• DocumentCode
    251322
  • Title

    Fault detection of Brahmanbaria Gas Plant using Neural Network

  • Author

    Sowgath, M.T. ; Ahmed, Shehab

  • Author_Institution
    Chem. Eng. Dept., BUET, Dhaka, Bangladesh
  • fYear
    2014
  • fDate
    20-22 Dec. 2014
  • Firstpage
    733
  • Lastpage
    736
  • Abstract
    In recent years, several accidents in pioneer gas processing industries led industries to put emphasis on real-time fault detection. Neural Network (NN) based fault (abnormal situation) detection technique played an important role in monitoring industrial safety. In this work, an attempt has been made to study the fault detection of Brahmanbaria gas processing plant using multi layered feed forward NN based system. NN based fault detection system is trained, validated and tested using data generated using the dynamic model. Preliminary results show that NN based method is able to detect the faults of Brahmanbaria Gas processing plant for fewer no of faults.
  • Keywords
    fault diagnosis; fuel processing industries; gas industry; industrial accidents; industrial plants; multilayer perceptrons; production engineering computing; safety; Brahmanbaria gas processing plant; NN based fault detection system; dynamic model; gas processing industries; industrial safety monitoring; multilayered feed forward NN based system; neural network; real-time fault detection; Artificial neural networks; Fault detection; Monitoring; Neurons; Poles and towers; Valves; Fault detection; GasProcessing Plant; Industrial Safety Management; Neural Network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical and Computer Engineering (ICECE), 2014 International Conference on
  • Conference_Location
    Dhaka
  • Print_ISBN
    978-1-4799-4167-4
  • Type

    conf

  • DOI
    10.1109/ICECE.2014.7026933
  • Filename
    7026933