基于核主成分分析算法的海底管道内腐蚀风险预测
Internal Corrosion Risk Prediction of Submarine Pipeline Based on Kernel Principal Component Analysis
摘 要
采用核主成分分析(KPCA)算法,分别对高斯核支持向量机(SVM-rbf)、多项式核支持向量机(SVM-poly)、线性核支持向量机(SVM-linear)、人工神经网络(ANN)和随机森林(RFR)等算法进行优化,以网格搜索法进行参数寻优,对20条海底管道的最大腐蚀速率进行预测,以均方根误差(RMSE)、平均绝对误差(MAE)和平方相关系数(R2)作为评价指标对优化前后模型的预测效果进行评价。结果表明:优化后各模型的R2值显著提高,最高达0.987 7;KPCA能够降低特征维度,减少噪声干扰,提升模型预测性能;优化后的支持向量机算法对海底管道腐蚀速率预测的准确性较高,能够为海底油气田管道腐蚀的预警与防护提供参考。
支持向量机
随机森林
人工神经网络
H2S/CO2腐蚀
kernel principal component analysis
support vector machine
random forest
artificial neural network
H2S/CO2 corrosion
Abstract
The kernel principal component analysis (KPCA) was used to optimize the algorithms such as gaussian kernel support vector machine (SVM-rbf), poly kernel support vector machine (SVM-poly), linear kernel support vector machine (SVM-linear), artificial neural network (ANN) and random forest (RFR). The maximum corrosion rate of 20 submarine pipelines was predicted by grid search method. The root mean square error (RMSE), mean absolute error (MAE) and squared correlation coefficient (R2) were used as evaluation indexes to evaluate the prediction effect of the model before and after optimization. The results showed that the R2 value of each model was significantly increased after optimization, and the maximum value was 0.987 7. KPCA could reduce both the feature dimension and the noise interference and improve the prediction performance of the model. The optimized support vector machine algorithm had high accuracy in predicting the corrosion rate of submarine pipelines, which could provide a reference for early warning and protection of submarine oil and gas pipeline corrosion.
中图分类号 TG174 DOI 10.11973/fsyfh-202303012
所属栏目 应用技术
基金项目 国家重点研发计划(2020YFB0704501)
收稿日期 2021/4/13
修改稿日期
网络出版日期
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引用该论文: JIA Haiyun,HU Lihua,LI Xiaqiao,QU Zhihao,WANG Zhu,CHANG Wei,ZHANG Lei. Internal Corrosion Risk Prediction of Submarine Pipeline Based on Kernel Principal Component Analysis[J]. Corrosion & Protection, 2023, 44(3): 82
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