Please use this identifier to cite or link to this item:
http://ir.library.ui.edu.ng/handle/123456789/2573
Title: | Artificial Neural Network Modeling for Al-Zn-Sn sacrificial anode protection of low carbon steel in saline media |
Authors: | Oluwole, O. Idusuyi, N. |
Keywords: | Cathodic Protection Artificial Neural Networks Sacrificial Anodes |
Issue Date: | 2012 |
Abstract: | This work presents the artificial neural network(ANN) modeling for sacrificial anode cathodic protection of low carbon steel using Al-Zn-Sn alloys anodes in saline media. Corrosion experiments were used to obtain data for developing a neural network model. The Feed forward Levenberg-Marquadt training algorithm with passive time, pH, conductivity,% metallic composition used in the input layer and the corrosion potential measured against a silver/silver chloride(Ag/AgCl) reference electrode used as the target or output variable. The modeling results obtained show that the network with 4 neurons in the input layer, 10 neurons in the hidden layer and 1 neuron in the output layer had a high correlation coefficient (R-value) of 0.850602 for the test data, and a low mean square error (MSE) of 0.0261294. 9 |
URI: | http://ir.library.ui.edu.ng/handle/123456789/2573 |
ISSN: | 2162-9382 2162-8424 |
Appears in Collections: | scholarly works |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
(58)ui_art_oluwole_artificial_2012.pdf | 843.45 kB | Adobe PDF | View/Open |
Items in UISpace are protected by copyright, with all rights reserved, unless otherwise indicated.