Simone Cesari
Eni
6 Papers
Simone Cesari is an academic researcher from Eni. The author has contributed to research in topics: Pipeline transport & Computer science. The author has an hindex of 1, co-authored 3 publications.
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Papers
A data-driven pipeline pressure procedure for remote monitoring of centrifugal pumps
TL;DR: A predictive maintenance strategy where the condition of a centrifugal pump is tracked by solely exploiting standard pressure measurements, recorded also on remote points along the pipeline, and using an unsupervised learning approach is presented and validated on historical pressure signals collected by Eni for several years on a crude oil transportation pipeline.
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Digital Transformation of Historical Data for Advanced Predictive Maintenance
Giuseppe Giunta,Giancarlo Bernasconi,Riccardo Angelo Giro,Simone Cesari +3 more
- 09 Nov 2020
TL;DR: The proposed case history reveals the potential of adding value to legacy data, as they can be reprocessed, tagged and used as supervised examples in the training phase of new data-driven procedures; comparing, merging and complementing monitoring strategies of assets at different digitalization stages; aiding the development of predictive maintenance strategies.
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Tagging and tracking oil-gas mixtures in multiphase pipelines
TL;DR: In this paper , the authors used a machine learning model to distinguish the characteristic behavior of two different oil-gas slugs, which behaves like a coded tag linked to the flowing fluid, and tracked such multiphase slugs along the flowline and at each monitoring station.
1
Online Monitoring of Inner Deposits in Crude Oil Pipelines
TL;DR: In this paper , a machine learning methodology was proposed to perform online monitoring of the inner deposits in crude oil trunklines, where the attenuation of pressure transients within the fluid is dependent on the free cross-sectional area of the pipe.
•Posted Content
Predicting pigging operations in oil pipelines.
TL;DR: In this paper, an innovative machine learning methodology that leverages on long-term vibroacoustic measurements to perform automated predictions of the needed pigging operations in crude oil trunklines is presented.