Applying Machine Learning to the Fuel Theft Problem on Pipelines-Reference-Cited by-同舟云学术

Applying Machine Learning to the Fuel Theft Problem on Pipelines

Published:2023-05 Issue:2 Volume:14 Page:
ISSN:1949-1190
Container-title:Journal of Pipeline Systems Engineering and Practice
language:en
Short-container-title:J. Pipeline Syst. Eng. Pract.

Author:

Ventriglia Rachel Martins¹,Dantas Leila Figueiredo²,Brandão Bianca³,Hamacher Silvio⁴,Bellé Rocha Marcos Vinicius⁵,David André Silveira⁵,Ribeiro Frederico Chalita⁵

Affiliation:

1. Engineer, Tecgraf Institute/Dept. of Industrial Engineering, Pontifícia Universidade Católica do Rio de Janeiro, R. Marquês de São Vicente, 225, 22451-900 Rio de Janeiro, Brazil.

2. Professor, Tecgraf Institute/Dept. of Industrial Engineering, Pontifícia Universidade Católica do Rio de Janeiro, R. Marquês de São Vicente, 225, 22451-900 Rio de Janeiro, Brazil.

3. Engineer, Tecgraf Institute/Dept. of Industrial Engineering, Universidade Católica do Rio de Janeiro, R. Marquês de São Vicente, 225, 22451-900 Rio de Janeiro, Brazil.

4. Professor, Tecgraf Institute/Dept. of Industrial Engineering, Pontifícia Universidade Católica do Rio de Janeiro, R. Marquês de São Vicente, 225, 22451-900 Rio de Janeiro, Brazil (corresponding author).

5. Engineer, Transpetro, Av. Presidente Vargas, 328, Rio de Janeiro 20091-060, Brazil.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanical Engineering,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JPSEA2.PSENG-1374

Reference33 articles.

1. Akinsete O. and A. Oshingbesan. 2019. “Leak detection in natural gas pipelines using intelligent models.” In Proc. SPE Nigeria Annual Int. Conf. and Exhibition. Calgary AB Canada: Society of Petroleum Engineers.

2. Attacks on oil transport pipelines in Nigeria: A quantitative exploration and possible explanation of observed patterns

3. ANP (Agência Nacional de Petróleo Gás Natural e Biocombustíveis). 2020. “Anuário Estatístico Brasileiro do Petróleo Gás Natural e Biocombustíveis.” Accessed July 6 2021. https://www.gov.br/anp/pt-br/centrais-de-conteudo/publicacoes/anuario-estatistico/anuario-estatistico-2021.

4. Araujo M. S. H. A. Spidle S. P. Siebenaler S. G. Blaisdell and D. W. Vickers. 2018. “Application of machine learning to distributed temperature sensing (DTS) systems.” In Proc. 2018 12th Int. Pipeline Conf. Volume 3: Operations Monitoring and Maintenance; Materials and Joining. Calgary AB: ASME.

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