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

摘要：

由于常减压蒸馏过程的复杂多变性，过程变量耦合严重，直接建模会增加问题分析的难度。为了提高模型性能，首先采用核主元分析（KPCA）算法对模型的变量进行选择，再将经过处理的数据作为高斯过程回归（GPR）模型的输入，采用KPCA?GPR模型建立常压塔塔顶汽油干点的估计模型。该方法可解决不同变量之间的非线性相关性，并且具有灵活的非参数推广及超参数自适应调节等优点，通过计算经验置信区间，不仅可以对汽油干点进行预测估计，还可以做概率解释。仿真结果表明，KPCA?GPR模型取得了较好的估计结果。

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