<abstract xmlns="http://www.w3.org/1999/xhtml">

The scientific knowledge, transposed into the engineering practice, requires the collection, by using the most state-of-the-art and complete means of the information necessary for the decision to initiate the most appropriate measures of predictive maintenance.
In this context, the information provided as a result of the investigation of the pipelines intended for the transport of fluid hydrocarbons with smart pigging devices (cleaning, calibration, geometric, magnetic flux leakage) refers to those pre-existing in the questionnaire of the pipeline of the inspection operation. The values in the questionnaire are used to evaluate the anomalies in the inspection reports (preliminary and final).
A quantitative assessment of anomalies is based on, and limited exclusively to the results of the inspection, and does not include any numerical parameters (corrosion growth rates, anodic potential etc.), other than those from In-Line Inspection such as values Estimated Repair Factor (ERF) of anomalies.
The questionnaire (initial data provided) of the pipeline to be investigated with smart pigging devices includes at least: pipe diameter, wall thickness, pipe material, design pressure, Maximum Allowable Operating Pressure (MAOP), transported product, curve type, investigation history. The detection thresholds are applied in accordance with the manufacturing standards of the pipes.
Generally, the calculation results, namely ERF and safe pressure, based on ASME B31G (Manual for Determining the Remaining Strength of Corroded Pipelines) are used to present the pipeline condition.
There are several approaches that can be used to characterize the behavior of corrosion anomalies, both pierced and partial. ASME B31G is a very conservative criterion that helps operators avoid unnecessary cuts. It is based on an empirical adequacy to an extensive series of tests on a full scale on vessels with narrow ridges.
Depth-based histograms show the distribution of all metal loss characteristics detected along the entire length of the pipe relative to their location and surface.
The approach to the referred issue allows the collection of essential information about the pipeline, and presents summaries of any anomalies of the pipeline, having a comprehensive character.
</abstract>

The scientific knowledge, transposed into the engineering practice, requires the collection, by using the most state-of-the-art and complete means of the information necessary for the decision to initiate the most appropriate measures of predictive maintenance.
In this context, the information provided as a result of the investigation of the pipelines intended for the transport of fluid hydrocarbons with smart pigging devices (cleaning, calibration, geometric, magnetic flux leakage) refers to those pre-existing in the questionnaire of the pipeline of the inspection operation. The values in the questionnaire are used to evaluate the anomalies in the inspection reports (preliminary and final).
A quantitative assessment of anomalies is based on, and limited exclusively to the results of the inspection, and does not include any numerical parameters (corrosion growth rates, anodic potential etc.), other than those from In-Line Inspection such as values Estimated Repair Factor (ERF) of anomalies.
The questionnaire (initial data provided) of the pipeline to be investigated with smart pigging devices includes at least: pipe diameter, wall thickness, pipe material, design pressure, Maximum Allowable Operating Pressure (MAOP), transported product, curve type, investigation history. The detection thresholds are applied in accordance with the manufacturing standards of the pipes.
Generally, the calculation results, namely ERF and safe pressure, based on ASME B31G (Manual for Determining the Remaining Strength of Corroded Pipelines) are used to present the pipeline condition.
There are several approaches that can be used to characterize the behavior of corrosion anomalies, both pierced and partial. ASME B31G is a very conservative criterion that helps operators avoid unnecessary cuts. It is based on an empirical adequacy to an extensive series of tests on a full scale on vessels with narrow ridges.
Depth-based histograms show the distribution of all metal loss characteristics detected along the entire length of the pipe relative to their location and surface.
The approach to the referred issue allows the collection of essential information about the pipeline, and presents summaries of any anomalies of the pipeline, having a comprehensive character.

Methods of Carrying Out the Anticipative Maintenance of Fluid Hydrocarbons Transport Systems

Bulletin of the Polytechnic Institute of Iași. Machine constructions Section

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The scientific knowledge, transposed into the engineering practice, requires the collection, by using the most state-of-the-art and complete means of the...

Methods of Carrying Out the Anticipative Maintenance of Fluid Hydrocarbons Transport Systems

Pubblicato online: 23 feb 2022

Pagine: 39 - 62

Ricevuto: 31 mar 2021

Accettato: 29 giu 2021

DOI: https://doi.org/10.2478/bipcm-2021-0016

Parole chiave
predictive maintenance, smart pigging, In-Line Inspection, histograms, metal loss

© 2021 Robert-Gheorghe Vlădescu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Methods of Carrying Out the Anticipative Maintenance of Fluid Hydrocarbons Transport Systems

Pubblicato online: 23 feb 2022

Pagine: 39 - 62

Ricevuto: 31 mar 2021

Accettato: 29 giu 2021

DOI: https://doi.org/10.2478/bipcm-2021-0016

Parole chiavepredictive maintenance, smart pigging, In-Line Inspection, histograms, metal loss

© 2021 Robert-Gheorghe Vlădescu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
predictive maintenance, smart pigging, In-Line Inspection, histograms, metal loss