Abstract:

The dry point of gasoline on the top of atmospheric tower is closely related to product quality, but it is difficult to measure the gasoline dry point online, and the soft sensor is a technical way to solve the estimation and control prediction of such variables. Due to the complexity of atmospheric and vacuum distillation process, the correlation between the variables increases. In this paper, sparse principal component analysis (SPCA) was introduced into kernel principal component analysis（KPCA） algorithm, and the input variables of the model were selected by sparse kernel principal component analysis（SKPCA） algorithm. The nonlinear dimensionality reduction between data was realized, the principal component structure was simplified, and the sparsity of principal component variables was increased. The selected sparse principal components were used as the input of the least squares support vector machine (LSSVM), and the soft sensor prediction model for the top dry point of atmospheric tower was established. The simulation results show that the SKPCA?LSSVM model has higher prediction accuracy and superior model performance compared with the traditional PCA?LSSVM and KPCA?LSSVM methods.

Key words: Soft sensor, Kernel principal component analysis, Sparse kernel principal component analysis, Least squares support vector machines, Dry point of gasoline

摘要：

常压塔塔顶汽油干点与产品质量密切相关，因为常减压蒸馏工艺流程和变量相关性均复杂，所以汽油干点预测很难在线进行。软测量方法是解决这类变量估计和控制预测问题的一种技术途径。在核主元分析（KPCA）算法中引入稀疏主元分析（SPCA）思想，采用稀疏核主元分析（SKPCA）算法对模型的输入变量进行选择，实现了数据的非线性降维，简化了主元结构，增加了主元变量的稀疏性。将选择的稀疏主成分作为最小二乘支持向量机（LSSVM）的输入，建立常压塔塔顶干点软测量预测模型。仿真结果表明，SKPCA?LSSVM模型相对于传统PCA?LSSVM、KPCA?LSSVM方法具有较高的预测精度和性能优越性。

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