Abstract Pipeline plays an important role in the oil and gas transportation industry. In recent years, more and more pipeline damages and breakdowns are caused by corrosion, which hurts the normal operation. Accurate prediction of the pipeline’s corrosion rate is of great significance for the pipeline to operate safely and soundly. In this study, a hybrid intelligent algorithm method is proposed to predict the corrosion rate of the multiphase flow pipeline. The proposed model combines support vector regression (SVR), principal component analysis (PCA), and chaos particle swarm optimization (CPSO), named PCA-CPSO-SVR. PCA can reduce the data dimension and screen out the main variables of corrosion influencing factors. CPSO is utilized to optimize the hyperfine parameters in SVR, thus improving the prediction accuracy of the prediction model. The mean absolute error of the proposed model is 0.083, which is 18.6% lower than that of SVR. Compared with five benchmark models including linear regression (LR), artificial neural network (ANN), PCA-genetic algorithm-SVR, PCA-PSO-SVR, and De warred95(OLGA), the proposed model has higher prediction accuracy. According to the above results, PCA-CPSO-SVR has a good performance in the prediction of the corrosion rate of the multiphase flow pipeline.

中文翻译：

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一种预测多相管道内腐蚀率的新混合算法模型

摘要 管道在油气运输行业中发挥着重要作用。近年来，越来越多的管道损坏和故障是由腐蚀引起的，损害了正常运行。准确预测管道腐蚀速率对于管道安全、健康运行具有重要意义。在这项研究中，提出了一种混合智能算法方法来预测多相流管道的腐蚀速率。所提出的模型结合了支持向量回归（SVR）、主成分分析（PCA）和混沌粒子群优化（CPSO），命名为PCA-CPSO-SVR。PCA可以降低数据维度，筛选出腐蚀影响因素的主要变量。利用CPSO对SVR中的超精细参数进行优化，从而提高预测模型的预测精度。所提模型的平均绝对误差为 0.083，比 SVR 低 18.6%。与线性回归(LR)、人工神经网络(ANN)、PCA-遗传算法-SVR、PCA-PSO-SVR、De warred95(OLGA)等5个基准模型相比，该模型具有更高的预测精度。根据以上结果，PCA-CPSO-SVR在多相流管道腐蚀速率预测中具有良好的性能。