Abstract
Predication Corrosion rate is quantitative method by which the effectiveness of corrosion control and prevention techniques can be evaluated and provides the feedback to enable corrosion control and prevention methods to be optimized. In this paper, a novel Model to predict corrosion rate based on RBFNN was proposed. A model is produced from experimental work for one year and eighty four specimens were used through this work using anode with a high level of precision. Learning data was performed by using a 36 samples test with different Environment Resistivity (ER), Impressed Current (IC), Location of Anode (LA), Corrosion Current (CC) and Corrosion Rate (CR). The RBFNN model has five input nodes representing the (ER, IC, CC, LN, and SA), sixteen nodes at hidden layer and one output node representing corrosion rate (CR). Simulation test use 6 data samples taken from the experimental results to check the performance of the neural network on these data and shows the proposed model can be use successfully to predicate the corrosion rate.
Highlights
Corrosion control modeling has become integral component of the modern science and researching of complex systems
Much researcher focus on Artificial neural network had been applied in studies of other corrosion fields for several years (H M G Smets, W F L Bogaerts. 1992)(H M G Smets, W F L Bogaerts. 1992)(Y Cheng, W L Huang, C Y Zhou. 1999)(J Leifer, P E Zapp, J I Michalonis. 1999)(J Leifer, J I Mickalonis. 2000)(F H Haynie, J P Upham. 1971)(Jianping Cai, Wei Ke. 1997)(Xiaoyan Ma, Yanbing Luan, Zuyu Qu, et al.. 2001), there is few application in corrosion in seawater
In order to obtain the effects of the studied parameters the impressed current anode was located in the middle and in the end of the cathode according to surface areas 832.1 and 1863.9 cm2 in different states and current demands
Summary
Corrosion control modeling has become integral component of the modern science and researching of complex systems. Artificial Intelligent play important roles in data acquisition in laboratory and field environments, data processing and analysis, data searching and data presentation in understandable and useful formats(H M G Smets, W F L Bogaerts. Much researcher focus on Artificial neural network had been applied in studies of other corrosion fields for several years The corrosion factors, which are the influence of immersion time, dissolved oxygen, temperature and water pressure on corrosion under the sea, are clarified by the results of laboratory experiments. In (Wei You, Yaxiu Liu, 2008) an artificial neural network applied software, the authors developed artificial neural network to predict the corrosion rate of materials in sea water. The model was used to analyze the quantitative effects of environment factors of sea water on the corrosion rate
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