【摘要】：精餾是石油化工生產過程中應用最廣泛的操作之一,對精餾設備所作的微小擾動可能會引發(fā)事故,產生巨大的經濟損失。為確保精餾裝置安全、平穩(wěn)運行,及時發(fā)現異常狀態(tài)中參數的劣化趨勢,需要及時識別異常原因,從源頭上預防和控制精餾事故的發(fā)生。精餾過程中存在大量擾動,擾動原因難以確定,擾動量信息診斷精度難以提高�；谝陨蠁栴},本文將物理領域的反演思想應用到精餾擾動診斷領域,建立擾動反演模型,深入分析擾動原因。本文以單、雙變量擾動為例,對精餾過程擾動原因反演問題進行了研究�？紤]動態(tài)精餾過程的非線性與非穩(wěn)定性,結合物料衡算和能量衡算等方程,運用機理建模的方法,建立了精餾塔的動態(tài)數學模型,并動態(tài)模擬出正常、異常樣本。對應地,運用人工神經網絡(ANN)、否定選擇法(NSA)和支持向量機(SVM)方法確定出擾動類型,然后結合遺傳算法(GA)建立了各擾動量與特征表示量的反演模型,通過運算獲得擾動量的大小。考慮到上述診斷方法中人工提取特征所帶來的復雜性和不確定性,本文運用深度學習(DL)對擾動類型進行識別,增強了識別過程的智能性。本文選取脫丙烷生產工藝為研究對象,建立了動態(tài)模擬仿真系統。同時也建立了擾動量的反演模型,診斷出了工藝中常見的擾動原因,對上述所提方法進行了驗證和對比。結果表明,此方法能夠快速定位擾動類型,準確得出擾動量,實現了動態(tài)精餾系統擾動原因的深入辨識。
[Abstract]:Distillation is one of the most widely used operations in the process of petrochemical production. Small disturbances to the distillation equipment may cause accidents and produce huge economic losses. In order to ensure the safety and smooth operation of the distillation unit, the deterioration trend of the parameters in the abnormal state is found in time. It is necessary to identify the abnormal causes in time and to prevent and control from the source. There are a lot of disturbances in the distillation process. The cause of disturbance is difficult to be determined and the accuracy of the disturbance information diagnosis is difficult to be improved. Based on the above problems, this paper applies the inverse thought in the physical field to the field of distillation disturbance diagnosis, establishes a disturbance inversion model, and analyzes the cause of disturbance. This paper takes the single and bivariate perturbation as an example. The problem of the inversion of the cause of the distillation process is studied. Considering the nonlinear and non stability of the dynamic distillation process, combining the equations of material balance and energy balance, the dynamic mathematical model of the distillation column is established by the method of mechanism modeling, and the normal and abnormal samples are simulated dynamically, and the artificial neural network (ANN) is used for the dynamic simulation. The NSA and support vector machine (SVM) method are used to determine the disturbance type, and then the inverse model of the disturbance momentum and the feature representation is established by combining the genetic algorithm (GA). The size of the disturbance is obtained by operation. The depth learning (D) is used in this paper. L) to identify the type of disturbance and enhance the intelligence of the identification process. In this paper, a dynamic simulation system is established by selecting the propane production process as the research object, and the inversion model of the disturbance is also established. The common disturbance causes in the process are diagnosed and the methods are verified and contrasted. The results show that this method is used. The method can locate the disturbance types quickly and get the disturbance quantities accurately, thus realizing the in-depth identification of the disturbance causes of the dynamic distillation system.
【學位授予單位】：青島科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TQ028.31;TQ221.13

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