Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines-Reference-Cited by-同舟云学术

Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines

Published:2022-11 Issue: Volume:259 Page:125025
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Zheng Jianqin^ORCID,Wang Chang,Liang Yongtu,Liao Qi,Li Zhuochao,Wang Bohong^ORCID

Publisher

Elsevier BV

Subject

General Energy,Pollution,Mechanical Engineering,Building and Construction,Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Civil and Structural Engineering

Reference43 articles.

1. A hybrid computational approach for detailed scheduling of products in a pipeline with multiple pump stations;Zhang;Energy,2017

2. Deeppipe: a customized generative model for estimations of liquid pipeline leakage parameters;Zheng;Comput Chem Eng,2021

3. Sustainable refined products supply chain: a reliability assessment for demand-side management in primary distribution processes;Wang,2020

4. Numerical analysis of multi-factors effects on the leakage and gas diffusion of gas drainage pipeline in underground coal mines;Cai;Process Saf Environ Protect,2021

5. An online real-time estimation tool of leakage parameters for hazardous liquid pipelines;Zheng;International Journal of Critical Infrastructure Protection,2020

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