Corrosion Rate Prediction Model for Natural Gas Well Oil Pipes Based on PSO Optimization
Corrosion damage is a common issue in the oil pipes of natural gas wells,presenting a significant threat to production safety.This paper introduces a PCA-PSO-SVR model for predicting corrosion rates in natural gas well oil pipes,incorporating Support Vector Regression(SVR),Principal Component Analysis(PCA),and Particle Swarm Optimization(PSO)algorithms.By leveraging Principal Component Analysis to reduce the dimensionality of evaluation indicators and optimizing key parameters of the Support Vector Machine through the Particle Swarm Optimization algorithm,the model is constructed.Experimental findings reveal that the model demonstrates strong stability,high accuracy in identification,exceptional predictive precision,and robust generalization capabilities,showing its practical value and significance.
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