Mashhadimoslem Hossein, Ghaemi Ahad, Palacios Adriana
School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, 72810, Iran.
Department of Chemical, Food and Environmental Engineering, Fundacion Universidad de las Americas, Puebla, 72810, Mexico.
Heliyon. 2020 Nov 18;6(11):e05511. doi: 10.1016/j.heliyon.2020.e05511. eCollection 2020 Nov.
Fires are important responsible factors to cause catastrophic events in the process industries, whose consequences usually initiate domino effects. The artificial neural network has been shown to be one of the rapid methods to simulate processes in the risk analysis field. In the present work, experimental data points on jet fire shape ratios, defined by the 800 K isotherm, have been applied for ANN development. The mass flow rates and the nozzle diameters of these jet flames have been considered as input dataset; while, the jet flame lengths and widths have been collected as output dataset by the ANN models. A Bayesian Regularization algorithm has been chosen as the three-layer backpropagation training from Multi-layer perceptron algorithm. Then it has been compared with a Radial based functions algorithm, based on single hidden layer. The optimized number of neurons in the first and second hidden layers of the MLP algorithm, and in the single hidden layer of the RBF algorithm has been found to be twenty and fifteen, respectively. The best MSE validation performance of MLP and RBF networks has been found to be 0.00286 and 0.00426 at 100 and 20 epochs, respectively.
火灾是导致过程工业中灾难性事件的重要责任因素，其后果通常会引发多米诺效应。人工神经网络已被证明是风险分析领域中模拟过程的快速方法之一。在本工作中，由800K等温线定义的喷射火焰形状比的实验数据点已被用于人工神经网络的开发。这些喷射火焰的质量流率和喷嘴直径被视为输入数据集；而喷射火焰的长度和宽度则被人工神经网络模型收集为输出数据集。已选择贝叶斯正则化算法作为来自多层感知器算法的三层反向传播训练。然后将其与基于单隐藏层的径向基函数算法进行比较。发现多层感知器算法的第一和第二隐藏层以及径向基函数算法的单隐藏层中的最佳神经元数量分别为20和15。多层感知器和径向基函数网络的最佳均方误差验证性能分别在100和20个epoch时为0.00286和0.00426。
