基于辛烷值调合效应模型的在线更新方法及其应用

主要介绍了用于汽油管道调合过程中的辛烷值调合效应模型及模型的在线更新方法。由于汽油辛烷值属性在调合过程中表现出强非线性,使用线性的调合模型会造成较大的预测误差。虽然经典的乙基模型为非线性模型,但是在建模后期或者后期模型维护过程中需要大量的数据,需要做调合实验,往往工作量太大,导致调合工况发生改变时模型很难快速适应。本文提出的基于辛烷值调合效应模型的在线更新方法是以辛烷值调合效应模型为基础,采用递推最小二乘的方法对模型参数进行在线实时更新,从而保证调合效应模型在较大的工况范围内都有效。该方法原理简单、计算速度快,可以适用于不同牌号的汽油调合,已取得了良好的应用效果。     

A New Sample-Selection and Modeling Method Based on Near-Infrared Spectroscopy and Its Industrial Application

Near-infrared （NIR） spectroscopy has been widely employed as a process analytical tool （PAT） in various fields; the most important reason for the use of this method is its ability to record spectra in real time to capture process properties. In quantitative online applications, the robustness of the established NIR model is often deteriorated by process condition variations, nonlinear of the properties or the high-dimensional of the NIR data set. To cope with such situation, a novel method based on principal component analysis （PCA） and artificial neural network （ANN） is proposed and a new sample-selection method is mentioned. The advantage of the presented approach is that it can select proper calibration samples and establish robust model effectively. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional partial leastsquares （PLS） , principal component regression （PCR） and several other modeling methods, the proposed approach was found to achieve good accuracy in the prediction of gasoline properties. An application of the proposed method is also reported.     

基于随机化分析的等价空间故障检测方法

基于等价空间的故障检测可实现残差对控制输入与未知状态的解耦,已成为提高系统安全性与可靠性的重要技术手段。针对难以同时降低故障漏报率与误报率的问题,提出一种随机化分析辅助的等价空间故障检测方法。首先,在未知输入概率分布未知的情况下,设计基于等价空间的残差产生器,并应用Chernoff界引理,给出故障误报率的随机化分析方法。其次,针对零故障误报率阈值选取的强保守性问题,给出一定故障误报率容许条件下的阈值选取方法,实现了故障误报率与可检测率的均衡设计。最后以某型无人机飞行控制系统进行仿真实验,验证了提出方法的有效性。