摘要
针对输油管道易发生腐蚀问题,建立了遗传算法(GA)优化反向传播神经网络(BPNN)的输油管道内腐蚀速率预测模型,给出了具体的优化流程。运用GA优化BPNN模型的起始权值和阈值,有效避免了单一BPNN模型陷入局部最优的问题发生,从而提升了预测的准确率。以某条输油管线为例,对改进的GA-BPNN模型进行验证和分析,结果表明:BPNN模型预测的最高相对误差高达24.49%,平均相对误差为11.13%。相较于BPNN模型,GA-BPNN模型的预测精度有了较大幅度地提高,最大相对误差仅为8.16%,平均相对误差为3.10%。因此使用GA-BPNN模型预测管道腐蚀情况可为管道的检维修提供可靠的理论依据。
Aiming at the problem that corrosion is easy to occur,in oil pipelines,a genetic algorithm( GA)optimized back propagation neural network( BPNN) is used to predict the corrosion rate of pipelines in oil pipelines. The specific optimization process is given. Using GA to optimize the starting weight and threshold of BPNN model,it effectively avoids the problem that a single BPNN model falls into local optimum,which improves the accuracy of prediction. Taking an oil pipeline as an example,the improved GA-BPNN model is verified and analyzed. The results show that the highest relative error predicted by BPNN model is as high as 24. 49 %,and the average relative error is 11. 13 %. Compared with the BPNN model,the prediction accuracy of the GA-BPNN model has been greatly improved,the maximum relative error is only 8. 16 %,and the average relative error is3. 10 %. Therefore,using GA-BPNN model to predict pipeline corrosion can provide a reliable theoretical basis for pipeline inspection and maintenance.
作者
凌晓
徐鲁帅
余建平
梁瑞
LING Xiao;XU Lushuai;YU Jianping;LIANG Rui(College of Petroleum and Chemical Engineering,Lanzhou University of Technology,Lanzhou 730000,China)
出处
《传感器与微系统》
CSCD
北大核心
2021年第2期124-127,共4页
Transducer and Microsystem Technologies
基金
甘肃省重点研发计划资助项目(1604GKCA022)。
关键词
反向传播神经网络
遗传算法
管道腐蚀
腐蚀速率
输油管道
back propagation neural network(BPNN)
genetic algorithm(GA)
pipeline corrosion
corrosion rate
oil pipeline
