基于模糊逻辑的微乳液搅拌釜温度串级PID控制
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摘要
有机硅因其独特的结构而具有许多优异的性能,在诸多领域有着广泛的应用,被视为21世纪最有发展前途的新型“绿色材料”。有机硅微乳液外观呈透明至半透明,介于乳液与溶液之间的一种液体状态,即简称为硅微乳。目前美国、日本、德国等发达国家已有多种牌号的有机硅微乳液产品问世,并且取得较好的收益。它能解决一般有机硅破乳、漂油以及贮存稳定性差等特点。对于微乳液来说,聚合速度一般比较快,且温度控制较难,尤其是在乳化剂添加时的温度控制波动要小才能够得到细小的微乳液粒径[1]。而温度控制是工业控制中的一个非常基本但是又很重要的问题,针对温度控制过程中存在大滞后、时变、非线性的特点,为适应复杂系统的控制要求,人们应用了很多智能控制的理念和方法到温度控制中,模糊控制便是其中之一,模糊方法调节PID便是其中之一。模糊调节器结合了PID控制算法和模糊控制方法的优点,可以在线实现PID参数调整,使控制系统的响应速度快,过渡过程时间大大缩短,超调量减少,振荡次数少,具有较强的稳定性,在模糊控制中扮演着十分重要的角色。
本文为了提高某精细化工厂微乳液搅拌釜温度在不同工况下的控制精度,克服反应过程的非线性特性,满足多种产品的不同需求,开发一种基于模糊逻辑的串级PID控制器,并介绍了模糊调节器运用在串级PID控制在微乳液搅拌釜内温度控制系统中的应用。首先建立了模糊控制规则,介绍了串级PID控制、模糊控制以及模糊调节器调节PID参数的基本原理,然后进行仿真模拟和现场试车,该方法在原有典型PID串级控制器上增加了一个模糊调节器,根据釜温与设定值差、夹套温度与釜温差,及经验规则调节主回路PID参数。实践证明了该方法是有效的,能够提高系统控制精度。结果表明,模糊调节器运用在串级PID的方法与常规的串级PID控制方法相比有稳定性强,和动态性能好等特点,该控制方法对于微乳液搅拌釜系统的温度控制是有效的。
Silicone has a lot of great performance due to it’s typical structure, and applies in quite a few area. It’s foresee the most potential new“green material”in 21 century. The appearance of silicone micro emulsion is transparence or half-transparence, so the silicone micro emulsions is a liquid between of the emulsions and solution and named silicone micro emulsion. Nowadays ,there are many silicone micro emulsions have been developed in U.S. , Japan, Germany and other developed country, and the market margin is very good. It can solve some common issue like emulsion-broken, surface oil that normal particle size emulsions. The micro-emulsions has a quick polymerization speed, however the temperature control seems a little difficult to control, it’s critical to maintain a steady temperature with very small fluctuation when load the surfactant to achieve a targeted small particle size. And temperature control is very fundamental but it’s very important subject in the process control. With considering the characteristics of long dead time , time variance ,non-linear of temperature control, to fulfill the complex process requirement, there is a lot think on utilize the intellectual control theory and method on temperature control, fuzzy PID control is one example. The fuzzy logic thinking combines the advantages of traditional PID control algorithm and fuzzy control theory, the on-line PID tuning become realistic with rapid responding from the control system and the transitioning period is minimized, also the overshoot has been reduced with less fluctuation and strong robustness, therefore it’s playing a very important role in fuzzy control.
In order to optimize the micro-emulsion blender’s temperature control performance especially under the disturbance and fulfill the requirement one control system for different product(with different specific heat), i.e. to minimize the temp. Fluctuation and avoid overshooting occasionally, A cascade PID controller with combining a Fuzzy logic on the PID tuning is developed. A Fuzzy control box is added in the typical cascade PID controller, the fuzzy logic will determine the PID parameters according to the temperature difference among the blender, the set point and the Jacket Temp., which also combines the experience from the field operators on the PID adjustment. The results of the implementation in real plant prove that the method is effective; the performance of the temperature control system for the blender is improved and result is satisfied.
