【摘要】：熱虹吸式再沸器是精餾塔的重要附屬設(shè)備,為了給再沸器的熱量控制提供一個(gè)較好的初值,本文采用神經(jīng)網(wǎng)絡(luò)和移動(dòng)窗兩者相結(jié)合的方法,建立熱虹吸式再沸器換熱量軟測(cè)量模型,并對(duì)各軟測(cè)量模型的影響因素進(jìn)行如下研究:(1)對(duì)再沸器進(jìn)行變量分析,在機(jī)理模型的基礎(chǔ)上,確定易測(cè)可控的關(guān)鍵變量,從中選擇五個(gè)變量作為數(shù)據(jù)模型的輸入輸出變量;(2)利用支持向量機(jī)建立了再沸器換熱量與各影響因素之間的數(shù)據(jù)模型,考察不同影響因素對(duì)換熱量軟測(cè)量結(jié)果的影響;(3)利用BP神經(jīng)網(wǎng)絡(luò)對(duì)非線性模型的擬合優(yōu)勢(shì),采用神經(jīng)網(wǎng)絡(luò)方法建立再沸器換熱量軟測(cè)量模型,考察不同影響因素對(duì)換熱量軟測(cè)量結(jié)果的影響;(4)針對(duì)生產(chǎn)過(guò)程的時(shí)變特性,以及軟測(cè)量技術(shù)實(shí)施過(guò)程中對(duì)模型預(yù)測(cè)可信度的要求,結(jié)合移動(dòng)窗方法改進(jìn)了神經(jīng)網(wǎng)絡(luò)模型,使模型盡可能貼合再沸器運(yùn)行狀態(tài)。在保證預(yù)測(cè)精度的前提下,降低了模型更新頻率,減少了計(jì)算;計(jì)算結(jié)果表明,采用神經(jīng)網(wǎng)絡(luò)加移動(dòng)窗方法建立熱虹吸式再沸器換熱量軟測(cè)量模型,可以為時(shí)變生產(chǎn)過(guò)程的在線檢測(cè)提供經(jīng)驗(yàn)與技術(shù)支持。
[Abstract]:Thermosyphon reboiler is an important auxiliary equipment of distillation column. In order to provide a better initial value for heat control of reboiler, the neural network and moving window are combined in this paper. The soft sensing model of heat transfer of thermosyphon reboiler is established, and the influencing factors of each soft-sensing model are studied as follows: (1) the variables of reboiler are analyzed, and the key variables which are easy to measure and controllable are determined on the basis of the mechanism model. Five variables are selected as input and output variables of the data model. (2) the data model between the heat transfer of reboiler and various factors is established by using support vector machine. The effects of different factors on the results of soft measurement of heat transfer are investigated. (3) using the advantage of BP neural network to fit the nonlinear model, the reboiler soft sensor model of heat exchange is established by using the neural network method. The effects of different factors on the results of soft measurement of heat exchange are investigated. (4) according to the time-varying characteristics of the production process and the requirement of the reliability of the model prediction in the implementation of soft sensing technology, the neural network model is improved with the moving window method. Make the model fit the reboiler running state as well as possible. On the premise of ensuring the prediction accuracy, the updating frequency of the model is reduced and the calculation is reduced. The calculation results show that the thermal siphon reboiler heat transfer soft sensing model is established by using the neural network and moving window method. It can provide experience and technical support for on-line detection of time-varying production process.
【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TQ051.65

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本文編號(hào)：2255035