A new pre-assessment model for failure-probability-based-planning by neural network |
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| Institution: | 1. Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 210009, China;2. School of Environment and Safety Engineering, Changzhou University, Changzhou, 213164, Jiangsu, China;3. School of Materials Engineering, Changshu Institute of Technology, Changshu, 215500, China;4. Department of Safety, Health, and Environmental Engineering, National Yunlin University of Science and Technology, 123, University Rd., Sec. 3, Douliou, Yunlin, 64002, Taiwan, ROC;5. BASF Corporation, 1609 Biddle Avenue, Wyandotte, MI, 48192, USA;1. College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao, 266590, China;2. Mine Disaster Prevention and Control-Ministry of State Key Laboratory Breeding Base, Shandong University of Science and Technology, Qingdao, 266590, PR China;3. Qingdao Intelligent Control Engineering Center for Production Safety Fire Accident, Qingdao, 266590, PR China;1. Physikalisch-Technische Bundesanstalt (PTB), Braunschweig, Germany;2. thuba AG, Basel, Switzerland;3. Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany;1. School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin, 300384, China;2. Tianjin Key Laboratory of Hazardous Waste Safety Disposal and Recycling Technology, Tianjin, 300384, China;1. College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 210009, China;2. Shenzhen Urban Public Safety and Technology Institute, Shenzhen, 518019, China;3. Shenzhen Key Laboratory of Disaster City Digital Twin, Shenzhen, 518019, China;1. Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India;2. Assam Energy Institute, Centre of Rajiv Gandhi Institute of Petroleum Technology, Sivasagar, 785697, Assam, India;3. Department of Chemical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Dhanbad, 826004, Jharkhand, India |
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| Abstract: | At present, the prediction of failure probability is based on the operation period for laid pipelines, and the method is complicated and time-consuming. If the failure probability can be predicted in the planning stage, the risk assessment system of gas pipeline will be greatly improved. In this paper, the pre-laying assessment model is established to minimize risk of leakage due to piping layout. Firstly, Fault Tree Analysis (FTA) modeling is carried out for urban natural gas pipeline network. According to expert evaluation, 84 failure factors, which can be determined in the planning stage, are selected as the input variables of the training network. Then the FTA model is used to calculate the theoretical failure probability value, and the failure probability prediction model is determined through repeated trial calculation based on BP (Back Propagation Neural Network) and RBF (Radial Basis Function), for obtaining the optimal network parameter combination. Finally, two prediction models are used to calculate the same example. By comparing our pre-assessment model with the theoretical prediction consequences of the fault tree, the results show that the error of RBF prediction model can be close to 3%, which proves the validity and correctness of the method. |
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| Keywords: | Failure probability Pre-laying assessment Combinatorial optimization The neural network The fault tree |
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