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Picture for Integrity Maintaining and Cost Saving Advanced CUI Detection Technique (PEC)
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Integrity Maintaining and Cost Saving Advanced CUI Detection Technique (PEC)

Product Number: MPWT19-14242
Author: Amro Hassanein, Ahmed Elsharkawi
Publication Date: 2019
$0.00

Corrosion under insulation (CUI) is a major problem for petroleum and petrochemical process industries that affects the plant mechanical integrity and attacks assets. CUI can result in sudden and hazardous leaks (safety concern), and plant shutdowns with high losses of production (economical concern).
Traditional detecting methods of CUI to cut a window in thermal insulation used to inspect visually, to measure the thickness, and then return thermal insulation back, giving high chance for water and moisture ingress, accelerating CUI, moreover big amount of scaffolding erection along with thermal insulation removal required.
SHARQ (Eastern Petrochemical Company, one of SABIC companies) is pioneer to study CUI, evaluating many Non–Destructive techniques has proven Pulsed Eddy Current (PEC) as the most effective technique in terms of integrity and cost optimization.
Considering all available techniques, all aspects studied, such as range of applications, features, and limitations, it is concluded and verified to meet our inspection plan strategy needs.
PEC does not require thermal insulation removal; optimize scaffolding erection, has a wide range of applications related to thickness, and temperature. PEC approved by international codes and standards (API) to meet RBI Meridium software requirements.
The validation study results show cost savings of more than 50% compared to traditional thickness measurement methodology, moreover it reduces EHSS (Environment, Health, Safety, and Security) negative impact reduces the probability of safety incidents due to reducing labor, man-hours, and eliminating many associated activities with potential hazard and risk.
PEC has high productivity, easily operated, and provides comprehensive and professional inspection report

Picture for Intelligent Corrosion Prediction using Bayesian Belief Networks
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Intelligent Corrosion Prediction using Bayesian Belief Networks

Product Number: 51319-13372-SG
Author: Michael Smith
Publication Date: 2019
$20.00

Accurate knowledge of corrosion location severity cause and growth rate is critical to pipeline integrity and in‑line inspection (ILI) is widely regarded as the most reliable and convenient method of obtaining such knowledge. Much industry effort has therefore centred on improving the metal loss detection and sizing capabilities of ILI tools.However when ILI data are lacking or unattainable operators must seek alternative ways to monitor the integrity of an asset. For managing internal corrosion Internal Corrosion Direct Assessment (ICDA) is perhaps the best known alternative. ICDA employs the engineering analyses of corrosion and flow modelling to identify areas at high risk from internal corrosion. The highest priority areas are excavated and directly examined in order to establish the condition of the pipeline. This combination of corrosion and flow modelling can be used to provide detailed corrosion predictions but in the absence of ILI data selection of excavation sites can be problematic. The inherent randomness and uncertainty in the models means that the outputs must often be overly conservative; consequently ICDA can be a costly process with no guarantee of quality.The shortcomings of ICDA (and related methods) create a need for a more reliable and accurate corrosion prediction solution which does not require a pipeline to be inspected using ILI. This paper explores the use of Bayesian Belief Networks (BBNs) for this purpose. BBNs are graphical models capable of integrating expert knowledge and data into a single system; ‘expert knowledge’ is captured through industry standard corrosion modelling techniques while ‘data’ is captured through historical ILIs for piggable pipelines. A trained BBN can then be used to make predictions for pipelines without ILI data based on a knowledge of their operational conditions alone.Using case studies on real pipelines it is demonstrated that BBNs can lead to more intelligent predictions of internal corrosion behaviour and improved pipeline integrity management decisions.

Picture for Interaction of a Faulty AC Power Conditioning Capacitor Bank with Welded Steel Pipeline
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Interaction of a Faulty AC Power Conditioning Capacitor Bank with Welded Steel Pipeline

Product Number: 51321-16221-SG
Author: Cal Chapman, P. E.
Publication Date: 2021
$20.00
	Picture for Interference Study between a Solar Array Power Station and a Transmission Pipeline
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Interference Study between a Solar Array Power Station and a Transmission Pipeline

Product Number: 51324-21051-SG
Author: Andres Peratta; Cristina Peratta; John Baynham; Nora Villamizar Piñeros; Didier Lozano Abril
Publication Date: 2024
$40.00