Simulation-based fault propagation analysis-Application on hydrogen production plant

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@Article{Gabbar:2014:PSEP,
  author =       "Hossam A. Gabbar and Sajid Hussain and 
                 Amir Hossein Hosseini",
  title =        "Simulation-based fault propagation
                 analysis-Application on hydrogen production plant",
  journal =      "Process Safety and Environmental Protection",
  volume =       "92",
  number =       "6",
  pages =        "723--731",
  year =         "2014",
  month =        nov,
  ISSN =         "0957-5820",
  DOI =          "doi:10.1016/j.psep.2013.12.006",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957582013000955",
  keywords =     "genetic algorithms, genetic programming, Fault sematic
                 network (FSN), Cu-Cl thermochemical cycle, Aspen HYSYS,
                 Neural networks, Process variables interaction",
  size =         "9 pages",
  abstract =     "Recently production of hydrogen from water through the
                 Cu--Cl thermochemical cycle is developed as a new
                 technology. The main advantages of this technology over
                 existing ones are higher efficiency, lower costs, lower
                 environmental impact and reduced greenhouse gas
                 emissions. Considering these advantages, the usage of
                 this technology in new industries such as nuclear and
                 oil is increasingly developed. Due to hazards involved
                 in hydrogen production, design and implementation of
                 hydrogen plants require provisions for safety,
                 reliability and risk assessment. However, very little
                 research is done from safety point of view. This paper
                 introduces fault semantic network (FSN) as a novel
                 method for fault diagnosis and fault propagation
                 analysis by using evolutionary techniques like genetic
                 programming (GP) and neural networks (NN), to uncover
                 process variables' interactions. The effectiveness,
                 feasibility and robustness of the proposed method are
                 demonstrated on simulated data obtained from the
                 simulation of hydrogen production process in Aspen
                 HYSYS. The proposed method has successfully achieved
                 reasonable detection and prediction of non-linear
                 interaction patterns among process variables.",
}

Genetic Programming entries for Hossam A Gabbar Sajid Hussain Amir Hossein Hosseini

Citations