Azizi, S., Karimi, H. (2015). An Artificial Neural Network Model for Predicting the Pressure Gradient in Horizontal Oil–Water Separated Flow. Journal of Chemical and Petroleum Engineering, 49(2), 131-141. doi: 10.22059/jchpe.2015.1808

Sadra Azizi; Hajir Karimi. "An Artificial Neural Network Model for Predicting the Pressure Gradient in Horizontal Oil–Water Separated Flow". Journal of Chemical and Petroleum Engineering, 49, 2, 2015, 131-141. doi: 10.22059/jchpe.2015.1808

Azizi, S., Karimi, H. (2015). 'An Artificial Neural Network Model for Predicting the Pressure Gradient in Horizontal Oil–Water Separated Flow', Journal of Chemical and Petroleum Engineering, 49(2), pp. 131-141. doi: 10.22059/jchpe.2015.1808

Azizi, S., Karimi, H. An Artificial Neural Network Model for Predicting the Pressure Gradient in Horizontal Oil–Water Separated Flow. Journal of Chemical and Petroleum Engineering, 2015; 49(2): 131-141. doi: 10.22059/jchpe.2015.1808

An Artificial Neural Network Model for Predicting the Pressure Gradient in Horizontal Oil–Water Separated Flow

^{}Department of Chemical Engineering, Yasouj University, Yasouj, I. R. Iran

Abstract

In this study, a three–layer \ artificial neural network (ANN) model was developed to predict the pressure gradient in horizontal liquid–liquid separated flow. A total of 455 data points were collected from 13 data sources to develop the ANN model. Superficial velocities, viscosity ratio and density ratio of oil to water, and roughness and inner diameter of pipe were used as input parameters of the network while corresponding pressure gradient was selected as its output. A tansig and a linear function were chosen as transfer functions for hidden and output layers, respectively and Levenberg–Marquardt back–propagation algorithm were applied to train the ANN. The optimal topology of the ANN was achieved with 16 neurons in hidden layer, which made it possible to estimate the pressure gradient with a good accuracy (R^{2}=0.996 &AAPE=7.54%). In addition, the results of the developed ANN model were compared to Al–Wahaibi correlation results (with R^{2}=0.884&AAPE=17.17%) and it is found that the proposed ANN model has higher accuracy. Finally, a sensitivity analysis was carried out to investigate the relative importance of each input parameter on the ANN output. The results revealed that the pipe diameter (D) has the most relative importance (24.43%) on the ANN output, while the importance of the other parameters is nearly the same.

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