一类化工过程监控系统开发及优化控制应用研究
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摘要
本文以辽宁锦州六陆实业股份公司年产3万吨二氧化碳精制回收工程为实例，根据该工程的设计要求和现场的实际情况，以降低项目成本、保证功能为出发点，在参考国内外现今流行的组态软件基础之上，设计开发了针对该化工生产过程的计算机监控系统。该监控系统具有良好的人机界面、简单方便的操作和易扩展性，同时，它也具有较好的可靠性和稳定性，不仪能够直观、形象地反映生产现场的实际工况，而且也为该化工生产过程安全可靠稳定连续的运行提供了有力的支持。
此外，本文也针对生产现场一些工艺参数，特别是温度参数采用常规的PID控制效果不好，并且参数整定麻烦这一问题，给出了一种解决办法。该方法实际上是根据常规PID离散化算式，借鉴了预测控制的思想，利用人工神经网络的非线性逼近能力和学习记忆能力，设计了一种神经网络的PID控制器及其改进的基于输出多步预测的神经网络PID控制器，通过仿真试验证明它们是可行的和有效的，能够较好地解决类似于温度这类响应较慢的对象的控制问题，对非线性、时变对象具有较强的适应性，并且其参数整定简单，方便用于在线实时控制。
The paper discusses a computer monitoring system for a thirty thousand ton CO2 refinedly retrieving project of Jinzhou Liulu Industry joint-stock company. According to the designing requirements and actual surroundings, the author designs a reliable and low cost system based on domestic and international popular configuration software. The monitoring system has not only friendly HMI which is convenient to operate but also good stability and expansibility. It can reflect the real-time situation of the production vividly so that it provides a strong support for the successive running of the chemical production safely and reliably.
In addition, the traditional PID controller for some parameter, specially is temperature parameter, has dissatisfactory results and the parameters are hard to tune. To improve the statues, the author proposes a method to solve the problem effectively. Actually this method use the idea of predictive control for reference and its improved one based on neural network PID controller which applies the non-linearly approaching ability and memorizing ability of neural network to traditional PID discrete equation. The simulation test proves the design's feasibility and validity. It is convenient to use on the real time control of online and better than the traditional one on the aspects of parameter tuning, adaptability of actual production and solve the control problem of slow response plant which is like temperature parameter.
此外，本文也针对生产现场一些工艺参数，特别是温度参数采用常规的PID控制效果不好，并且参数整定麻烦这一问题，给出了一种解决办法。该方法实际上是根据常规PID离散化算式，借鉴了预测控制的思想，利用人工神经网络的非线性逼近能力和学习记忆能力，设计了一种神经网络的PID控制器及其改进的基于输出多步预测的神经网络PID控制器，通过仿真试验证明它们是可行的和有效的，能够较好地解决类似于温度这类响应较慢的对象的控制问题，对非线性、时变对象具有较强的适应性，并且其参数整定简单，方便用于在线实时控制。
The paper discusses a computer monitoring system for a thirty thousand ton CO2 refinedly retrieving project of Jinzhou Liulu Industry joint-stock company. According to the designing requirements and actual surroundings, the author designs a reliable and low cost system based on domestic and international popular configuration software. The monitoring system has not only friendly HMI which is convenient to operate but also good stability and expansibility. It can reflect the real-time situation of the production vividly so that it provides a strong support for the successive running of the chemical production safely and reliably.
In addition, the traditional PID controller for some parameter, specially is temperature parameter, has dissatisfactory results and the parameters are hard to tune. To improve the statues, the author proposes a method to solve the problem effectively. Actually this method use the idea of predictive control for reference and its improved one based on neural network PID controller which applies the non-linearly approaching ability and memorizing ability of neural network to traditional PID discrete equation. The simulation test proves the design's feasibility and validity. It is convenient to use on the real time control of online and better than the traditional one on the aspects of parameter tuning, adaptability of actual production and solve the control problem of slow response plant which is like temperature parameter.
引文
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[2] 万加富，张文斐，张古松．网络监控系统原理与应用．第一版．北京：机械工业出版社，2003．5～9．
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[5] 王常力，廖道文．集散型控制系统的设计与应用．第一版．北京：清华大学出版社．1999．3
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[9] FP-M／FP1编程手册．日本：松下电工株式会社，1998．
[10] 翁维琴，孙洪程．过程控制系统及工程．第二版．北京：化学工业出版社．2002．213～229．
[11] Eric Brierley, Anthony Prince, David Rinaldi. Visual Basic6开发人员指南．北京：机械工业出版社．1999．
[12] Brian Siler, Jeff Spotts. Visual Basic6开发使用手册．北京：机械工业出版社．1999．
[13] Steven Holzner. Visum Basic6技术内幕．北京：机械工业出版社．1999．316～318．
[14] 张树宾，戴红，陈哲．Visum Basic6.0中文版入门与提高．北京：清华大学出版社．1999．
[15] 牛洪涛．工控软件干扰设计．微机及应用．1998．6
[16] 张东来等．基于VB平台的测控系统关键技术研究．计算机工程．1998(9)．
[17] 王华强．工控组态式软件功能分析和应用实例．电气自动化．1998(4)．
[18] 王常力．面向对象的AMC测控系统的软件设计．测控技术．1998(17)．
[19] 王亚民等．组态软件设计与开发．西安：西安电子科技大学出版社．2003．126～217．
[20] 刘定晟，杨俊，蒋迪消．用Visual Basic实现测控软件中的实时曲线和历史曲线．计算机应用研究．2001(2)：147～149．
[21] Jeffrey P. McManus. Visual Basic6数据库访问技术．北京：机械工业出版社．1999
[22] John W. Fronckowiak, David J. Heida. Visual Basic6数据库编程大全．北京：电子工业出版社．1999．
[23] 陶永华．新型PID控制及应用．第二版．北京：机械工业出版社，2002．149～196．
[24] 易继锴，侯媛彬．智能控制技术．北京：北京工业大学出版社，1999．95～99．
[25] Simon．神经网络的综合基础．第二版．北京：清华大学出版社．2002．
[26] Martin T·Hagan．神经网络设计(英文版)．北京：机械工业出版社．2002．
[27] 谭永红．神经网络自适应PID控制及应用．模式识别与人工智能．1993，6(1)：81-85．
[28] Iwasa. T, Morizumi. N, Omatu. S. Temperature control in a batch process by neural networks. IEEE World Congress on Computational Intelligence. 1998(2):992-995.