本文为了提高某精细化工厂微乳液搅拌釜温度在不同工况下的控制精度,克服反应过程的非线性特性,满足多种产品的不同需求,开发一种基于模糊逻辑的串级PID控制器,并介绍了模糊调节器运用在串级PID控制在微乳液搅拌釜内温度控制系统中的应用。首先建立了模糊控制规则,介绍了串级PID控制、模糊控制以及模糊调节器调节PID参数的基本原理,然后进行仿真模拟和现场试车,该方法在原有典型PID串级控制器上增加了一个模糊调节器,根据釜温与设定值差、夹套温度与釜温差,及经验规则调节主回路PID参数。实践证明了该方法是有效的,能够提高系统控制精度。结果表明,模糊调节器运用在串级PID的方法与常规的串级PID控制方法相比有稳定性强,和动态性能好等特点,该控制方法对于微乳液搅拌釜系统的温度控制是有效的。
Silicone has a lot of great performance due to it’s typical structure, and applies in quite a few area. It’s foresee the most potential new“green material”in 21 century. The appearance of silicone micro emulsion is transparence or half-transparence, so the silicone micro emulsions is a liquid between of the emulsions and solution and named silicone micro emulsion. Nowadays ,there are many silicone micro emulsions have been developed in U.S. , Japan, Germany and other developed country, and the market margin is very good. It can solve some common issue like emulsion-broken, surface oil that normal particle size emulsions. The micro-emulsions has a quick polymerization speed, however the temperature control seems a little difficult to control, it’s critical to maintain a steady temperature with very small fluctuation when load the surfactant to achieve a targeted small particle size. And temperature control is very fundamental but it’s very important subject in the process control. With considering the characteristics of long dead time , time variance ,non-linear of temperature control, to fulfill the complex process requirement, there is a lot think on utilize the intellectual control theory and method on temperature control, fuzzy PID control is one example. The fuzzy logic thinking combines the advantages of traditional PID control algorithm and fuzzy control theory, the on-line PID tuning become realistic with rapid responding from the control system and the transitioning period is minimized, also the overshoot has been reduced with less fluctuation and strong robustness, therefore it’s playing a very important role in fuzzy control.
In order to optimize the micro-emulsion blender’s temperature control performance especially under the disturbance and fulfill the requirement one control system for different product(with different specific heat), i.e. to minimize the temp. Fluctuation and avoid overshooting occasionally, A cascade PID controller with combining a Fuzzy logic on the PID tuning is developed. A Fuzzy control box is added in the typical cascade PID controller, the fuzzy logic will determine the PID parameters according to the temperature difference among the blender, the set point and the Jacket Temp., which also combines the experience from the field operators on the PID adjustment. The results of the implementation in real plant prove that the method is effective; the performance of the temperature control system for the blender is improved and result is satisfied.
