Journal
CORROSION SCIENCE
Volume 51, Issue 2, Pages 349-355Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.corsci.2008.10.038
Keywords
Steel; Modelling studies; Alkaline corrosion
Funding
- Program for New Century Excellent Talents in University of China [NCET-07-0903]
- SRF or OCS, EM, Chongqing Natural Science Foundation [2006BB5240]
- Innovation Experimental Program for National Undergraduate Students in China [CQUCX-G-2007-016]
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The support vector regression (SVR) approach combined with particle swarm optimization (PSO) for its parameter optimization is proposed to establish a model for prediction of the corrosion rate of 3C steel under five different seawater environment factors, including temperature, dissolved oxygen. salinity, pH value and oxidation-reduction potential. The prediction results strongly support that the generalization ability of SVR model consistently surpasses that of back-propagation neural network (BPNN) by applying identical training and test samples. The absolute percentage error (APE) of 80.43% test samples out of 46 samples does not exceed 1% such that the best prediction result was provided by leave-one-out cross validation (LOOCV) test of SVR. These suggest that SVR may be a promising and practical methodology to conduct a real-time corrosion tracking of steel surrounded by complicated and changeable seawater. (C) 2008 Elsevier Ltd. All rights reserved.
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