直流LF炉电流电压解耦方法研究与控制系统的设计
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摘要
LF钢包精炼炉是国内外最为流行的炉外精炼方式。钢包炉通常采用三相交流电弧加热。随着直流电弧炉技术的发展,钢包炉也采用直流电弧加热,并具有多方面的优点,尤其是在耐火材料消耗指数方面有着极大的优势。本项目采用炉底阳极形式的直流LF钢包精炼炉,是将一根石墨电极控制在具有一定电阻的熔渣层中,使石墨阴电极与金属熔池间保持一定的距离,当直流电压加在电极上时,阴极产生电弧。直流电通过阴阳极、电弧、电渣构成回路,达到对钢水加热的目的。
本文针对某钢铁集团技术中心实验工厂新建项目——2吨直流LF精炼炉系统,采用西门子S7—300及ET200S系列PLC设计了基于PROFIBUS—DP的分布式计算机控制系统,实现了对整个精炼过程的控制。利用西门子公司的WinCC组态软件,完成上位机监控软件组态,实现现场数据实时记录和监控。设计了冶炼计时、记录查询、读写工艺参数等具有Windows风格的动态操作画面。
在LF炉内钢水的加热速度和温度完全由电流控制系统和电压调节系统决定。电流控制通过调节石墨电极升降速度和插入深度实现,电压控制通过调节直流整流电源的输出电压实现。然而,电极插入深度既影响电流大小,同时也影响供电电压的大小,形成较强的耦合作用。本文针对这一现象,同时考虑到LF炉非线性、时变、没有准确数学模型的特性,尝试设计了一种PID神经网络解耦控制器。它是把传统PID方法与神经网络相结合,对LF的电流电压进行解耦控制,从而使被控对象具有良好的动、静态性能,满足控制要求。
论文详细地介绍了直流LF炉的生产工艺流程、控制系统的总体结构设计、PLC系统选型、控制软件的编制等内容。
The ladle furnace is the most popular refine way in the domestic and foreign. The ladle furnace usually uses the three phase AC arc to heat. Along with the development of the DC arc furnace technology, the ladle furnace also uses DC arc to heat, and has the various merits, has the enormous superiority in particular in the fire-proof material consumption index aspect. This project uses single bar bottom positive electrode, which takes a graphite electrode in the slag level that has the certain resistance, maintains the certain distance between the graphite negative electrodes and the metal molten bath. When DC voltage is added to the electrode, the negative electrode produces the electric arc. The DC constitutes loop through the electrode, the electric arc and the electricity slag, to achieve the goal to heat up the molten steel.
This article aim at some Iron & Steel Corporation Steel Corporation group technology center pilot plant newly built project of 2t DC ladle furnace system, design distributed computer control system which is based on the PROFIBUS-DP. It controls the entire smelting process by using Siemens S7-300 and ET200S series PLC. Using Siemens's WinCC configuration software, to complete monitoring software configuration, to realizes the field data real-time recorded and the monitoring. Having designed smelting time, recording inquiry, read-write parameter and so on. Those have the dynamic operation picture of the Windows style.
In the ladle furnace, heating speed and the temperature of the molten steel completely lie on the electricity control system and the DC voltage control system. The electricity control is plying by the rise and fall speed and the infixed depth of graphite electrode. The voltage control is plying by the output voltage regulation on the DC power supply. But the infixed depth of graphite electrode make effect on the electricity, at the same time it also influenced the voltage. So it made great coupling. The thesis aim at the bad control impact made by the voltage and the electricity coupling in the DC ladle furnace, take into account the characters of the ladle furnace:non-linear, time-variation, inaccurate mathematic model. Design one kind of PID neural network decoupling controller. It combined the conventional PID method and the neural network, decoupling the electricity and the voltage of the ladle furnace, accordingly to have good dynamic and static characteristic, meet the control demand.
