Application of Artificial Intelligence in Oil and Gas Pipelines Maintenance Management System

Publish Year: 1388
نوع سند: مقاله کنفرانسی
زبان: English
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IPPC02_070

تاریخ نمایه سازی: 25 شهریور 1388

Abstract:

Oil and gas Pipelines maintenance activities are very important to increase reliability of pipelines system. New technical tools are applied in maintenance management system (MMS) of oil and gas pipelines. One of the most exclusive and powerful of them is Artificial Intelligence (AI) techniques. These tools are employed to modeling of Pipelines MMS and optimize the efficiency of the system. Surveying some new papers in recent years shows that application of AI in MMS is used in two major fields, Prediction of time series, and Fault diagnosis and detection. Iran as a country with biggest pipeline network of the Middle East and its strategic placement in the region, for energy market of Europe and Central and South Asia could apply and profit from AI techniques in pipelines MMS.This article reviews these applications and expresses new fields of AI employment in gas and oil pipelines MMS.

Authors

Seyed Jafar Golestaneh

PhD, Candidate, Industrial Engineering

Napsiah bt Ismail

PhD, Mechanical Engineering

Tang Sai Hong

PhD, Mechanical Engineering

Hassan Moslemi Naeini

PhD, Mechanical Engineering

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  • Edwards, David J., Yang, J., Cabahug, R., Love, P.E.D., In ...
  • Witczak, M., Advances in model-based fault diagnosis with evolutionary algorithms ...
  • Tian , L. and Noore, A., Evolutionary neuural network modeling ...
  • Bevilacqua M., Braglia M., Frosolini M.and Montanari R., Failure rate ...
  • Al-Garni, A.Z., Jamal A., Ahmad A.M., Al-Garni A.M.and Tozan M., ...
  • May 24-25, 2009 Razi International Conference Center, Tehran, Iran ...
  • Liang, Y., Combining neural networks and genetic algorithms for predicting ...
  • Negnevitsky, M. and Pavlovsky, V., Neural Networks Approach to Online ...
  • Tan, S.C. , Lim, C.P. and Rao, M.V.C., A hybrid ...
  • Mrugalski , M., Witczak, M. and Korbicz, J., Confidence estimation ...
  • Sunil, K., Sinha, A., Fieguth, P.W., Neuro-fuzzy network for the ...
  • Kumar, S., Taheri, F., Neuro-fuzzy approaches for pipeline condition assessment, ...
  • Sukarno, P., Adji Sidarto, K, Trisnobudi, A., Ira Setyoadi, D., ...
  • Verde, C., M ora _ es-Menendez, R., Garza, L.E., Vargas, ...
  • May 24-25, 2009 Razi International Conference Center, Tehran, Iran ...
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