基于极点配置的管式聚合反应温度分布控制
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摘要
本文是以实验室规模的阳离子管式反应器为背景,研究管式反应器温度分布控制方法问题,具体采用的是极点配置方法。在目前的工业生产中,聚合物的分子量缺乏在线测量技术,急需一种可以通过间接测量某一个性能指标,从而得到聚合物分子量分布的方法。众所周知,在化学反应中,温度的变化对反应速率及产物品位起着举足轻重的作用,所以选择反应器温度作为替代参数,通过控制管式反应器的温度分布,在某种程度上达到控制反应速率和分子量分布的目的。因此建立管式反应器沿管长的温度分布的模型,设计控制器对温度分布进行控制对研究聚合物分子量分布的意义十分重大。
本文主要研究以下工作:
1、根据B样条函数具有拟合任意连续函数的性质,通过计算得出温度分布对应于B样条函数的权值向量,再利用B样条神经网络与递归神经网络相结合的混合神经网络,采用最小二乘的辨识方法,建立了管式反应器沿管长的温度分布模型,得到了与温度分布对应的权值向量的状态方程。
2、在已建立的模型基础上,采用极点配置方法设计管式聚合反应温度分布控制器。探究并总结了高阶系统闭环期望极点位置的选取办法,给出了带参考输入的状态反馈控制系统结构中反馈矩阵和增益矩阵的求法,最后对设计的控制器进行仿真研究。
3、由于控制策略存在余差,且考虑实际生产存在干扰的情况,对控制器结构进行改进,引入积分环节。对改进后的系统结构进行仿真研究,得出较好的控制效果。
本文在以上几方面的研究中,按照确定控制目标、建立模型、设计控制器等步骤开展,仿真研究结果证明了方案的可行性。
This paper studies distributed temperature control problem in tubular reactor, which is based on the cation pipe reactor at laboratory scale. However, at present, since the direct way of online measurement is absent, it is crucial to find a method that can measure indirectly in industrial polymer production. As we known, the change in temperature is very important to the reaction velocity and the outcome categories in chemical reaction, so the reactor's temperature is chose as the substituted parameter. Furthermore, one of the characteristics of the tubular reactor is that the pipe temperature can be controlled segments by segments, so reaction velocity and molecular weight distribution can be controlled through this way. Hence, establishing the temperature distribution model of tubular reactor along the pipe is more significant in the research of polymeric molecular weight distribution.
The main contributions of the paper are as follows:
1 The weight vector is attained by fitting of the temperature distribution with the characters of B-spline's fitting continuous function. And then the model of weighted temperature distribution along the tube of tubular reactor and the system input are established using the least square identification method based on the combination of B-spline neural network and recurrent neural network.
2 The model is controlled through the state feedback after it has been established. Then the way of choosing the location of expected pole in high-order system and the method of getting the feedback and gain matrixes when allocating the poles with reference input are introduced. Simulations are also given to show the results of designed controller.
3 The structure of the controller is improved by introducing the integral part for the existence of residual error in the simulation and interference in real production. Simulation is given to show the good performance of the proposed controller.
With the above studies, this paper is developed in accordance with the steps of identifying control objectives, building model and design controller. At last, simulation results prove the feasibility of the program.
本文主要研究以下工作:
1、根据B样条函数具有拟合任意连续函数的性质,通过计算得出温度分布对应于B样条函数的权值向量,再利用B样条神经网络与递归神经网络相结合的混合神经网络,采用最小二乘的辨识方法,建立了管式反应器沿管长的温度分布模型,得到了与温度分布对应的权值向量的状态方程。
2、在已建立的模型基础上,采用极点配置方法设计管式聚合反应温度分布控制器。探究并总结了高阶系统闭环期望极点位置的选取办法,给出了带参考输入的状态反馈控制系统结构中反馈矩阵和增益矩阵的求法,最后对设计的控制器进行仿真研究。
3、由于控制策略存在余差,且考虑实际生产存在干扰的情况,对控制器结构进行改进,引入积分环节。对改进后的系统结构进行仿真研究,得出较好的控制效果。
本文在以上几方面的研究中,按照确定控制目标、建立模型、设计控制器等步骤开展,仿真研究结果证明了方案的可行性。
This paper studies distributed temperature control problem in tubular reactor, which is based on the cation pipe reactor at laboratory scale. However, at present, since the direct way of online measurement is absent, it is crucial to find a method that can measure indirectly in industrial polymer production. As we known, the change in temperature is very important to the reaction velocity and the outcome categories in chemical reaction, so the reactor's temperature is chose as the substituted parameter. Furthermore, one of the characteristics of the tubular reactor is that the pipe temperature can be controlled segments by segments, so reaction velocity and molecular weight distribution can be controlled through this way. Hence, establishing the temperature distribution model of tubular reactor along the pipe is more significant in the research of polymeric molecular weight distribution.
The main contributions of the paper are as follows:
1 The weight vector is attained by fitting of the temperature distribution with the characters of B-spline's fitting continuous function. And then the model of weighted temperature distribution along the tube of tubular reactor and the system input are established using the least square identification method based on the combination of B-spline neural network and recurrent neural network.
2 The model is controlled through the state feedback after it has been established. Then the way of choosing the location of expected pole in high-order system and the method of getting the feedback and gain matrixes when allocating the poles with reference input are introduced. Simulations are also given to show the results of designed controller.
