發(fā)布時(shí)間：2018-08-09 21:11

【摘要】：精餾是石油化工生產(chǎn)過(guò)程中應(yīng)用最廣泛的操作之一,對(duì)精餾設(shè)備所作的微小擾動(dòng)可能會(huì)引發(fā)事故,產(chǎn)生巨大的經(jīng)濟(jì)損失。為確保精餾裝置安全、平穩(wěn)運(yùn)行,及時(shí)發(fā)現(xiàn)異常狀態(tài)中參數(shù)的劣化趨勢(shì),需要及時(shí)識(shí)別異常原因,從源頭上預(yù)防和控制精餾事故的發(fā)生。精餾過(guò)程中存在大量擾動(dòng),擾動(dòng)原因難以確定,擾動(dòng)量信息診斷精度難以提高。基于以上問(wèn)題,本文將物理領(lǐng)域的反演思想應(yīng)用到精餾擾動(dòng)診斷領(lǐng)域,建立擾動(dòng)反演模型,深入分析擾動(dòng)原因。本文以單、雙變量擾動(dòng)為例,對(duì)精餾過(guò)程擾動(dòng)原因反演問(wèn)題進(jìn)行了研究�？紤]動(dòng)態(tài)精餾過(guò)程的非線(xiàn)性與非穩(wěn)定性,結(jié)合物料衡算和能量衡算等方程,運(yùn)用機(jī)理建模的方法,建立了精餾塔的動(dòng)態(tài)數(shù)學(xué)模型,并動(dòng)態(tài)模擬出正常、異常樣本。對(duì)應(yīng)地,運(yùn)用人工神經(jīng)網(wǎng)絡(luò)(ANN)、否定選擇法(NSA)和支持向量機(jī)(SVM)方法確定出擾動(dòng)類(lèi)型,然后結(jié)合遺傳算法(GA)建立了各擾動(dòng)量與特征表示量的反演模型,通過(guò)運(yùn)算獲得擾動(dòng)量的大小�？紤]到上述診斷方法中人工提取特征所帶來(lái)的復(fù)雜性和不確定性,本文運(yùn)用深度學(xué)習(xí)(DL)對(duì)擾動(dòng)類(lèi)型進(jìn)行識(shí)別,增強(qiáng)了識(shí)別過(guò)程的智能性。本文選取脫丙烷生產(chǎn)工藝為研究對(duì)象,建立了動(dòng)態(tài)模擬仿真系統(tǒng)。同時(shí)也建立了擾動(dòng)量的反演模型,診斷出了工藝中常見(jiàn)的擾動(dòng)原因,對(duì)上述所提方法進(jìn)行了驗(yàn)證和對(duì)比。結(jié)果表明,此方法能夠快速定位擾動(dòng)類(lèi)型,準(zhǔn)確得出擾動(dòng)量,實(shí)現(xiàn)了動(dòng)態(tài)精餾系統(tǒng)擾動(dòng)原因的深入辨識(shí)。
[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(xué)位授予單位】：青島科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TQ028.31;TQ221.13

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