低碳钢人工神经网络腐蚀模型构建和改进
Development and Improvement of Artificial Neural Network Corrosion Model for Low Carbon Steels
摘 要
为构建碳钢的酸雨腐蚀模型,用Autolab电化学工作站测定了低碳钢Q235和35钢在模拟酸雨溶液中的腐蚀数据,使用该数据建立了3层BP神经网络模型,并使用交互检验方法对模型进行分析和改进.结果表明,NH+4、SO2-4、NO-3、Cl-和pH值为影响酸雨腐蚀行为的主要因素;改进后的模型预测精度更高,可用作酸雨腐蚀研究的数值模拟.
腐蚀速率
酸雨
碳钢
neural network
corrosion rate
acid rain
carbon steel
Abstract
In order to develop the corrosion model of low carbon steels in acid rain,corrosion data of low carbon steels Q235 and 35 in simulated acid rain were obtained by electrochemical work station with the brand of Auto-lab.Based on the data,a three-layer BP neural network model was built and used to forecast the corrosion rate.The cross-verification method was used to analyze and improve the model.The results indicated that the model prediction precision was higher than before,and the model could be used to simulate the corrosion experiment in acid rain.
中图分类号 TG172.3
所属栏目 试验研究
基金项目 国家“863”计划资助项目(2004A A 602210-2)
收稿日期 2010/1/29
修改稿日期 2010/2/15
网络出版日期
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联系人作者闫华(yanh911@163.com)
引用该论文: XIANG Bin,YAN Hua,HU Ge. Development and Improvement of Artificial Neural Network Corrosion Model for Low Carbon Steels[J]. Corrosion & Protection, 2010, 31(11): 837～839
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参考文献
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