基于RS-ISOA-KELM的输气管道内腐蚀速率预测方法

Prediction method for internal corrosion rate of gas pipeline based on RS-ISOA-KELM model

【目的】地面输气管道易出现严重的内腐蚀问题,为保障管道服役安全,需准确预测管道内腐蚀速率。【方法】通过粗糙集(Rough Set,RS)理论筛选影响腐蚀的主控因素,将重构数据集作为输入、腐蚀速率作为输出,对核极限学习机(Kernel Based Extreme Learning Machine,KELM)模型进行训练,并利用改进的海鸥优化算法(Improved Seagull Optimization Algorithm,ISOA)对模型超参数进行优化,从而提出了一种基于RS-ISOA-KELM模型的输气管道内腐蚀速率预测方法,与其他组合模型的预测精度进行对比,并进行了长期预测效果及模型普适性分析。【结果】在Sphere、Schaffer、Rosenbrock、Rastrigin、Griewank 5个基准函数上对ISOA算法进行收敛性分析,发现其在求解精度和计算稳定性上具有较强优势;利用某气田区块的实际运行数据对该模型进行验证,结果表明温度、CO_(2)分压、H2S分压、流速、Cl-含量、含水率、缓蚀剂残余浓度是影响内腐蚀的重要因素,其中H2S分压、流速、缓蚀剂残余浓度的权重最大;使用RS-ISOA-KELM模型对腐蚀速率进行预测,其平均相对误差为1.498%、均方根误差为0.0021 mm/a、决定系数为0.9993,优于其他常见的组合模型。【结论】所建组合模型具有强泛化性能和高预测精度,通过对原数据库的扩充和更新,可以实现管道中长期腐蚀速率的预测;在腐蚀参数、数据量、训练测试比均不同的情况下,该模型仍然保持了较好的预测效果。

[Objective]Surface gas transmission pipelines suffer serious internal corrosion.To ensure the service safety of pipelines,it is necessary to accurately predict their internal corrosion rate.[Methods]The main control factors that affect corrosion were screened based on the Rough Set(RS)theory,and the reconstructed data set was used as the input while the corrosion rate was used as the output to train the Kernel Based Extreme Learning Machine(KELM)model.Additionally,the Improved Seagull Optimization Algorithm(ISOA)was used to optimize the model hyperparameters,and a prediction method of internal corrosion rate based on the RS-ISOA-KELM model was proposed.The prediction precision was compared with that of other combination models,and the long-term prediction effect and model universality were analyzed.[Results]The convergence analysis for the ISOA algorithm was conducted on five benchmark functions including Sphere,Schaffer,Rosenbrock,Rastrigin,and Griewank,and it was found that the ISOA algorithm had better performance in terms of solution precision and calculation stability.The model was verified using actual operating data from a gas field block.For the selected data set,the temperature,CO2 partial pressure,H2S partial pressure,flow rate,Cl-content,moisture content,and corrosion inhibitor residual concentration were crucial factors that affect internal corrosion.Among them,H2S partial pressure,flow rate,and corrosion inhibitor residual concentration had the largest weight.The RS-ISOA-KELM model was used to predict the corrosion rate,and its average relative error was 1.498%,root mean square error was 0.0021 mm/a,and coefficient of determination was 0.9993,which were all better than other common comparison models.[Conclusion]The combined model has strong generalization performance and high prediction precision.By expanding and updating the original database,it can predict the medium-and long-term corrosion rate of pipelines.With different corrosion parameters,data sizes,and training test ratios,the model still maintains a good prediction effect.