Artificial Neural Network Modeling for Al-Zn-Sn sacrificial anode protection of low carbon steel in saline media
| dc.contributor.author | Oluwole, O. | |
| dc.contributor.author | Idusuyi, N. | |
| dc.date.accessioned | 2018-10-16T11:47:40Z | |
| dc.date.available | 2018-10-16T11:47:40Z | |
| dc.date.issued | 2012 | |
| dc.description.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 | en_US |
| dc.identifier.issn | 2162-9382 | |
| dc.identifier.issn | 2162-8424 | |
| dc.identifier.other | ui_art_oluwole_artificial_2012 | |
| dc.identifier.other | American Journal of Materials Science 2(3), pp. 62-65 | |
| dc.identifier.uri | http://ir.library.ui.edu.ng/handle/123456789/2573 | |
| dc.language.iso | en | en_US |
| dc.subject | Cathodic Protection | en_US |
| dc.subject | Artificial Neural Networks | en_US |
| dc.subject | Sacrificial Anodes | en_US |
| dc.title | Artificial Neural Network Modeling for Al-Zn-Sn sacrificial anode protection of low carbon steel in saline media | en_US |
| dc.type | Article | en_US |