本文针对某钢铁集团技术中心实验工厂新建项目——2吨直流LF精炼炉系统,采用西门子S7—300及ET200S系列PLC设计了基于PROFIBUS—DP的分布式计算机控制系统,实现了对整个精炼过程的控制。利用西门子公司的WinCC组态软件,完成上位机监控软件组态,实现现场数据实时记录和监控。设计了冶炼计时、记录查询、读写工艺参数等具有Windows风格的动态操作画面。
在LF炉内钢水的加热速度和温度完全由电流控制系统和电压调节系统决定。电流控制通过调节石墨电极升降速度和插入深度实现,电压控制通过调节直流整流电源的输出电压实现。然而,电极插入深度既影响电流大小,同时也影响供电电压的大小,形成较强的耦合作用。本文针对这一现象,同时考虑到LF炉非线性、时变、没有准确数学模型的特性,尝试设计了一种PID神经网络解耦控制器。它是把传统PID方法与神经网络相结合,对LF的电流电压进行解耦控制,从而使被控对象具有良好的动、静态性能,满足控制要求。
论文详细地介绍了直流LF炉的生产工艺流程、控制系统的总体结构设计、PLC系统选型、控制软件的编制等内容。
The ladle furnace is the most popular refine way in the domestic and foreign. The ladle furnace usually uses the three phase AC arc to heat. Along with the development of the DC arc furnace technology, the ladle furnace also uses DC arc to heat, and has the various merits, has the enormous superiority in particular in the fire-proof material consumption index aspect. This project uses single bar bottom positive electrode, which takes a graphite electrode in the slag level that has the certain resistance, maintains the certain distance between the graphite negative electrodes and the metal molten bath. When DC voltage is added to the electrode, the negative electrode produces the electric arc. The DC constitutes loop through the electrode, the electric arc and the electricity slag, to achieve the goal to heat up the molten steel.
This article aim at some Iron & Steel Corporation Steel Corporation group technology center pilot plant newly built project of 2t DC ladle furnace system, design distributed computer control system which is based on the PROFIBUS-DP. It controls the entire smelting process by using Siemens S7-300 and ET200S series PLC. Using Siemens's WinCC configuration software, to complete monitoring software configuration, to realizes the field data real-time recorded and the monitoring. Having designed smelting time, recording inquiry, read-write parameter and so on. Those have the dynamic operation picture of the Windows style.
In the ladle furnace, heating speed and the temperature of the molten steel completely lie on the electricity control system and the DC voltage control system. The electricity control is plying by the rise and fall speed and the infixed depth of graphite electrode. The voltage control is plying by the output voltage regulation on the DC power supply. But the infixed depth of graphite electrode make effect on the electricity, at the same time it also influenced the voltage. So it made great coupling. The thesis aim at the bad control impact made by the voltage and the electricity coupling in the DC ladle furnace, take into account the characters of the ladle furnace:non-linear, time-variation, inaccurate mathematic model. Design one kind of PID neural network decoupling controller. It combined the conventional PID method and the neural network, decoupling the electricity and the voltage of the ladle furnace, accordingly to have good dynamic and static characteristic, meet the control demand.
引文
1.张鉴.炉外精炼的理论与实践[M],北京：冶金工业出版社,1993,514～539.
2.刘辉杰.LF炉适宜钢种探讨[J],江西冶金,2007,27(2)：23-25.
3.刘川汉.国内LF炉发展现状[J],钢铁技术,2000,16(3)：17～20.
4.桑保华,薛晓中.多变量解耦控制方法[J],火力与指挥控制,2007,32(11)：13-16.
5.李康伟,.王宏力.多变量线性系统解耦控制算法研究[J],计算机测量与控制,2007,15(3)：346～348.
6.刘国海,孙玉坤.磁悬浮开关磁阻电动机径向力的动态解耦控制[J].,东南大学学报,2004,34(增刊)：24-26.
7.周涌,陈庆伟.基于动态神经网络解耦线性化的内模控制[J],南京理工大学学报,2004,28(6)：566-570.
8.张杰,杨翠娥.电加热锅炉系统神经网络PID解耦控制器[J],系统工程理论与实践,,2002,,22(1)：123～127.