[29] Iwasa. K, Morizumi. N, Omatu. S. Pressure control in a plant generating chloride by neural network PID control. IEEE International Conference on Neural Networks. 1995(1):627-630.
[30] Yonghong Tan, De Keyser. R. Auto-tuning PID control using neural predictor to compensate large time-delay. The Third IEEE Conference on Control Applications. 1994(2):1429-1434.
[31] Nielson H R.Theroy of the back propagation networks. IEEE IJCNN, 1989(Ⅰ):593～606.
[32] 王耀南，童调生，蔡自兴．基于神经元网络的智能PID控制及应用．信息与控，1994，23(3)：185～189．
[33] Khalid. M, Omatu. S. A neural network controller for a temperature control system. Control Systems Magazine, IEEE. 1992(12):58～64.
[2] 万加富，张文斐，张古松．网络监控系统原理与应用．第一版．北京：机械工业出版社，2003．5～9．
[3] 邹益仁，马增良，蒲维编．现场总线控制系统的设计和开发．北京：国防工业出版社．2003．
[4] 史忠科．神经网络控制理论．西安：西北工业大学出版社．1998．
[5] 王常力，廖道文．集散型控制系统的设计与应用．第一版．北京：清华大学出版社．1999．3
[6] Programmable Controller:Fp2 Hardware Manual Book. Japan:Matsushita Electric Works, Ltd. 1998.
[7] Mewtocol Communication Procedure. Japan: Matsushita Electric Works, Ltd. 1998.1～38.
[8] 范逸芝，陈立元．Visual Basic与RS232串行通信控制(最新版)．北京：中国青年出版社，2002．3～24．
[9] FP-M／FP1编程手册．日本：松下电工株式会社，1998．
[10] 翁维琴，孙洪程．过程控制系统及工程．第二版．北京：化学工业出版社．2002．213～229．
[11] Eric Brierley, Anthony Prince, David Rinaldi. Visual Basic6开发人员指南．北京：机械工业出版社．1999．
[12] Brian Siler, Jeff Spotts. Visual Basic6开发使用手册．北京：机械工业出版社．1999．
[13] Steven Holzner. Visum Basic6技术内幕．北京：机械工业出版社．1999．316～318．
[14] 张树宾，戴红，陈哲．Visum Basic6.0中文版入门与提高．北京：清华大学出版社．1999．
[15] 牛洪涛．工控软件干扰设计．微机及应用．1998．6
[16] 张东来等．基于VB平台的测控系统关键技术研究．计算机工程．1998(9)．
[17] 王华强．工控组态式软件功能分析和应用实例．电气自动化．1998(4)．
[18] 王常力．面向对象的AMC测控系统的软件设计．测控技术．1998(17)．
[19] 王亚民等．组态软件设计与开发．西安：西安电子科技大学出版社．2003．126～217．
[20] 刘定晟，杨俊，蒋迪消．用Visual Basic实现测控软件中的实时曲线和历史曲线．计算机应用研究．2001(2)：147～149．
[21] Jeffrey P. McManus. Visual Basic6数据库访问技术．北京：机械工业出版社．1999
[22] John W. Fronckowiak, David J. Heida. Visual Basic6数据库编程大全．北京：电子工业出版社．1999．
[23] 陶永华．新型PID控制及应用．第二版．北京：机械工业出版社，2002．149～196．
[24] 易继锴，侯媛彬．智能控制技术．北京：北京工业大学出版社，1999．95～99．
[25] Simon．神经网络的综合基础．第二版．北京：清华大学出版社．2002．
[26] Martin T·Hagan．神经网络设计(英文版)．北京：机械工业出版社．2002．
[27] 谭永红．神经网络自适应PID控制及应用．模式识别与人工智能．1993，6(1)：81-85．
[28] Iwasa. T, Morizumi. N, Omatu. S. Temperature control in a batch process by neural networks. IEEE World Congress on Computational Intelligence. 1998(2):992-995.
[29] Iwasa. K, Morizumi. N, Omatu. S. Pressure control in a plant generating chloride by neural network PID control. IEEE International Conference on Neural Networks. 1995(1):627-630.
[30] Yonghong Tan, De Keyser. R. Auto-tuning PID control using neural predictor to compensate large time-delay. The Third IEEE Conference on Control Applications. 1994(2):1429-1434.
[31] Nielson H R.Theroy of the back propagation networks. IEEE IJCNN, 1989(Ⅰ):593～606.
[32] 王耀南，童调生，蔡自兴．基于神经元网络的智能PID控制及应用．信息与控，1994，23(3)：185～189．
[33] Khalid. M, Omatu. S. A neural network controller for a temperature control system. Control Systems Magazine, IEEE. 1992(12):58～64.