Abstract

In the present study, an artificial neural network (ANN) model and three well-known correlations were used to predict the smoke points of 430 kerosene distillates from their specific gravities and distillation temperatures. The ANN model was developed in MATLAB software, it is a feedforward multilayer perceptron with a single hidden layer. The optimal number of neurons in the hidden layer as well as the best training algorithm and the best values of connection weights and biases were determined by trial and error using the nftool command. The early stopping technique by cross-validation was employed to avoid overfitting of the model. The developed model composed of 17 sigmoid hidden neurons and one linear output neuron was trained with the Levenberg-Marquardt backpropagation algorithm. This model allowed the prediction of smoke points with a coefficient of determination of 0.852, an average absolute deviation of 1.4 mm and an average absolute relative deviation of 6%. Statistical analysis of the results indicated that the prediction accuracy of the ANN model is higher than that of the conventional correlations. Indeed, in addition to its effectiveness, the proposed ANN method for the estimation of smoke points has the advantages of low-cost and easy implementation, as it relies on simple laboratory tests. Thus, the developed ANN model is a reliable tool that can be used in petroleum refineries for fast quality control of kerosene distillates.

中文翻译：

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使用简单的实验室测试预测煤油馏分的烟点：人工神经网络与传统相关性

摘要

在本研究中，使用人工神经网络 (ANN) 模型和三个众所周知的相关性，根据 430 种煤油馏分的比重和蒸馏温度来预测其烟点。ANN模型是在MATLAB软件中开发的，它是一个具有单个隐藏层的前馈多层感知器。使用 nftool 命令通过反复试验确定隐藏层中神经元的最佳数量以及最佳训练算法以及连接权重和偏差的最佳值。采用交叉验证的早期停止技术来避免模型的过度拟合。所开发的模型由 17 个 sigmoid 隐藏神经元和 1 个线性输出神经元组成，并使用 Levenberg-Marquardt 反向传播算法进行训练。该模型可以预测烟点，确定系数为0.852，平均绝对偏差为1.4 mm，平均绝对相对偏差为6%。结果统计分析表明，ANN模型的预测精度高于传统相关模型的预测精度。事实上，除了其有效性之外，所提出的用于估计烟点的 ANN 方法还具有低成本和易于实施的优点，因为它依赖于简单的实验室测试。因此，开发的 ANN 模型是一种可靠的工具，可用于炼油厂对煤油馏分的快速质量控制。