Corrosion Control by Impressed Cathodic Protection Using Intelligent Systems

摘要

The aim of this study is to adopt the Artificial Neural Network (ANN) to Model the Cathodic Protection system (CPS) and evaluation the potential required to protect the coated and bared pipeline as well as to the prediction of corrosion rate. On the other hand, the experimental work was carried out to collect the required data to be used for training and testing the neural network. The objective of this research paper is to corrosion control in the pipeline with different potential values. The proposed structure of ANN for potential and corrosion is an input layer, two hidden layers and one output layer and this structure is arbitrarily selected. The transfer function that has been used in the first hidden layer for each network is the Tan-Sigmoid function and for the second layer is the pure line. The back propagation training algorithm with one variable learning rate is used to train these neural networks. For the potential assessment; the ANN input data includes the distance between anodes and cathodes (D), Current Density (CD), length of pipe from end to the drain point (L), resistivity of solution (p) and the voltage of power stations, while the potential is the network output. For the corrosion rate prediction, the network input information is only time, surface area and resistivity of the soil (solution) (p), while the corrosion rate is the network output. Many networks are constructed by changing the number of neurons for the hidden layers. This has been simulated by using the MATLAB R2009a software. The optimum network for coated pipe was (13) neurons in the first hidden layer and (8) neurons in the second hidden layer which is tested and trained by using (120 data sample). This network has proved to be reliable and can be used to assess the potential required for CPS. Concerning the bared pipe-lines, the collected experimental data is not stable and the fluctuation of the data occurs due to the interference between the corroded part of the pipe and the protected parts, which causes the un-stability of potential. The optimum network for coated pipe was (15) neurons in the first hidden layer and (4) neurons in the second hidden layer) which is tested and trained by using (250 data sample). This network demonstrates to be reliable and capable of predicting the corrosion rate.