Artificial neural network for predication of zinc consumption rate of cathodic protection of copper in saline water: A short communication
The rate of zinc consumption (anode) for cathodic protection of copper pipeline in saline water is forecasted using artificial neural network (ANN). Zinc consumption rate (input dependent variable) is estimated as a function of temperature, flow rate, time and NaCl concentration (output independent...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Results in Chemistry |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211715622000893 |
| _version_ | 1811199012718510080 |
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| author | Anees A. Khadom |
| author_facet | Anees A. Khadom |
| author_sort | Anees A. Khadom |
| collection | DOAJ |
| description | The rate of zinc consumption (anode) for cathodic protection of copper pipeline in saline water is forecasted using artificial neural network (ANN). Zinc consumption rate (input dependent variable) is estimated as a function of temperature, flow rate, time and NaCl concentration (output independent variables). One hundred ANNs are created using STATISTIC 10 software based on the Intelligent Problem Solver tool. Only ten high performance networks are retained. Three types of ANNs are constructed. Linear Model (LM), Multi-Layer Perceptrons (MLP), and Radial Basis Function (RBF). MLP 4:4-7-1:1 with four input variables and one output variable, and three layers of 4, 7, and 1 unit, respectively is the high performance network with a 0.9946 correlation coefficient and 0.0071 absolute errors. The predicted results are in a good agreement with experimental one. It is found that the rate of zinc consumption increases with increasing of all independent variables. Sensitivity analysis showed that the time is the most sensitive variables, while salt concentration, flow rate, and the temperature had the lower effect, respectively. |
| first_indexed | 2024-04-12T01:40:44Z |
| format | Article |
| id | doaj.art-773135a499ef45d7843cea661c14f5ef |
| institution | Directory Open Access Journal |
| issn | 2211-7156 |
| language | English |
| last_indexed | 2024-04-12T01:40:44Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Chemistry |
| spelling | doaj.art-773135a499ef45d7843cea661c14f5ef2022-12-22T03:53:12ZengElsevierResults in Chemistry2211-71562022-01-014100370Artificial neural network for predication of zinc consumption rate of cathodic protection of copper in saline water: A short communicationAnees A. Khadom0Department of Chemical Engineering, College of Engineering, University of Diyala, Baquba City, 32001 Daiyla Governorate, IraqThe rate of zinc consumption (anode) for cathodic protection of copper pipeline in saline water is forecasted using artificial neural network (ANN). Zinc consumption rate (input dependent variable) is estimated as a function of temperature, flow rate, time and NaCl concentration (output independent variables). One hundred ANNs are created using STATISTIC 10 software based on the Intelligent Problem Solver tool. Only ten high performance networks are retained. Three types of ANNs are constructed. Linear Model (LM), Multi-Layer Perceptrons (MLP), and Radial Basis Function (RBF). MLP 4:4-7-1:1 with four input variables and one output variable, and three layers of 4, 7, and 1 unit, respectively is the high performance network with a 0.9946 correlation coefficient and 0.0071 absolute errors. The predicted results are in a good agreement with experimental one. It is found that the rate of zinc consumption increases with increasing of all independent variables. Sensitivity analysis showed that the time is the most sensitive variables, while salt concentration, flow rate, and the temperature had the lower effect, respectively.http://www.sciencedirect.com/science/article/pii/S2211715622000893Cathodic protectionCorrosionArtificial neural networkSensitivity analysis |
| spellingShingle | Anees A. Khadom Artificial neural network for predication of zinc consumption rate of cathodic protection of copper in saline water: A short communication Results in Chemistry Cathodic protection Corrosion Artificial neural network Sensitivity analysis |
| title | Artificial neural network for predication of zinc consumption rate of cathodic protection of copper in saline water: A short communication |
| title_full | Artificial neural network for predication of zinc consumption rate of cathodic protection of copper in saline water: A short communication |
| title_fullStr | Artificial neural network for predication of zinc consumption rate of cathodic protection of copper in saline water: A short communication |
| title_full_unstemmed | Artificial neural network for predication of zinc consumption rate of cathodic protection of copper in saline water: A short communication |
| title_short | Artificial neural network for predication of zinc consumption rate of cathodic protection of copper in saline water: A short communication |
| title_sort | artificial neural network for predication of zinc consumption rate of cathodic protection of copper in saline water a short communication |
| topic | Cathodic protection Corrosion Artificial neural network Sensitivity analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2211715622000893 |
| work_keys_str_mv | AT aneesakhadom artificialneuralnetworkforpredicationofzincconsumptionrateofcathodicprotectionofcopperinsalinewaterashortcommunication |