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Artificial Neural Network Modeling for Al-Zn-Sn sacrificial anode protection of low carbon steel in saline media
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 trainin...
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2012
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| LEADER | 00000njm a2000000a 4500 | ||
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| 001 | oai:repository.ui.edu.ng:123456789/2573 | ||
| 042 | |a dc | ||
| 720 | |a Oluwole, O. |e author | ||
| 720 | |a Idusuyi, N. |e author | ||
| 260 | |c 2012 | ||
| 520 | |a 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 | ||
| 024 | 8 | |a 2162-9382 | |
| 024 | 8 | |a 2162-8424 | |
| 024 | 8 | |a ui_art_oluwole_artificial_2012 | |
| 024 | 8 | |a American Journal of Materials Science 2(3), pp. 62-65 | |
| 024 | 8 | |a http://ir.library.ui.edu.ng/handle/123456789/2573 | |
| 653 | |a Cathodic Protection | ||
| 653 | |a Artificial Neural Networks | ||
| 653 | |a Sacrificial Anodes | ||
| 245 | 0 | 0 | |a Artificial Neural Network Modeling for Al-Zn-Sn sacrificial anode protection of low carbon steel in saline media |