Artificial Neural Network Modeling of Deposition Thickness Through the Pipeline

Abstract

Modeling and prediction of deposition thickness is a key to developing crude oil transportation process. Usually, the heat transfer method has been used to calculate deposition thickness through the pipeline, but this method requires vast information and cumbersome calculations. To eliminate this problem, a feed-forward multilayer perceptron neural network (MLPNN) algorithm has been applied to predict the deposition thickness through the pipeline. We present an optimized one-layer feed-forward neural network using properties of the oil pipeline such as inlet and outlet oil temperatures, environmental (mixture of water and ethylene glycol) temperature, and oil Reynolds’ numbers.

Different networks are considered and trained using 15313 data sets; the accuracy of the proposed network is validated by 5104 testing data sets. Using validating data set, the network that is having the lowest mean square error (MSE) and average absolute relative deviation percent (AARD%) and the highest regression coefficient (R²) is selected as an optimal configuration.

Statistical analysis show that the artificial neural network (ANN) predictions have an excellent agreement (MSE = 9.4949e-006, AARD% = 1.0109 and R² = 0.9999) with the heat transfer method. In the other words, accuracy of the proposed ANN model is approximately equal to the heat transfer method without required too much information and cumbersome calculations.

Keywords:

• Artificial neural network
• crude oil
• deposition thickness
• heat transfer method

Acknowledgments

The authors are grateful to the Shiraz University for supporting this research.

Notes

^a Number of data.

^a Weight connection from the input layer to hidden layer.

^b Weight connection from the hidden layer to output layer.