Development of machine learning model for determination of contamination length in a multi-product pipeline-Reference-Cited by-同舟云学术

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Development of machine learning model for determination of contamination length in a multi-product pipeline

Published:2023-02 Issue: Volume:10 Page:100085
ISSN:2666-2604
Container-title:Upstream Oil and Gas Technology
language:en
Short-container-title:Upstream Oil and Gas Technology

Author:

Uwaezuoke N.^ORCID,Obiora C.F.^ORCID,Igwilo K.C.,Kerunwa A.^ORCID,Nwanwe E.O.^ORCID

Publisher

Elsevier BV

Subject

Geochemistry and Petrology,Geophysics,Geotechnical Engineering and Engineering Geology,Fuel Technology,Chemical Engineering (miscellaneous)

Reference52 articles.

1. Direct fitting-the technology of viscous products batching from the pressure product pipeline;Salamandra;J. Mach. Manuf. Reliabil.,2013

2. Mixing of miscible but dissimilar liquids in a serial flow in a pipeline;Austin,1963

3. Chapter 11; Pump stations;Shashi Menon,2011

4. Batch Scheduling of Multiproduct Pipeline Networks;Vlot,2017

5. Estimating Mixing Lengths in Multi-Product Pipelines;Li,2019

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