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Application of Artificial Neural Network In Cathodic Protection of Carbon Steel Pipe In Sea Water | ||
| Engineering and Technology Journal | ||
| Article 1, Volume 26, Issue 8, 2008, Pages 907-918 PDF (257.94 K) | ||
| DOI: 10.30684/etj.26.8.2 | ||
| Authors | ||
| Sami Abulnoun Ajeel; Ghalib A. Ali | ||
| Abstract | ||
| The intelligent techniques are used successfully in a broad band of applications one of these applications is the cathodic protection system. Examples of these techniques used in cathodic protection are fuzzy logic and genetic algorithms. The present work aims to use the neural network to predict the minimum current density required in impressed current cathodic protection to protect low carbon steel pipe which have been related previously.[1]. This work deals with choosing the best network architecture for cathodic protection system. This step used multilayer feed forward network four environment variables (concentration C%, temperature T, distance D and pH) as input to identify the minimum current density as output in a feed forward network structure with one hidden layer using the practical results data for the learning process. The best number of neurons in the hidden layer is chosen by trial and error and it is found to be 25 neurons. the decision function used is the tan training algorithm with one variable learning rate. Then, neural network training is done using 25 data samples from the experimental data for the current density in the above four variables conditions. The stopping criterion for training was to obtain a sum square error of 0.001 or read 10000 Epochs. An (SSE) than 0.001 were obtained after 5226 Epochs. Generalization test used 5 data samples taken from the experimental results other than those data samples used in the learning process to check the performance of the neural network on these data. The SSE for these samples was 0.0053 and it shows a good generalization results for our application. The comparison between the actual experimental output and the neural network out put after the learning process are almost identical which indicates that good learning process was achieved. The practical results indicate that neural network system can be used successfully to obtain minimum cathodic protection current density to protect low carbon steel pipes. | ||
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