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Prediction of BLEVE mechanical energy by implementation of artificial neural network
پیش بینی انرژی مکانیکی BLEVE با اجرای شبکه عصبی مصنوعی-2020 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. Keywords: BLEVE | Vessel explosion | Explosion energy | Blast overpressure | Pressure wave | Artificial neural network |
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