Application of IAFSA-GRNN in CO2 corrosion rate prediction of oil gathering and transportation pipelines

doi:10.16265/j.cnki.issn1003-3033.2022.01.015

Abstract

Abstract:

In order to achieve more accurate prediction of CO₂ corrosion rate of oil gathering and transportation pipelines, considering the low prediction accuracy of original GRNN, an improved swarm intelligence algorithm was proposed to optimize it. Then, GRNN model, AFSA optimized GRNN (AFSA-GRNN) model and adaptive improved AFSA-GRNN (IAFSA-GRNN) model were used to predict CO₂ corrosion rate of X65 pipeline steel. The results show that the prediction accuracy of GRNN model is greatly improved by using AFSA and IAFSA to optimize smoothing factor S, with its mean relative error of prediction results being reduced from 36.09% to 7.20% and 6.90%, respectively. Compared with AFSA, IAFSA optimized GRNN has not only higher prediction accuracy, but also faster convergence rate. The AFSA-GRNN converges at the 164th iteration, while the IAFSA-GRNN does that at the 109th one, indicating that AFSA could improve convergence rate of optimization process and further improve prediction accuracy of GRNN through adaptive optimization.

Key words: artificial fish swarms algorithm (AFSA), general regression neural network (GRNN), gathering and transportation pipelines, CO₂ corrosion rate, prediction accuracy, relative error

ZHENG Dukui, CHENG Yuanpeng, LI Haoran, HE Tianlong. Application of IAFSA-GRNN in CO₂ corrosion rate prediction of oil gathering and transportation pipelines[J]. China Safety Science Journal, 2022, 32(1): 110-117.

Figures/Tables 7

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Application of IAFSA-GRNN in CO₂ corrosion rate prediction of oil gathering and transportation pipelines

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模型	训练样本的平均相对误差	测试样本的平均相对误差
GRNN	63.72	36.09
AFSA-GRNN	1.24	7.20
IAFSA-GRNN	0.58	6.90