Modeling of corrosion rate under two phase flow in horizontal pipe using neural network

Abstract EN

The present study develops an artificial neural network (ANN) to model an analysis and a simulation of the correlation between the average corrosion rate carbon steel and the effective parameter Reynolds number (Re), water concentration (Wc) % temperature (T) with constant of PH 7 .

The water, produced fom oil in Kirkuk oil field in Iraq from well no.

k184-Depth2200ft., has been used as a corrosive media and specimen area (400 mm2) for the materials that were used as low carbon steel pipe.

The pipes are supplied by Doura Refinery.

The used flow system is all made of Q.

V.

F glass, and the circulation of the two –phase (liquid – liquid ) is affected using a Q.

V.

F pump .The input parameters of the model consists of Reynolds number , water concentration and temperature.

The output is average corrosion rate .The performance of the two training algorithms, gradient descent with momentum and Levenberg-Marquardt, are compared to select the most suitable training algorithm for corrosion rate model.

The model can be used to calculate the average corrosion rate properties of carbon steel alloy as functions of Reynolds number, water concentration and temperature.

Accordingly, the combined influence of these effective parameters and the average corrosion rate is simulated.

The results show that the corrosion rate increases with the increase of temperature, Reynolds number and the increase of water concentration.