本文選題：刮膜式分子蒸餾　切入點(diǎn)：預(yù)測(cè)模型　出處：《長(zhǎng)春工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：分子蒸餾是一種新型的在高真空條件下進(jìn)行的液-液分離技術(shù),具有蒸餾溫度低,真空度高,物料受熱時(shí)間短,分離效率高等特點(diǎn);且分離過程不可逆,沒有沸騰鼓泡現(xiàn)象,特別適用于分離高沸點(diǎn)、熱敏性、高粘度和易被氧化的物質(zhì)。該技術(shù)已在醫(yī)藥行業(yè)、石油化工、食品工業(yè)、化妝品工業(yè)和農(nóng)業(yè)等各行各業(yè)中得到了廣泛應(yīng)用。在分子蒸餾的現(xiàn)有的諸多研究當(dāng)中,其建模、工藝參數(shù)的優(yōu)化及控制量的控制算法研究較少,但是這些研究對(duì)于分子蒸餾的生產(chǎn)節(jié)能、提高生產(chǎn)的有效時(shí)間及減少對(duì)人工經(jīng)驗(yàn)的依賴都起著很重要的作用。因此本文根據(jù)五味子粗油作為實(shí)驗(yàn)原料的提純實(shí)驗(yàn)結(jié)果,對(duì)工藝參數(shù)與產(chǎn)品指標(biāo)之間的關(guān)系進(jìn)行了討論,并結(jié)合刮膜式分子蒸餾的理論分析得到了影響被提純物的純度和得率的幾個(gè)主要參數(shù)。然后在此基礎(chǔ)上展開了對(duì)刮膜式分子蒸餾過程的建模、多參數(shù)優(yōu)化以及控制量的控制算法的研究,本文的具體研究?jī)?nèi)容可以概括為如下幾點(diǎn):首先,提出了采用極限學(xué)習(xí)機(jī)建立刮膜式分子蒸餾過程的預(yù)測(cè)模型。由于分子蒸餾系統(tǒng)的非線性、強(qiáng)耦合、大滯后等特性,采用機(jī)理建模很難實(shí)現(xiàn),并且考慮到BP網(wǎng)絡(luò)的局部最優(yōu)、訓(xùn)練時(shí)間長(zhǎng)的缺點(diǎn),因此提出采用極限學(xué)習(xí)機(jī)建立分子蒸餾系統(tǒng)的預(yù)測(cè)模型,仿真結(jié)果也表明該預(yù)測(cè)模型能夠更加準(zhǔn)確及時(shí)的預(yù)測(cè)系統(tǒng)的下一時(shí)刻輸出狀態(tài)。其次,采用啟發(fā)式動(dòng)態(tài)規(guī)劃方法優(yōu)化刮膜式分子蒸餾過程的多個(gè)參數(shù),并對(duì)該算法進(jìn)行了改進(jìn)。由于參數(shù)間的耦合,單個(gè)參數(shù)優(yōu)化效果并不理想,并且多個(gè)參數(shù)要進(jìn)行多次優(yōu)化,所以本文提出多參數(shù)同時(shí)優(yōu)化方法。通過該方法能夠在任意初始狀態(tài)下,快速找到較好的工藝參數(shù),縮短了參數(shù)調(diào)節(jié)時(shí)間。由于前人是采用BP網(wǎng)絡(luò)實(shí)現(xiàn)啟發(fā)式動(dòng)態(tài)規(guī)劃,將BP網(wǎng)絡(luò)的如局部最優(yōu)、調(diào)節(jié)時(shí)間長(zhǎng)等問題也引入到了該算法中,因此本文給出了用極限學(xué)習(xí)機(jī)實(shí)現(xiàn)啟發(fā)式動(dòng)態(tài)規(guī)劃的方法的具體理論推導(dǎo),使改進(jìn)后算法的尋優(yōu)速度提高了近一倍。再次,提出了非線性系統(tǒng)的基于序貫極限學(xué)習(xí)機(jī)的逆模型控制方法。該方法不僅解決了解析逆系統(tǒng)難以求得的問題,還實(shí)現(xiàn)了逆系統(tǒng)的在線調(diào)整。該方法使用逆模型作為控制器,將逆系統(tǒng)與原系統(tǒng)的串聯(lián),逆系統(tǒng)的輸出直接作用于被控對(duì)象。該方法能夠有效的控制分子蒸餾系統(tǒng)的控制量如電機(jī)轉(zhuǎn)速、熱油機(jī)的溫度控制等。最后,設(shè)計(jì)了刮膜式分子蒸餾的現(xiàn)場(chǎng)總線的控制方案,并且利用OPC(Object Linking and Embending,OPC)通信技術(shù),能夠?qū)⑸衔粰C(jī)中運(yùn)行的高級(jí)算法的運(yùn)算結(jié)果通過PROFIBUS網(wǎng)絡(luò)發(fā)送到現(xiàn)場(chǎng)的控制器中。
[Abstract]:Molecular distillation is a new liquid-liquid separation technology under high vacuum conditions. It has the characteristics of low distillation temperature, high vacuum, short heating time and high separation efficiency, and the separation process is irreversible without boiling bubbling. It is especially suitable for the separation of substances with high boiling point, heat sensitivity, high viscosity and easy oxidation. The technology has been used in the pharmaceutical, petrochemical and food industries. It has been widely used in cosmetics industry and agriculture. Among the existing researches on molecular distillation, there are few researches on modeling, optimization of process parameters and control algorithm of control quantity. However, these studies play an important role in the production of molecular distillation energy saving, increasing the effective time of production and reducing the dependence on artificial experience. Therefore, based on the experimental results of crude oil from Schisandra chinensis as raw material, The relationship between process parameters and