本文關(guān)鍵詞： 精餾塔 解耦控制 BP神經(jīng)網(wǎng)絡(luò) 粒子群 RBF神經(jīng)網(wǎng)絡(luò)　出處：《浙江理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：精餾塔是石油化工行業(yè)中常用的化工設(shè)備，主要用于多種混合石化產(chǎn)品的分離過(guò)程。作為一種典型的多變量耦合系統(tǒng)，其控制性能的好壞將直接影響精餾生產(chǎn)過(guò)程的能耗及產(chǎn)品質(zhì)量。因此，研究有效的解耦控制方法，研制高精度精餾塔控制系統(tǒng)，一直以來(lái)都得到了化工過(guò)程控制領(lǐng)域的高度關(guān)注。本文在分析精餾塔耦合特性的基礎(chǔ)上，研究了基于神經(jīng)網(wǎng)絡(luò)PID的解耦控制方法，研制了基于PLC的精餾塔解耦控制系統(tǒng)，并實(shí)驗(yàn)驗(yàn)證了方法的有效性。完成的主要工作如下： (1)在綜述國(guó)內(nèi)外研究現(xiàn)狀的基礎(chǔ)上，介紹了精餾塔生產(chǎn)工藝與控制要求，分析了所具有的非線性、大時(shí)滯、多變量、強(qiáng)耦合等特點(diǎn)，提出了系統(tǒng)的總體控制方案。 (2)針對(duì)精餾塔塔底與塔頂溫度耦合嚴(yán)重以及傳統(tǒng)解耦控制方法的不足，提出了一種基于混沌粒子群算法的神經(jīng)網(wǎng)絡(luò)PID控制方法。用混沌粒子群算法來(lái)替代神經(jīng)網(wǎng)絡(luò)PID原先的反向傳遞學(xué)習(xí)算法，調(diào)節(jié)PIDNN各個(gè)神經(jīng)元之間的權(quán)值，以達(dá)到快速解耦的控制效果。仿真結(jié)果表明，所提出的方法與原有的BP算法相比具有更加優(yōu)秀的動(dòng)態(tài)和穩(wěn)態(tài)性能。 (3)進(jìn)一步分析了精餾塔所具有的非線性、大慣性、強(qiáng)耦合等特性，提出一種基于動(dòng)態(tài)RBF神經(jīng)網(wǎng)絡(luò)的單神經(jīng)元PID解耦控制算法。構(gòu)建動(dòng)態(tài)RBF神經(jīng)網(wǎng)絡(luò)用于辨識(shí)耦合系統(tǒng)模型，將辨識(shí)所得到的Jcobian矩陣信息用于單神經(jīng)元PID控制器參數(shù)在線整定，從而完成對(duì)精餾塔系統(tǒng)的解耦控制。仿真結(jié)果表明，所提出的算法與傳統(tǒng)的基于RBF神經(jīng)網(wǎng)絡(luò)的PID解耦控制相比，控制精度提高，魯棒性增強(qiáng)。 (4)以實(shí)驗(yàn)室乙酸乙酯生產(chǎn)線精餾塔設(shè)備為對(duì)象，以西門子s7-300PLC為下位機(jī)控制器，以北京亞控科技有限公司的組態(tài)王軟件(6.53)為上位機(jī)監(jiān)控平臺(tái)，研制了精餾塔智能解耦控制系統(tǒng)。完成了硬件系統(tǒng)控制柜的設(shè)計(jì)及調(diào)試，制作了上位機(jī)組態(tài)界面，，編寫了基于step7軟件的解耦控制算法。進(jìn)行了精餾塔溫度控制的實(shí)驗(yàn)研究，實(shí)際運(yùn)行結(jié)果表明，本文提出的解耦控制方法具有動(dòng)態(tài)性能好、控制精度高、魯棒性強(qiáng)等特點(diǎn)，明顯提高了精餾塔解耦控制系統(tǒng)的溫度控制精度，具有較高的實(shí)用價(jià)值。
[Abstract]:Distillation column is a chemical equipment commonly used in petrochemical industry. It is mainly used in the separation process of mixed petrochemical products. Its control performance will directly affect the energy consumption and product quality of distillation production process. Therefore, an effective decoupling control method is studied to develop the control system of high precision distillation column. In this paper, the decoupling control method based on neural network PID is studied, and a distillation tower decoupling control system based on PLC is developed. The effectiveness of the method is verified by experiments. The main work accomplished is as follows:. 1) on the basis of summarizing the present research situation at home and abroad, this paper introduces the production process and control requirements of distillation column, analyzes the characteristics of nonlinearity, large time delay, multivariable and strong coupling, and puts forward the overall control scheme of the system. 2) aiming at the serious temperature coupling between bottom and top of distillation column and the deficiency of traditional decoupling control method, A neural network PID control method based on chaotic particle swarm optimization (PSO) is proposed, which replaces the original reverse transfer learning algorithm of neural network PID and adjusts the weights of each neuron in PIDNN. The simulation results show that the proposed method has better dynamic and steady-state performance than the original BP algorithm. In this paper, the nonlinear, large inertia and strong coupling characteristics of distillation column are further analyzed, and a single neuron PID decoupling control algorithm based on dynamic RBF neural network is proposed. The dynamic RBF neural network is used to identify the coupled system model. The Jcobian matrix information obtained by identification is used to set the parameters of single neuron PID controller online, and the decoupling control of distillation column system is completed. The simulation results show that, Compared with the traditional PID decoupling control based on RBF neural network, the proposed algorithm improves the control precision and robustness. Taking the distillation tower equipment of ethyl acetate production line in laboratory as the object, Siemens S7-300 PLC as the lower computer controller, and the Kingview software of Beijing Asia Control Technology Co., Ltd. The intelligent decoupling control system of distillation column is developed, the design and debugging of the hardware control cabinet is completed, the configuration interface of the upper computer is made, the decoupling control algorithm based on step7 software is compiled, and the experimental research on the temperature control of the distillation column is carried out. The actual operation results show that the decoupling control method presented in this paper has the advantages of good dynamic performance, high control precision and strong robustness, and it obviously improves the temperature control accuracy of the decoupling control system of the distillation tower, and has a higher practical value.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TE962;TP273

