Prediction of BLEVE mechanical energy by implementation of artificial neural network |
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| Affiliation: | 1. School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, 81310, Johor, Malaysia;2. Construction Research Centre, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, 81310, Johor, Malaysia;3. Department of Civil Engineering, Shiraz Branch, Islamic Azad University, Shiraz, 74731-71987, Iran;1. State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, 100081 Beijing, China;2. Beijing Academy of Safety Science and Technology, 100070 Bejing, China;3. Petroleum Production Technology Research Institute, Petro China Jilin Oilfield Company, Songyuan, China;1. LSR Laboratory for Risk Science, IMT Mines Ales, France;2. Department of Mechanical and Materials Engineering, Queen’s University, Kingston, Ontario, Canada;1. Center for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China;2. Tianjin University-Curtin University Joint Research Centre of Structure Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University, WA 6102, Australia |
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| Abstract: | In the event of a BLEVE, the overpressure wave can cause important effects over a certain area. Several thermodynamic assumptions have been proposed as the basis for developing methodologies to predict both the mechanical energy associated to such a wave and the peak overpressure. According to a recent comparative analysis, methods based on real gas behavior and adiabatic irreversible expansion assumptions can give a good estimation of this energy. In this communication, the Artificial Neural Network (ANN) approach has been implemented to predict the BLEVE mechanical energy for the case of propane and butane. Temperature and vessel filling degree at failure have been considered as input parameters (plus vessel volume), and the BLEVE blast energy has been estimated as output data by the ANN model. A Bayesian Regularization algorithm was chosen as the three-layer backpropagation training algorithm. Based on the neurons optimization process, the number of neurons at the hidden layer was five in the case of propane and four in the case of butane. The transfer function applied in this layer was a sigmoid, because it had an easy and straightforward differentiation for using in the backpropagation algorithm. For the output layer, the number of neurons had to be one in both cases, and the transfer function was purelin (linear). The model performance has been compared with experimental values, proving that the mechanical energy of a BLEVE explosion can be adequately predicted with the Artificial Neural Network approach. |
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| Keywords: | BLEVE Vessel explosion Explosion energy Blast overpressure Pressure wave Artificial neural network |
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