3 The structure of the controller is improved by introducing the integral part for the existence of residual error in the simulation and interference in real production. Simulation is given to show the good performance of the proposed controller.
With the above studies, this paper is developed in accordance with the steps of identifying control objectives, building model and design controller. At last, simulation results prove the feasibility of the program.
引文
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[4]Flores-Cerrillo J,MacGregor JF.Within-batch and batch-m-batch inferential adaptive control of semi-batch reactors:a partial least squares approach[J].Ind.Eng.Chem.Res.,2003,42:3334-3345
[5]Sayer C,Arzamendi G,Asua JM,et al.Dynamic optimization of semi continuous emulsion copolymerization reactions:composition and molecular weight distribution[J].Comp.Chem.Eng.,2001,25:839-849
[6]Meira G R,Johnson A F.Molecular weight distribution control in continuous'living'polymerizations through periodic operation of the monomer feed[J].Polymer Engineering and Science,1981,21(7):415
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[12]D Jones,Wang H.A new nonlinear optimal control strategy for paper formation[J].Journal of Measurement and Control,1999,32:241-245
[13]曹柳林,吴海燕.利用B样条神经网络实现聚合反应分子量分布的建模与控制[J].化工学报,2004,55(5):742-746
[14]袁曾任.人工神经元网络及其应用[M].北京:清华大学出版社,1999.66-131
[15]孙增圻,张再兴,邓志东.智能控制理论与技术[M].北京:清华大学出版社,1997.120-131
[16]Timothy J C,Kyu Y C.Discrete Optimal Control of MWD in a Batch Free Radical Polymerization Process[J].Ind.Eng Chem.Res.1997,36:3676-3682
[17]Cao Liulin.Modeling of Molecular Weight Distribution for a Kind of Polycondensation Reaction Proeesses.Comp.and Appl.Chem.China,1999,16(3):225-230
[18]张先明,李广赞,冯连芳.双螺杆挤出机中局部停留时间分布研究[J].高校化学工程学报,2008,(22)3:435-440
[19]Flores-Cerrillo J.,MacGregor J.F..Within-batch and batch-to-batch inferential-adaptive control of semi-batch reactors:a partial least squares approach[J].Ind.Eng.Chem.Res.,2003,42:3334-3345
[20]Ravindranath,Mashelkar.Analysis of the role of stripping agents in polymer devolatilization[J].Chemical Engineering Science,1988,43(3):429-442
[21]Kumar A,upta S K,Gupta B,et al.Modelling of reversible poly(ethylene terephthalate)reactors[J].J of Applied Polymer Science,1982,27:4421-4438
[22]Kumar A,Gupta S K,Somu N.Molecular weight distribution of polyethylene terephthalate in homogeneous,continuous-flow-stirred tank reactors.Polymer Engimeering and Science,1982,22(5):314-323
[23]王晖.管式阳离子聚合反应过程的数学模型[J].石油化工,1985,9:523-529
[24]刘济,顾幸生,张素贞.PET同相缩聚反应器动态模型[J].化工学报,2008,7(59):1727-1731
[25]唐渊,何波兵,李兰军.超声技术在聚合物检测中的应用进展[J].塑料工业,2007,6(35):25-27
[26]曹柳林,马娜.基于管式聚合反应温度分布的动态建模和PI控制器设计[J].东北大学学报,2007,28(s1):29-32.
[27]钱学森.工程控制论[M].北京:科学出版社,1958.8-12
[28]宋健,于景元.带有常微分控制器的分布参数反馈系统[J].中国科学A辑:数学,1975,18(2):141-145
[29]刘艳,黄发伦.几类分布参数控制系统的反馈稳定化问题[D].四川:四川大学,2008
[30]郭发明,黄发伦.分布参数控制西欧她能过得小时滞鲁班稳定性[D].四川:四川大学,2008
[31]邢海龙,高寸臣,曾宪武.一类具有变时滞不确定分布参数系统的滑动模控制[J].控制与决策,2007,(22)3:333-336
[32]于乃润.一类分布参数控制系统的镇定问题,西北纺织工学院学报,1994,8(4),358-362
[33]杨然,周钢.分布参数系统辨识的小波谱方法[J].信号处理,2001,17(4):308-312
[34]葛照强,马勇镐.一阶广义分布参数系统的极点配置问题[J].数学年刊:A辑,2001,6:729-734
[35]陈显强,赵怡.一类分布参数系统的反馈能稳性[J].控制理论与应用,2003,20(6):879-883
[36]Girosi F,Poggio T.Networks and the best approximation property[J].Biol.Cybern.,1990,163:169-176
[37]於东军,王士同.B样条神经网络的构造理论[J].计算机研究与发展,1999,36(5):534-540
[38]Ka Fai Cedric Yiu,Song Wang,Kok Lay Teo.Nonlinear system modeling via knot-optimizing B-spline networks[J].IEEE Transactions on Neural Networks,2001,12:1013-1022
[39]从爽,宋瑞祥.改进型B样条网络函数及其应用[J].中国科学技术大学学报,2000,6:633-638
[40]王耀南,孙炜,张冒凡.基于模糊B样条神经网络的复杂工业过程智能控制[A].见中国智能自动化学术会议论文集[C]:北京:清华大学出版社,1999,192-198
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