关键词: 软测量, 核主成分分析, 稀疏核主成分分析, 最小二乘支持向量机, 汽油干点

Abstract:

Due to the complexity and variability of atmospheric and vacuum distillation process, the coupling between process variables is serious, and the direct modeling will increase the difficulty of problem analysis. In order to improve the performance of the model, KPCA algorithm was used to select the variables of the model, and then the processed data were used as the input of the Gaussian process regression (GPR) model, and KPCA?GPR was used to establish the estimation model of the gasoline dry point on the atmospheric tower roof. The method solves the strong nonlinear correlation between different variables, and has the advantages of flexible nonparametric generalization and super?parameter adaptive adjustment. By calculating the empirical confidence interval, not only can the dry point of gasoline be predicted and estimated, but also can do the probability interpretation. The simulation results show that the KPCA?GPR method achieves better estimation results.

Key words: Soft sensor, Kernel principal component analysis, Sparse kernel principal component analysis, Least squares support vector machine, Gasoline dry point