基于LASSO-WOA-LSSVM的海洋管线外腐蚀速率预测

目的 构建海洋管线外腐蚀速率预测模型,提高海底油气管线外腐蚀速率预测的准确性.方法 建立基于套索(LASSO)回归和鲸鱼优化算法(WOA)的最小二乘支持向量机(LSSVM)腐蚀速率预测模型,采用LASSO回归方法对指标进行筛选,提取海洋管线腐蚀的主要影响因素.应用最小二乘支持向量机算法建立海洋管线外腐蚀速率预测模型,并使用鲸鱼优化算法对模型参数进行优化,避免了参数取值对模型回归性能的影响.以海洋挂片实验为例,通过MATLAB进行模拟仿真,分析验证模型预测结果,并将预测结果与其他模型进行对比分析.结果 LASSO回归算法筛选得到影响腐蚀速率的主要因素为:温度、溶解氧含量、pH值.采用WOA-LSSVM模型所预测的结果与实际值较为吻合,其平均相对误差为2.23％,均方根误差(RMSE)为0.3248,决定系数R2达到0.9708,均优于其他两种模型.结论 基于LASSO回归和鲸鱼优化算法的最小二乘支持向量机预测模型具有更优的泛化能力和预测精度,为海底管道腐蚀研究工作提供了新思路,也为海洋油气输送系统的结构安全与风险防范提供了参考.

Prediction of External Corrosion Rate of Marine Pipelines Based on LASSO-WOA-LSSVM

The paper aims to construct a prediction model for the external corrosion rate of marine pipelines and improve the accuracy of prediction for the corrosion rate of offshore pipelines, the least square support vector machine (LSSVM) corrosion rate prediction model based on LASSO (LASSO) regression algorithm and whale optimization algorithm (WOA) was established. The index was screened by LASSO regression method to extract the main influencing factors of marine pipeline corrosion. The least square support vector machine algorithm is used to establish a prediction model for the corrosion rate of submarine pipelines, and the whale optimization algorithm is used to optimize the model parameters to avoid the influence of parameter values on the regression performance of the model. Based on the experiment of real sea hanging film, the simulation is carried out through MATLAB, and the prediction results of the model are analyzed and verified, and the prediction results are compared with other models. The main factors obtained by LASSO regression algorithm are temperature, dissolved oxygen content and pH value. The prediction results of the adopted WOA-LSSVM model are in good agreement with the actual values. The average relative error is 2.23%, the root mean square error (RMSE) is 0.3248, and the coefficient of determination R2 reaches 0.9708, which are better than the other two models. The prediction model of the least square support vector machine based on LASSO regression and whale optimization algorithm has better generalization ability and prediction accuracy, which provides a new idea for the research of submarine pipeline corrosion, and also provides a reference for the structural safety and risk prevention of marine oil and gas transportation system.