9.梁伟平,张健宇.基于神经网络的除氧器水位解耦控制[J],华北电力大学学报,2005,32 (1) ：66-68.
10.王晓哲,顾树生.基于遗传算法的PID神经网络解耦控制[J],控制与决策,1999,14(增刊)：617-619.
11.冯恩波,愈金寿,蒋慰孙.基于神经网络技术的解耦及容错解耦控制策略[J],信息与控制,1992,21(6)：363～365.
12. Czogala and E, Zimmermann H J. Some Aspects of Synthesis of Probabilistic Fuzzy Cont rollers [J], Fuzzy Sets and System,1984,13 (2):169-178.
13.Kiszka JB, Gupta MM, Trojan GM. Multivariable Fuzzy Controller Under Godela Implication [J], Fuzzy Sets and Systems,1990,34 (3):301-321.
14. Walichiewicz L. Decomposition of Linguistic Rule in the Design of a Multidimensional Fuzzy control Algorithm [J], Cybernetics Systems,1984,2 (3):185-193.
15.任新宇,樊思齐.航空发动机多变量自学习模糊解耦控制[J],推进技术,2004,25(6)535～537.
16. Ushiyama H, Secondary steelmaking proceedings in Japan [J], Ironmaking and Steel-making,1978,17(3):121-134.
17.闵义,丁松,谢健,马天玲,姜茂发.LF精炼渣泡沫化的研究[J],工业加热,2007, 36(6)：12-14.
18.李树江,高宪文,柴天佑.钢包炉精炼控制技术的发展与展望[J],冶金自动化,1997,6： 3.
19.李军辉,赵文勇,沈桂根.LF精炼炉造白渣操作实践[J],浙江冶金,2007,2(1)：34-35.
20.虞明全.国外炉外精炼方法概况(3)[J],工业加热,1996,6：8-9.
21.西门子(中国)有限公司自动化与驱动集团.S7-300可编程序控制器硬件与安装手册[M],2002,30-50.
22.叶晓山.Profibus现场总线在工业自动化控制系统中的应用[J],安徽化工,2002,25(3)：45-50.
23.西门子(中国)有限公司.深入浅出西门子WinCC V6[M],北京：北京航空航天大学出版社,2004,5-84.
24.SIEMENS. WinCC组态手册[M],北京：西门子(中国)公司,2003,5-120.
25.SIEMENS. WinCC通讯手册[M],北京：西门子(中国)公司,2003,30-96.
26.陶永华.PID控制原理和自整定策略[J],工业仪表与自动化装置,1997,12(4)：60-64.
27.张宇河.计算机控制系统[M],北京：北京理工大学出版社,1996,18-96.
28. Siemens AG Automation and Drives Group. SIMATIC Stand PID Control manual [M],2001,25-47.
29.王旭.人工神经元网络原理与应用[M],沈阳：东北大学出版社,2000,1-63.
30.程启明,郑勇.多变量PID型神经元网络控制系统及在除氧器水位解耦控制中的仿真研究[J],上海电力学院学报,2007,23(1)：33-37
31.孙建华,汪伟,郑可为.船用核动力装置二回路PID神经网络解耦控制研究[J],哈尔滨工程大学学报,2007,28(6)：656-659
32.苏远平,陈善富.基于PID神经元网络的交流感应电动机解耦控制研究[J],煤矿机电,2007,6：8-11
33.刘娟,杨润贤,郑恩让.一种基于PID神经网络的解耦控制方法的研究[J],微计算机信息,2007,23(9-1)：61-62
2.刘辉杰.LF炉适宜钢种探讨[J],江西冶金,2007,27(2)：23-25.
3.刘川汉.国内LF炉发展现状[J],钢铁技术,2000,16(3)：17～20.
4.桑保华,薛晓中.多变量解耦控制方法[J],火力与指挥控制,2007,32(11)：13-16.
5.李康伟,.王宏力.多变量线性系统解耦控制算法研究[J],计算机测量与控制,2007,15(3)：346～348.
