Atmospheric distillation column is one of the most important units in an oil refinery where crude oil is fractioned into its more valuable constituents. Almost all of the state-of-the art online equipment has a time lag to complete the physical property analysis in real time due to complexity of the analyses. Therefore, estimation of the physical properties from online plant data with a soft sensor has significant benefits. In this paper, we estimate the physical properties of the hydrocarbon products of an atmospheric distillation column by support vector regression using Linear, Polynomial and Gaussian Radial Basis Function kernels and SVR parameters are optimized by using a variety of algorithms including genetic algorithm, grid search and non-linear programming. The optimization-based data analytics approach is shown to produce superior results compared to linear regression, the mean testing error of estimation is improved by 5% with SVR 4.01˚C to 3.8˚C.

常压蒸馏塔是炼油厂中最重要的装置之一，在炼油厂中，原油被分馏成更有价值的成分。由于分析的复杂性，几乎所有最先进的在线设备都存在一定的时滞，无法实时完成物理性能分析。因此，使用软传感器根据在线工厂数据估算物理特性具有明显的优势。在本文中，我们通过使用线性，多项式和高斯径向基函数的支持向量回归，通过支持向量回归来估计常压蒸馏塔的烃产物的物理性质，并通过使用多种算法（包括遗传算法，网格搜索和非线性编程。