基于分片线性近似方法的煤油干点估计

Inferential estimation of kerosene dry point via piecewise linear approximation

在流程工业中,煤油干点的估计对于产品质量控制有着重要的意义。因为原油组分时常变化,目前还不存在一种通用有效的模型来适应这些变化所带来的不同特性。本文引入自适应链接超平面(AHH)这一新颖的连续分片线性表示方法,建立了软测量模型。干点估计的仿真测试结果显示了该模型具有良好的拟合能力。而在实际数据上的测试表明,自适应链接超平面的模型在表达能力上优于已有的基于分类的建模方法。由于利用AHH的估计方法可以适应多变的生产工况,因而对于其他具有复杂多变工况的化工过程中具有推广价值。

The estimation of kerosene dry point is important for quality control in process industry.Since the crude oil changes with varying compositions,there does not exist any effective model to cover different inherent characteristics.In this paper,adaptive hinging hyperplane （AHH）,a novel piecewise linear method is introduced to build soft sensor model.A dry point estimation simulating experiment shows the good result.And the test on practical data reveals that AHH method has a better performance than the existing models based on classification information.It is expected that the method can be extended to other chemical process estimation with varying situations.