Application of LS-SVM on slurry pipeline critical deposition velocity prediction - Yang, Jingzong; Wang, Xiaodong; Wu, Jiande (Chinese Control and Decision Conference, 2016)
As an important parameter in slurry pipeline transportation, the critical deposition velocity has long been the subject of many experts and scholars at home and abroad. However, during slurry pipeline hydraulics design, there are some problems in slurry pipeline critical deposition formulas, such as the various forms of it, the big error of the calculated value and the narrow scope of application in it. Least squares support vector machine (LS-SVM) is a machine learning method based on statistical learning theory, which can avoid many shortcomings of the traditional neural network. Therefore, this paper introduced the least square support vector machine to predict the critical deposition velocity based on the analysis of the main factors that influence it. After the sample test, the simulation results show that this model can achieve good results and the accuracy of predicted results is higher when compared with the empirical formula of representation.