Prediction of Flash Points of Petroleum Middle Distillates Using an Artificial Neural Network Model

IF 1.3 4区工程技术 Q3 CHEMISTRY, ORGANIC Petroleum Chemistry Pub Date : 2024-08-13 DOI:10.1134/s0965544124040066

Kahina Bedda

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Abstract

An artificial neural network (ANN) model of a multilayer perceptron-type was developed to predict flash points of petroleum middle distillates. The ANN model was designed using 252 experimental data points taken from the literature. The properties of the distillates, namely, specific gravity and distillation temperatures, were the input parameters of the model. The training of the network was carried out using the Levenberg– Marquardt backpropagation algorithm and the early stopping technique. A comparison of the statistical parameters of different networks made it possible to determine the optimal number of neurons in the hidden layer with the best weight and bias values. The network containing nine hidden neurons was selected as the best predictive model. The ANN model as well as the Alqaheem–Riazi’s model was evaluated for the prediction of flash points by a statistical analysis based on the calculation of the mean square error, Pearson correlation coefficient, coefficient of determination, absolute percentage errors, and the mean absolute percentage error. The ANN model provided higher prediction accuracy over a wide distillation range than the Alqaheem–Riazi’s model. The developed ANN model is a reliable and fast tool for the low-cost estimation of flash points of petroleum middle distillates.

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利用人工神经网络模型预测石油中间馏分的闪点

摘要开发了一种多层感知器型人工神经网络（ANN）模型，用于预测石油中间馏分的闪点。ANN 模型是利用文献中的 252 个实验数据点设计的。馏分油的特性，即比重和馏分温度，是模型的输入参数。使用 Levenberg- Marquardt 反向传播算法和早期停止技术对网络进行了训练。通过比较不同网络的统计参数，确定了具有最佳权值和偏置值的隐层神经元的最佳数量。包含九个隐藏神经元的网络被选为最佳预测模型。通过计算均方误差、皮尔逊相关系数、决定系数、绝对百分比误差和平均绝对百分比误差等统计分析，对 ANN 模型和 Alqaheem-Riazi 模型进行了闪光点预测评估。与 Alqaheem-Riazi 模型相比，ANN 模型在较宽的蒸馏范围内提供了更高的预测精度。所开发的 ANN 模型是低成本估算石油中间馏分闪点的可靠而快速的工具。

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来源期刊

Petroleum Chemistry 工程技术-工程：化工

CiteScore

2.50

自引率

21.40%

发文量

102

审稿时长

6-12 weeks

期刊介绍： Petroleum Chemistry (Neftekhimiya), founded in 1961, offers original papers on and reviews of theoretical and experimental studies concerned with current problems of petroleum chemistry and processing such as chemical composition of crude oils and natural gas liquids; petroleum refining (cracking, hydrocracking, and catalytic reforming); catalysts for petrochemical processes (hydrogenation, isomerization, oxidation, hydroformylation, etc.); activation and catalytic transformation of hydrocarbons and other components of petroleum, natural gas, and other complex organic mixtures; new petrochemicals including lubricants and additives; environmental problems; and information on scientific meetings relevant to these areas. Petroleum Chemistry publishes articles on these topics from members of the scientific community of the former Soviet Union.