Corrosion Rate Prediction of Storage Tank Based on Particle Swarm Optimization and Least Squares Support Vector Machine

WANG Minghui; DANG Pengfei; YANG Zhengxin; GONG Bo

doi:10.11973/fsyfh-202408011

Corrosion & Protection > 2024 > 45(8): 71-76. > DOI: 10.11973/fsyfh-202408011

WANG Minghui, DANG Pengfei, YANG Zhengxin, GONG Bo. Corrosion Rate Prediction of Storage Tank Based on Particle Swarm Optimization and Least Squares Support Vector Machine[J]. Corrosion & Protection, 2024, 45(8): 71-76. DOI: 10.11973/fsyfh-202408011

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Corrosion Rate Prediction of Storage Tank Based on Particle Swarm Optimization and Least Squares Support Vector Machine

1.
School of Mechanical and Power Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China
2.
Second Oil Mine Geological Team of Sixth Oil Production Plant, Petrochina Daqing Oilfield, Daqing 163400, China

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Received Date: July 04, 2022

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Abstract

A prediction method for corrosion rate of large storage tanks was proposed based on particle swarm optimization (PSO) algorithm and least squares support vector machine (LSSVM), which utilize the global optimization capability of PSO algorithm to optimize the regularization parameters and kernel parameters of LSSVM. The corrosion rates of storage tanks were predicted by the method, and the prediction accuracy of the model was verified by measured data. The results show that the predicted corrosion rates obtained using PSO-LSSVM were in good agreement with the actual corrosion rates. The mean absolute percentage errors of the predicted results of the tank top, the first tank wall and the tank bottom were 2.265%, 3.077% and 1.18%, the root mean square errors were 0.010%, 0.012% and 0.011%, and the corresponding coefficient of determination were 0.973, 0.982 and 0.976, respectively. So this method can effectively predict the corrosion rates of storage tanks.
- particle swarm optimization (PSO),
- least square support vector machine (LSSVM),
- corrosion rate prediction

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