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How to Cite | Author Information | Publication History | Funding Information
International Journal of Earth & Environmental Sciences Volume 6 (2021), Article ID 6:IJEES-189, 6 pages
https://doi.org/10.15344/2456-351X/2021/189
Original Article
Business Readiness for Implementation of AI Prevention of Potential Risks in Natural Gas Transmission and Storage

Petya Biolcheva

Department of Industrial Business, University of National and World Economy, Bulgaria
Dr. Petya Biolcheva, Department of Industrial Business, University of National and World Economy, Sofia 1000, Students Town, Bulgaria, Tel: +359 887695738; E-mail: p.biolcheva@unwe.bg
22 October 2021; 04 December 2021; 06 December 2021
Biolcheva P (2021) Business Readiness for Implementation of AI Prevention of Potential Risks in Natural Gas Transmission and Storage. Int J Earth Environ Sci 6: 189. doi: https://doi.org/10.15344/2456-351X/2021/189
This research is funded by the Bulgarian National Science Fund, Contract КП-06-М35/1 от 29.09.2020, Project "Risk Integration in Organizational Business Process Management".
© 2021 Biolcheva. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

References

  1. 2030 climate & energy framework. European Commission.
  2. Climate & energy framework (2021) European Commission.
  3. International Energy Statistics database. [View]
  4. Rios-Mercado RZ, Sanchez CB (2015) Optimization Problems in Natural Gas Transportation Systems: A State-of-the-Art Review. Applied Energy 147: 536-555. [CrossRef] [Google Scholar]
  5. Global Energy Statistical Yearbook 2021. Enterdata.
  6. Acton MR, Baldwin PJ, Baldwin TR (1998) The Development оf The Pipesafe Risk Assessment Package for Gas Transmission Pipelines. International Pipeline Conference. [CrossRef] [Google Scholar]
  7. Horita FEA, Albuquerque JP, Marchezini V (2018) Understanding the decision-making process in disaster risk monitoring and early-warning: A case study within a control room in Brazil. International Journal of Disaster Risk Reduction 28: 22-31. [CrossRef] [Google Scholar]
  8. Tsang YP, Choy KL, Wu CH, Ho GTS, Lam CHY, et al. (2018) Internet of Things (IoT)-based risk monitoring system for managing cold supply chain risks. Industrial Management & Data Systems © Emerald Publishing Limited. [Google Scholar]
  9. Han Z, Weng W (2010) An integrated quantitative risk analysis method for natural gas pipeline network. Journal of Loss Prevention in the Process Industries 23: 428-436. [CrossRef] [Google Scholar]
  10. Sosa E, Alvarez-Ramirez J (2009) Time-correlations in the dynamics of hazardous material pipelines incidents. J Hazard Mater 165: 1204-1209. [CrossRef] [Google Scholar] [PubMed]
  11. Bianchini A, Donini F, Guzzini A, Pellegrini M, Saccani C, et al. (2015) Natural Gas pipelines distribution: analysis of risk, design and maintenance to improve the safety performance. 20th Summer School "Francesco Turco" At: Naples (Italy) Volume: Issue Industrial Systems Engineering. [Google Scholar]
  12. Ilan Y (2021) Improving Global Healthcare and Reducing Costs Using Second-Generation Artificial Intelligence-Based Digital Pills: A Market Disruptor. Int J Environ Res Public Health 18: 811. [CrossRef] [Google Scholar] [PubMed]
  13. Yousuf H, Zainal AY, Alshurideh M, Salloum SA (2021) Artificial Intelligence Models in Power System Analysis. Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications. [CrossRef] [Google Scholar]
  14. Addo A (2019) Artificial Intelligence for Risk Management. Business Expert Press.
  15. Artificial Intelligence for Natural Gas Utilities: A Primer (2020) Department of Energy-National Association of Regulatory Utility Commissioners Natural Gas Partnership.
  16. Salehi M, Nikkhah A, Boostani R (2015) Predicting National Gas Consumption in Iran using a Hierarchical Combination of Neural Networks and Genetic Algorithms. Energy.
  17. Anagnostis A, Papageorgiou Е, Bochtis D (2020) Application of Artificial Neural Networks for Natural Gas Consumption Forecasting. Sustainability 12: 6409. [CrossRef] [Google Scholar]
  18. Mata J, Miguel I, Durán RJ, Merayo N, Singh SK, et al. (2018) Artificial intelligence (AI) methods in optical networks: A comprehensive survey. Optical Switching and Networking 28: 43-57. [CrossRef] [Google Scholar]
  19. Cotelle P, Dias T, Florian J, Gunes O (2019) Artificial Intelligence Applied to Risk Management. FERMA Perspectives 3: 15-20.
  20. Maliene V, Grigonis V, Palevičius V, Griffiths S (2011) Geographic information system: Old principles with new capabilities. Urban Design International 16: 1-6. [CrossRef] [Google Scholar]
  21. 11 Impacts of Artificial Intelligence on Project Management. Project Practicai. [View]
  22. Erbach G, O'Shea J (2019) Cybersecurity of critical energy infrastructure. European parliament. [View]
  23. Noguerol TM, Godino FP, Valdivia MT, Menias CO, Luna A, et al. (2019) Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology. Journal of the American College of Radiology 16: 1239-1247. [CrossRef] [Google Scholar]
  24. Кumar S (2019) Advantages and Disadvantages of Artificial Intelligence.
  25. Biolcheva P (2021) The place of artificial intelligence in the risk management process. SHS Web Conf, EDP Sciences. [CrossRef] [Google Scholar]
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