Prediction Method of Wax Deposition Rate in Crude Oil Pipeline Based on RBF Neural Network and Support Vector Machine

摘要

Wax-bearing crude oil will precipitate wax crystals in pipeline transportation, which will cause hidden dangers and affect the economic benefits of the pipeline. In order to study the complex wax deposition on the pipe wall and calculate the wax deposition under other conditions, this paper uses RBF neural network and support vector machine to predict the wax deposition data in Huachi operation area. The results show that the errors of the two methods meet the requirements. Because support vector machine can model and calculate finite samples, it is found that the accuracy of support vector machine is higher.