Research on corrosion prediction model of submarine pipeline based on GWO-LSSVM algorithm

Objective Aiming at the problems of information superposition and mutual coupling of submarine pipeline corrosion factors, complex action mechanisms, and difficult corrosion rate prediction, this article proposes a corrosion rate prediction new model of gray wolf optimization(GWO) algorithm optimized least square support vector machine (LSSVM).

Methods The model uses the gray wolf optimization algorithm to iteratively optimize the kernel parameters and penalty factors of the least squares support vector machine to reduce the blindness of parameter selection and improve the prediction accuracy. The model is applied to 50 sets of samples of seawater coupon corrosion experiment. The learning and prediction are carried out, and the prediction accuracy is compared with traditional least square support vector machine and particle swarm optimization minimum support vector machine.

Results The average absolute error, mean square error, and root mean square error of the gray wolf optimized least squares support vector machine are all smallest, and the coefficient of determination is closer to 1, which indicate that the prediction result of the model is closest to the real value, and the algorithm efficiency is high.

Conclusions The model constructed in this article can be used in the current corrosion prediction driven by big data in oil and gas engineering, and the results can provide a decision-making technical support for the corrosion and protection of submarine pipelines.