發(fā)布時(shí)間：2018-09-12 15:07

【摘要】：針對(duì)工業(yè)過(guò)程的非線性及動(dòng)態(tài)特性,提出了一種新的慢特征回歸軟測(cè)量方法。該方法首先通過(guò)添加時(shí)延數(shù)據(jù)構(gòu)造動(dòng)態(tài)數(shù)據(jù)集,利用互信息最大化準(zhǔn)則篩選變量從而減少信息冗余的影響。同時(shí)該方法在慢特征分析的基礎(chǔ)上引入核函數(shù)擴(kuò)展,加強(qiáng)模型處理非線性數(shù)據(jù)的能力,并將獲得的核慢特征用于回歸建模。核慢特征分析通過(guò)分析樣本的變化,提取具有緩慢變化特征的成分,可以有效地刻畫(huà)工業(yè)過(guò)程的變化趨勢(shì),提升回歸模型精度。最后該方法的有效性在常壓塔常頂油干點(diǎn)與常一線初餾點(diǎn)的軟測(cè)量模型中得到了驗(yàn)證。
[Abstract]:Based on the nonlinear and dynamic characteristics of industrial processes, a new slow feature regression soft sensing method is proposed. In this method, the dynamic data set is constructed by adding delay data, and the influence of information redundancy is reduced by filtering variables by using mutual information maximization criterion. At the same time, the kernel function expansion is introduced on the basis of the slow feature analysis, and the ability of the model to deal with nonlinear data is enhanced, and the kernel slow feature obtained is used in regression modeling. Kernel slow feature analysis can effectively describe the changing trend of industrial process and improve the precision of regression model by analyzing the change of samples and extracting the components with slow changing characteristics. Finally, the effectiveness of the method is verified in the soft sensing model of the dry point and the initial distillation point of the constant top oil in the atmospheric tower.
【作者單位】： 華東理工大學(xué)化工過(guò)程先進(jìn)控制與優(yōu)化技術(shù)教育部重點(diǎn)實(shí)驗(yàn)室;中國(guó)石油天然氣股份有限公司獨(dú)山子石化研究院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(21676086,21406064)~~
【分類號(hào)】：O212.1;TE624.2
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本文編號(hào)：2239412