6.刘国海,孙玉坤.磁悬浮开关磁阻电动机径向力的动态解耦控制[J].,东南大学学报,2004,34(增刊)：24-26.
7.周涌,陈庆伟.基于动态神经网络解耦线性化的内模控制[J],南京理工大学学报,2004,28(6)：566-570.
8.张杰,杨翠娥.电加热锅炉系统神经网络PID解耦控制器[J],系统工程理论与实践,,2002,,22(1)：123～127.
9.梁伟平,张健宇.基于神经网络的除氧器水位解耦控制[J],华北电力大学学报,2005,32 (1) ：66-68.
10.王晓哲,顾树生.基于遗传算法的PID神经网络解耦控制[J],控制与决策,1999,14(增刊)：617-619.
11.冯恩波,愈金寿,蒋慰孙.基于神经网络技术的解耦及容错解耦控制策略[J],信息与控制,1992,21(6)：363～365.
12. Czogala and E, Zimmermann H J. Some Aspects of Synthesis of Probabilistic Fuzzy Cont rollers [J], Fuzzy Sets and System,1984,13 (2):169-178.
13.Kiszka JB, Gupta MM, Trojan GM. Multivariable Fuzzy Controller Under Godela Implication [J], Fuzzy Sets and Systems,1990,34 (3):301-321.
14. Walichiewicz L. Decomposition of Linguistic Rule in the Design of a Multidimensional Fuzzy control Algorithm [J], Cybernetics Systems,1984,2 (3):185-193.
15.任新宇,樊思齐.航空发动机多变量自学习模糊解耦控制[J],推进技术,2004,25(6)535～537.
16. Ushiyama H, Secondary steelmaking proceedings in Japan [J], Ironmaking and Steel-making,1978,17(3):121-134.
17.闵义,丁松,谢健,马天玲,姜茂发.LF精炼渣泡沫化的研究[J],工业加热,2007, 36(6)：12-14.
18.李树江,高宪文,柴天佑.钢包炉精炼控制技术的发展与展望[J],冶金自动化,1997,6： 3.
19.李军辉,赵文勇,沈桂根.LF精炼炉造白渣操作实践[J],浙江冶金,2007,2(1)：34-35.
20.虞明全.国外炉外精炼方法概况(3)[J],工业加热,1996,6：8-9.
21.西门子(中国)有限公司自动化与驱动集团.S7-300可编程序控制器硬件与安装手册[M],2002,30-50.
22.叶晓山.Profibus现场总线在工业自动化控制系统中的应用[J],安徽化工,2002,25(3)：45-50.
23.西门子(中国)有限公司.深入浅出西门子WinCC V6[M],北京：北京航空航天大学出版社,2004,5-84.
24.SIEMENS. WinCC组态手册[M],北京：西门子(中国)公司,2003,5-120.
25.SIEMENS. WinCC通讯手册[M],北京：西门子(中国)公司,2003,30-96.
26.陶永华.PID控制原理和自整定策略[J],工业仪表与自动化装置,1997,12(4)：60-64.
27.张宇河.计算机控制系统[M],北京：北京理工大学出版社,1996,18-96.
28. Siemens AG Automation and Drives Group. SIMATIC Stand PID Control manual [M],2001,25-47.
29.王旭.人工神经元网络原理与应用[M],沈阳：东北大学出版社,2000,1-63.
30.程启明,郑勇.多变量PID型神经元网络控制系统及在除氧器水位解耦控制中的仿真研究[J],上海电力学院学报,2007,23(1)：33-37
31.孙建华,汪伟,郑可为.船用核动力装置二回路PID神经网络解耦控制研究[J],哈尔滨工程大学学报,2007,28(6)：656-659
32.苏远平,陈善富.基于PID神经元网络的交流感应电动机解耦控制研究[J],煤矿机电,2007,6：8-11
33.刘娟,杨润贤,郑恩让.一种基于PID神经网络的解耦控制方法的研究[J],微计算机信息,2007,23(9-1)：61-62