PLS方法應用于PTA脫水塔的軟測量建模

化工論文編號:HG131  論文字數:19938,頁數:45

目  錄
中文摘要 I
Abstract II
目錄 III
1. 緒論 1
 1.1 研究背景 1
 1.2  軟測量建模方法研究現狀 2
 1.3 PTA溶劑脫水塔工藝概述 6
 1.4 論文內容安排 8
2. 實驗部分 9
 2.1 模型說明與樣本數據分析 9
 2.2 試驗方式與性能評價指標 9
 2.3 樣本數據特征分析 10
 2.4 多元線性回歸建模（MLR） 12
 2.5 PCA_NN神經網絡的軟測量建模 14
 2.6 偏最小二乘回歸建模（PLSR） 18
 2.7 PLSR_NN神經網絡建模 21
     2.7.3 小結 26
 2.8 不同因素對PLSR_NN模型影響 27
3. 結果與討論 30
 3.1 實驗樣本數據分析及討論   30
 3.2 多元線性回歸方法建模結果及討論 30
 3.3 PCA_NN方法建模及討論 31
 3.4 偏最小二乘回歸方法建模結果及討論 32
 3.5 PLSR_NN方法建模結果及討論 33
 3.6 不同主元數對PLSR_NN模型影響的討論 34
 3.7 節點數對PLSR_NN模型影響的討論 35
 3.8 四種方法建模效果討論 37
4．總結與展望 38
致謝 40
參考文獻 41

摘 要：及時測定化工過程變量，對確保生產過程穩定、有效控制產品質量具有重要意義。目前基于實驗測定的分析，時間滯后明顯，不能實現產品質量精確控制的要求，而基于過程參數的軟測量模型，通過采集實時樣本數據，構建其與性質參數間的經驗數學模型，可實時用于產品質量的在線分析和控制。本論文將基于PTA裝置溶劑脫水塔化工過程采集的時序樣本數據，并考慮數據屬高維強相關的小樣本，擬采用有偏估計的偏最小二乘回歸方法，為塔頂排出的醋酸含量建立軟測量模型，并將用其它經典軟測量建模方法，以作比較。

關鍵詞：軟測量；偏最小二乘；PTA溶劑脫水塔；回歸；主成份回歸

Abstract: It is well known that to measure and estimate the chemical process variables in time has vital significance in ensuring process stabilization and effectively controlling its product quality.Based on the experimental determination of the present analysis, the time lag obviously, can not be achieved precise control of the product quality requirements, and process parameters based on the soft measurement model, sample real-time data acquisition and building parameters and the nature of their experience mathematical model can be used for real-time On-line analysis of product quality and control. The internship will be based on the PTA plant solvent dehydration tower chemical process data collected samples of the timing, and taking into account the high-dimensional data-related small sample, to be adopted biased estimate of the partial least squares regression method for the solution of acetic acid from the top in the establishment of soft measurement model, and with other classical soft measurement modeling method for comparison.
Keywords：Soft-sensing; Partial least squares regression; PTA solvent dehydration tower; Regression; Principle component regression