Date: 2018
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Five fungal organisms were isolated using Starkey media from the soil of corroded pipeline tank. The influence of these fungal isolates on both rusted and non – rusted mild steel plates were studied for a period of 25 days.
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