product indexes is discussed. Based on the theoretical analysis of scraping molecular distillation, several main parameters affecting the purity and yield of the purified product were obtained. Then, the modeling of the scraped membrane molecular distillation process was carried out. The research of multi-parameter optimization and control algorithm of control quantity can be summarized as follows: firstly, In this paper, a predictive model of scraping membrane molecular distillation is proposed by using the extreme learning machine. Because of the nonlinear, strong coupling and large delay characteristics of the molecular distillation system, it is difficult to use the mechanism modeling, and the local optimum of BP network is considered. Because of the disadvantage of long training time, a prediction model of molecular distillation system based on extreme learning machine is proposed. The simulation results also show that the prediction model can predict the output state of the system at the next moment more accurately and timely. Secondly, The heuristic dynamic programming method is used to optimize the parameters of the scraped membrane molecular distillation process, and the algorithm is improved. Because of the coupling between the parameters, the optimization effect of single parameter is not satisfactory, and many parameters should be optimized several times. Therefore, this paper proposes a multi-parameter simultaneous optimization method, which can quickly find better process parameters in any initial state, and shorten the adjusting time of parameters. Because the former use BP neural network to realize heuristic dynamic programming, The problems such as local optimum and long adjusting time of BP neural network are also introduced into the algorithm, so this paper presents the theoretical derivation of the heuristic dynamic programming method using the ultimate learning machine. The optimization speed of the improved algorithm is nearly doubled. Thirdly, the inverse model control method based on sequential limit learning machine for nonlinear systems is proposed. This method not only solves the problem that the analytical inverse system is difficult to obtain. In this method, the inverse model is used as the controller, and the inverse system and the original system are connected in series. The output of the inverse system acts directly on the controlled object. This method can effectively control the control quantities of the molecular distillation system such as the speed of the motor, the temperature control of the thermal oil engine, etc. Finally, the field bus control scheme of the scraper membrane molecular distillation is designed. And by using OPC(Object Linking and EmbendingOPC (OPC) communication technology, the result of the advanced algorithm running in the upper computer can be sent to the controller in the field through the PROFIBUS network.
【學(xué)位授予單位】：長(zhǎng)春工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TQ028.31

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 吳海波;張玉姣;方巖雄;楊祖金;芮澤寶;葉超;yひ，

本文編號(hào)：1599650