In this study, pressure drop for oil–water flow in horizontal pipes is represented by using artificial neural network (ANN). Results were compared with Al-Wahaibi correlation and Two-fluid model. This research has used a multilayer feed forward network with Levenberg Marquardt back propagation training for prediction of pressure drop. Original data were divided into two parts where 80% of data was used as training data and remaining 20% of data was used for testing. In this method inputs are oil superficial velocity, water superficial velocity, ratio of density, ratio of viscosity, diameter of pipe and roughness of the pipe wall. The number of neurons is set on four. The feasibility of ANN, Al-Wahaibi correlation and Two-fluid model has been tested against 11 pressure drop data sources. The average absolute percent error of Al-Wahaibi correlation and two-fluid model are 12.73 and 15.84 while this average for the same systems using neural network is only 6.36.so the ANN is in good agreement with experimental data.
Amooey, A. A. (2016). Prediction of Pressure Drop for Oil–Water Flow in Horizontal Pipes using an Artificial Neural Network System. Journal of Applied Fluid Mechanics, 9(5), 2469-2474. doi: 10.18869/acadpub.jafm.68.236.24072
MLA
A. A. Amooey. "Prediction of Pressure Drop for Oil–Water Flow in Horizontal Pipes using an Artificial Neural Network System". Journal of Applied Fluid Mechanics, 9, 5, 2016, 2469-2474. doi: 10.18869/acadpub.jafm.68.236.24072
HARVARD
Amooey, A. A. (2016). 'Prediction of Pressure Drop for Oil–Water Flow in Horizontal Pipes using an Artificial Neural Network System', Journal of Applied Fluid Mechanics, 9(5), pp. 2469-2474. doi: 10.18869/acadpub.jafm.68.236.24072
VANCOUVER
Amooey, A. A. Prediction of Pressure Drop for Oil–Water Flow in Horizontal Pipes using an Artificial Neural Network System. Journal of Applied Fluid Mechanics, 2016; 9(5): 2469-2474. doi: 10.18869/acadpub.jafm.68.236.24072
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