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Author: search.filters.author.Bedda, KahinaHas files: No

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    Prediction of Flash Points of Petroleum Middle Distillates Using an Artificial Neural Network Model
    (Pleiades Publishing, 2024) Bedda, Kahina
    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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    Estimation of net heat of combustion of light kerosene distillates using artificial neural networks
    (Pleiades Publishing, 2024) Bedda, Kahina
    In this study, six feedforward neural network models were developed to estimate the net heat of combustion of light kerosene distillates. These networks use different sets of physicochemical properties of the distillates as input variables and are all composed of 8 sigmoid hidden neurons and one linear output neuron. The networks were designed in MATLAB software with 205 data points using the nftool command. Determining the relative importance of input variables in the networks revealed the significant effect of density on the estimates. The developed models as well as two correlative methods taken from the literature were used to predict the net heat of combustion of 40 other samples. The statistical analysis of the results was carried out by calculating for each estimation method the absolute errors, the mean absolute error, the standard deviation of the absolute errors and the coefficient of determination. It was found that the most accurate method is the neural network model based on the density, viscosity, aromatics content and sulfur content of the distillates. The least efficient method is the neural network that does not include density in its inputs, which once again indicates the importance of this property. Consequently, density should be taken into account to ensure high prediction ability of estimation methods.