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,NH4+、SO42-、NO3-、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。
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.
出处
《腐蚀与防护》
CAS
北大核心
2010年第11期837-839,855,共4页
Corrosion & Protection
基金
国家"863"计划资助项目(2004AA602210-2)
关键词
神经网络
腐蚀速率
酸雨
碳钢
neural network
corrosion rate
acid rain
carbon steel