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張軍;王玫;趙德安;;高超飛行器的再入非線性魯棒控制[J];動(dòng)力學(xué)與控制學(xué)報(bào);2011年01期

2 姬曉飛,孟令柏,申?yáng)|日,陳義俊;基于RBF神經(jīng)網(wǎng)絡(luò)多步預(yù)測(cè)的自適應(yīng)PID控制[J];甘肅科學(xué)學(xué)報(bào);2003年02期

3 張平洋;吳向前;袁紅旗;;MIMO系統(tǒng)的MPSO-PID型神經(jīng)網(wǎng)絡(luò)解耦控制研究[J];工業(yè)控制計(jì)算機(jī);2012年02期

4 曹躍進(jìn);陳新運(yùn);;西門子PLC和Profibus-DP總線技術(shù)在粗軋機(jī)系統(tǒng)中的應(yīng)用[J];工業(yè)儀表與自動(dòng)化裝置;2012年06期

5 趙靜;;精餾塔溫度模糊解耦控制系統(tǒng)的研究[J];工業(yè)儀表與自動(dòng)化裝置;2013年06期

6 潘海鵬;徐玉穎;;基于BP網(wǎng)絡(luò)的流漿箱雙變量PID解耦控制[J];化工學(xué)報(bào);2010年08期

7 王衛(wèi)兵;楊傳香;王偉;常治國(guó);;改進(jìn)BP算法解耦熱網(wǎng)控制器的設(shè)計(jì)與仿真[J];哈爾濱理工大學(xué)學(xué)報(bào);2012年02期

8 尹平林，瞿坦;模型參考自適應(yīng)解耦控制研究[J];華中理工大學(xué)學(xué)報(bào);1994年08期

9 劉軍民;高岳林;;混沌粒子群優(yōu)化算法[J];計(jì)算機(jī)應(yīng)用;2008年02期

10 湛力,羅喜霜,張?zhí)鞓?非線性系統(tǒng)的自組織模糊解耦控制[J];計(jì)算機(jī)仿真;2004年01期

本文編號(hào)：1528570