Please use this identifier to cite or link to this item: http://ir.library.ui.edu.ng/handle/123456789/2573
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dc.contributor.authorOluwole, O.-
dc.contributor.authorIdusuyi, N.-
dc.date.accessioned2018-10-16T11:47:40Z-
dc.date.available2018-10-16T11:47:40Z-
dc.date.issued2012-
dc.identifier.issn2162-9382-
dc.identifier.issn2162-8424-
dc.identifier.otherui_art_oluwole_artificial_2012-
dc.identifier.otherAmerican Journal of Materials Science 2(3), pp. 62-65-
dc.identifier.urihttp://ir.library.ui.edu.ng/handle/123456789/2573-
dc.description.abstractThis 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. 9en_US
dc.language.isoenen_US
dc.subjectCathodic Protectionen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectSacrificial Anodesen_US
dc.titleArtificial Neural Network Modeling for Al-Zn-Sn sacrificial anode protection of low carbon steel in saline mediaen_US
dc.typeArticleen_US
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