引文
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[3]王孝武.现代控制理论基础.机械工业出版社,2002
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[11] J.N. Lygouras, P.N. Botsaris, J. Vourvoulakis, V. Kodogiannis.Fuzzy logic controller implementation for a solar air-conditioning system Applied Energy. Volume 84, Issue 12, December 2007, Pages 1305~1318
[12] Romano Borchiellini, Vittorio Verda.A fuzzy logic procedure for ventilation control in case of fire in long tunnels Fire Safety Journal.Volume 44, Issue 4, May 2009, Pages 612~621
[13] K.K. Ahn, D.Q. Truong.Online tuning fuzzy PID controller using robust extended Kalman filter Journal of Process Control. Volume 19, Issue 6, June 2009, Pages 1011~1023
[14] Saravuth Pothiya, Issarachai Ngamroo.Optimal fuzzy logic-based PID controller for load–frequency control including superconducting magnetic energy storage units Energy Conversion and Management. Volume 49, Issue 10, October 2008, Pages 2833~2838
[15] Zafer Aydogmus.Implementation of a fuzzy-based level control using SCADA Expert Systems with Applications.Volume 36, Issue 3, Part 2, April 2009, Pages 6593-6597
[16] Chin-Hsing Cheng, Po-Jen Cheng, Ming-Tzong Wu.Fuzzy logic design of self-tuning switching power supply Expert Systems with Applications.In Press, Corrected Proof, Available online 19 September 2009
[17] Shahin Salehia and Mohammad Shahrokhib.Adaptive fuzzy backstepping approach for temperature control of continuous stirred tank reactors. Fuzzy Sets and Systems,Volume 160, Issue 12, 16 June 2009, Pages 1804~1818
[18] Kuan-Yu Chen, Pi-Cheng Tung, Mong-Tao Tsai, Yi-Hua Fan.A self-tuning fuzzy PID-type controller design for unbalance compensation in an active magnetic bearing Expert Systems with Applications.Volume 36, Issue 4, May 2009, Pages 8560~8570
[19] Fang-Ming Yu.A self-tuning fuzzy logic design for perturbed time-delay systems withnonlinear input Expert Systems with Applications. Volume 36, Issue 3, Part 1, April 2009, Pages 5304~5309
[20]廉小亲.模糊控制技术.中国电力出版社,2003:43~76
[21]张文修,梁广锡.模糊控制与系统.西安交通大学出版社,1998:36~44
[22]马聪,吴建国,朱海荣.模糊PID控制在烟酸反应釜温度控制中的应用.南通大学学报,第5卷,第2期,2006年6月
[23]诸静等.模糊控制原理与应用.机械工业出版社,ISBN 7-111-04671-4
[24]陶永华.PID控制原理和自整定策略[J].工业仪表和自动化装置,1997.4:60-64
[25]陶永华.新型PID控制及其应用[M].北京:机械工业出版社,2002.8:2-7
[26] Franks, R. G., Worley, C. W., (1956), Quantities analysis of cascade control, Industrial and Engineering Chemistry, 48 (6): 1074-1079.
[27]孟华,王幸之, (2002),工业过程检测与控制,北京,北京航空航天大学出版社, 1-260.
[28] Stephanopoulos, G., (1984), Chemical Process Control: An introduction to theory and practice. New Jersey, Prentice Hall, 1-324.
[29] Krishnaswamy, P. R. Rangaiah, G. P., (1990), When to use cascaded control. Ind. Eng. Chem. Res., 29(3): 2163-2170.
[30] Zhang, M. X., Atherton, D. P., (1994), Optimum cascade PID controller design for SISO systems, In UKACC Control, (3): 606-611.
[31] Silva, R.N., Rato, L. M., Lemos, J. M., (1997), Cascade control of a distributed collector solar field, Journal of Process Control, 7(4): 111-117.
[32] Li, S. H., Zhang, W.Z., Zhang, R.R.,etc, (2005), Cascade fuzzy control for gas engine driven heat pump, Energy Conversiion & Management, 46 (11):1757-1766.
[33] Dumur, D., Boucher, P., Kolb, T., (1996), A Application of cascaded constrained receding horizon predictive control to an induction machine. IEEE International Conference on Control Applications, Dearborn, MI, Sept, (7): 888-893.
[34] Hua, X.M., Rohani, S., Jutan, A., (2004), Cascade closed-loop optimization and control of batch reactors, Chemical Engineering Science, 59(24):5695-5708.
[35] Wu, W., Jayasuriya, S., (2000), A new QFT method for SISO cascade-loop design,Chicago, June, (6): 3827-3831.
[36] Ibrahim, K., (2001), Improving performance using cascade control and a smith predictor. ISA Transaction. 40(3): 223-234.
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