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Picture for Applying Machine Learning Techniques To Identify And Predict Behavior Of Rectifier And Groundbed State Change
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Applying Machine Learning Techniques To Identify And Predict Behavior Of Rectifier And Groundbed State Change

Product Number: 51322-17833-SG
Author: Matt Barrett, Will Maize, Tony da Costa
Publication Date: 2022
$20.00

Impressed current rectifiers are the backbone of a pipeline operator’s cathodic protection (CP) systems. A rectifier’s ability to protect a large length of electrically continuous pipeline considerably improves efficiencies and reduces material costs as compared to galvanic systems. However, like galvanic anodes, impressed current anodes are a consumable asset, and require replacement at the end of their service life to ensure that the rectifier can continue to adequately protect the pipeline.
Picture for Digital Monitoring System of Saipem Pipelay Vessels Productivity Parameters
Available for download

Digital Monitoring System of Saipem Pipelay Vessels Productivity Parameters

Product Number: MPWT19-15321
Author: Valerio Bregonzio, Valerio Bregonzio
Publication Date: 2019
$0.00

Saipem recently took a more integrated approach at production data collection and elaboration recently launched an advanced pilot project: the deployment of a vast data collection and analysis system in the production lines of its Flagship CastorONE. Data is collected from multiple sources by means of communication infrastructures and articulated network systems that allow to connect to different machines and equipment. The most meaningful data are identified and pre-filtered. The data are then processed, aggregated and subject to advanced analytics in order to extract useful information on the productivity, the main process parameters, building on its already available tools to manage welding productivity.
This enables Saipem not only to build an integrated, real-time, comprehensive representation of the on-going activities, but also to gain new and valuable insights on the efficiency of its vessels during and after the execution of projects, across different phases, making it easier to compare the productivity estimations with the field performances. The advantages of data-driven continuous improvement are discussed too. This paper will provide a general overview of the system and of the results reached.
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