Corrosion is an important factor leading to the failure of subsea process operations especially subsea crude oil pipelines. Developing a data-driven corrosion prediction model is urgently required by the digitization of subsea process system in the industry 4.0 environment, which is critical to improve the intelligent level of risk management of subsea process system. This paper proposed a new data-driven model based on hybrid techniques to model corrosion degradation of subsea operations. The model is built integrating three data-driven methods: principal component analysis (PCA), artificial bee colony algorithm (ABC) and support vector regression (SVR). The developed model is tested on the corrosion rate prediction of subsea crude oil pipelines. This model can realize effective prediction of corrosion rate. In the proposed hybrid model, PCA is used to reduce the dimension of corrosion influencing factors. The obtained principal components are selected as the input variables of the model. The ABC algorithm is adopted to optimize the hyper-parameters of the SVR. The model is trained using fraction of the historical data; subsequently, the model performance is tested on the remaining set of the data. A case study demonstrates the feasibility and effectiveness of the proposed model. The model is compared with the four different models SVR, PCA-SVR, PCA-GA-SVR, PCA-PSO-SVR. The PCA-ABC-SVR model performed superior in terms of prediction accuracy and robustness of results (MAE = 7.10 %; RMSE = 9.19 %; R² = 0.976). The proposed model will serve as a useful online tool to support safety and digitization of process system.

腐蚀是导致海底加工操作尤其是海底原油管道故障的重要因素。工业4.0环境下海底过程系统数字化迫切需要开发数据驱动的腐蚀预测模型，这对于提高海底过程系统风险管理的智能化水平至关重要。本文提出了一种基于混合技术的新数据驱动模型来模拟海底作业的腐蚀退化。该模型集成了三种数据驱动方法：主成分分析（PCA）、人工蜂群算法（ABC）和支持向量回归（SVR）。开发的模型在海底原油管道的腐蚀速率预测上进行了测试。该模型可以实现对腐蚀速率的有效预测。在提出的混合模型中，PCA用于降低腐蚀影响因素的维度。选取得到的主成分作为模型的输入变量。采用ABC算法对SVR的超参数进行优化。该模型使用历史数据的一小部分进行训练；随后，在剩余的数据集上测试模型性能。案例研究证明了所提出模型的可行性和有效性。该模型与四种不同的模型 SVR、PCA-SVR、PCA-GA-SVR、PCA-PSO-SVR 进行了比较。PCA-ABC-SVR 模型在预测精度和结果稳健性方面表现优异（采用ABC算法对SVR的超参数进行优化。该模型使用历史数据的一小部分进行训练；随后，在剩余的数据集上测试模型性能。案例研究证明了所提出模型的可行性和有效性。该模型与四种不同的模型 SVR、PCA-SVR、PCA-GA-SVR、PCA-PSO-SVR 进行了比较。PCA-ABC-SVR 模型在预测精度和结果稳健性方面表现优异（采用ABC算法对SVR的超参数进行优化。该模型使用历史数据的一小部分进行训练；随后，在剩余的数据集上测试模型性能。案例研究证明了所提出模型的可行性和有效性。该模型与四种不同的模型 SVR、PCA-SVR、PCA-GA-SVR、PCA-PSO-SVR 进行了比较。PCA-ABC-SVR 模型在预测精度和结果稳健性方面表现优异（MAE = 7.10%；均方根误差= 9.19%；R ² = 0.976）。提议的模型将作为一种有用的在线工具来支持过程系统的安全和数字化。