Detection of specific features in the functioning of a system for the anti-corrosion protection of underground pipelines at oil and gas enterprises using neural networks

Authors

DOI:

https://doi.org/10.15587/1729-4061.2019.154999

Keywords:

neural network, underground pipelines, polarization potential, DC voltage distribution, oil and gas enterprises

Abstract

The information was reviewed to orderly arrange theoretical provisions and to devise practical recommendations for the diagnostic examination of a system for the anti-corrosion protection of underground metal oil and gas pipelines.

A set of informative parameters for modeling functional relations and determining polarization potential in the system "underground metal structure – cathodic protection plant" was formed.

It was proposed to apply the algorithm of prediction of corrosive current, the approach of non-linear programming, as well as the neural network, including the corresponding methods of learning, for a pipeline section, taking into account the polarization potential on the outer surface. The test set to evaluate the effectiveness of a neural network was formed.

The above-mentioned information is essential for the improvement of the equipment of distant control of metal structures of oil and gas enterprises, that is, the procedures for correct measuring and evaluating direct and alternating voltages, as well as polarization potential in a pipeline.

The methods and algorithms of neural networks, which are used to control the anticorrosive protection of underground pipelines, were explored. The study of the effectiveness of artificial neural networks, specifically, a two-layer network of direct propagation with the function of prediction of the resource of metal pipes. Using the polarization potential, we detected the capability of neural networks to perform inaccessible for conventional mathematics operations of processing, comparison, classification of images, capability of self-learning and self-organization relative to underground pipelines. The qualimetric quality criterion for a pipeline section, taking into consideration the optimal range of polarization potential was improved.

We developed the method for prediction of the polarization potential of a cathodic protection plant and transitional specific resistance of the insulating coating on the surface of an underground metal structure using a neural network. Taking into consideration the results of analysis of polarization potential and transitional specific resistance, the methodology of formation of information support for procedures of degradation of anticorrosive dielectric coating and metal on the outer surface of an underground metal structure, as well as for predicting its resource, was designed

Author Biographies

Vitalii Lozovan, Karpenko Physico-Mechanical Institute of the NAS of Ukraine Naukova str., 5, Lviv, Ukraine, 79601

Postgraduate student

Department of Electrophysical Methods of Non-Destructive Testing

Roman Dzhala, Karpenko Physico-Mechanical Institute of the NAS of Ukraine Naukova str., 5, Lviv, Ukraine, 79601

Doctor of Technical Sciences, Head of Department

Department of Electrophysical Methods of Non-Destructive Testing

Ruslan Skrynkovskyy, Lviv University of Business and Law Kulparkіvska str., 99, Lviv, Ukraine, 79021

PhD, Associate Professor

Department of Business Economy and Information Technology

Volodymyr Yuzevych, Karpenko Physico-Mechanical Institute of the NAS of Ukraine Naukova str., 5, Lviv, Ukraine, 79601

Doctor of Physical and Mathematical Sciences, Professor

Department of Electrophysical Methods of Non-Destructive Testing

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Published

2019-01-23

How to Cite

Lozovan, V., Dzhala, R., Skrynkovskyy, R., & Yuzevych, V. (2019). Detection of specific features in the functioning of a system for the anti-corrosion protection of underground pipelines at oil and gas enterprises using neural networks. Eastern-European Journal of Enterprise Technologies, 1(5 (97), 20–27. https://doi.org/10.15587/1729-4061.2019.154999

Issue

Vol. 1 No. 5 (97) (2019): Applied physics

Section

Applied physics

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Copyright (c) 2019 Vitalii Lozovan, Roman Dzhala, Ruslan Skrynkovskyy, Volodymyr Yuzevych

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