基于LASS0-MBAS-ELM的海底多相流管道CO_(2)内腐蚀速率预测

CO_(2) Internal Corrosion Rate Prediction of Subsea Multiphase Flow Pipelines Based on LASS0-MBAS-ELM

针对海底多相流管道CO_(2)内腐蚀发生频繁,检测难度大的问题,建立基于套索回归(LASSO)和多种群甲虫天牛须优化算法(MBAS)的极限学习机(ELM)预测模型,以提高预测效率及预测精度。以LASSO回归筛选腐蚀影响因素,提取关键指标,降低预测输入维度;采用MBAS对ELM的输入权值及隐层阈值进行修正,避免因随机设置造成的不稳定性。以我国海南东部某海底油气管道的50组数据为例,通过MATLAB模拟仿真,分析预测结果,并与其他两种模型对比。结果表明:温度、pH值、流体流速和CO_(2)分压是影响该类型管道腐蚀的关键因素。LASSO-MBAS-ELM模型的预测结果与实际值拟合度更高,其均方根误差、平均绝对误差及平均绝对百分误差分别为0.089%、0.079%和3.068%,均优于对比模型。所提出的方法在数据有限的情况下,仍具有良好的可靠性和稳定性,为准确掌握海底管道腐蚀状况提供了新的思路;同时为海洋油气运输系统日常运行维护提供了参考依据。

Aiming at the frequent occurrence of CO_(2) corrosion in submarine multiphase flow pipelines and the difficulty of detection,an extreme learning machine(ELM)prediction model based on lasso regression(LASSO)and multi-group beetle beetle optimization algorithm(MBAS)was established to improve the forecasting efficiency and forecasting accuracy.LASSO regression screens corrosion influencing factors,extracts key indicators,and reduces the predictive input dimension;MBAS was used to revise ELM input weights and hidden layer thresholds to avoid instability caused by random settings.Taking 50 sets of data from a submarine oil and gas pipeline in eastern Hainan as an example,MATLAB simulation was used to analyze the prediction results and compare with the other two models.The results show that temperature,pH value,fluid flow rate and CO_(2) partial pressure are the key factors affecting the corrosion of this type of pipeline.The prediction results of the LASSO-MBAS-ELM model have a higher degree of fit with the actual value.The root mean square error,average absolute error and average absolute percentage error are 0.089%,0.079%and 3.068%,respectively,which are better than the comparison model.The proposed method has good reliability and stability even with limited data.It provides a new idea for accurately grasping the corrosion status of submarine pipelines,and at the same time provides a reference basis for the daily operation and maintenance of marine oil and gas transportation systems.