精炼炉底吹氩过程神经网络PID控制与优化研究
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摘要
底吹氩技术是精炼炉进行二次精炼的一项重要技术,对提高钢材的质量具有重要的意义。目前国内的底吹氩基本都是手动操作,工人凭借工作经验来控制流量,这样就增加了失误率并且降低了工作效率。这种落后的监控和管理机制远不能适应生产和科技发展的要求。随着智能控制的迅速发展,实时控制与工艺优化成为当今底吹氩技术发展的趋势。
本文首先介绍了论文的选题背景、底吹氩技术的国内外发展现状,对精炼炉底吹氩过程的工艺、原理、以及功能进行了概述。在此基础上对底吹氩供气系统进行分析,建立了底吹氩供气系统的数学模型。对精炼炉底吹氩供气系统的控制方法进行了研究。考虑到现场许多的干扰因素,底吹氩供气系统模型不易精确确定,对精炼炉底吹氩系统的BP神经网络PID控制方法进行了研究。本文利用MATLAB软件对所建立的模型进行了仿真研究和分析,将BP神经网络PID控制方法应用于底吹氩供气系统。
在VC++6.0的环境下,利用C++语言设计了一套精炼炉底吹氩供气系统,以保证氩气流量能够按照设定值平稳的吹入钢包。首先介绍了系统的硬件选型以及模拟钢包的设计。本系统具有良好的人机界面,主要包括流量的设定、实时数据的动态曲线显示、实时数据的保存等功能。利用物理模拟的方法,在所建立的底吹氩供气系统上进行了底吹氩水模实验。在不同的吹氩量、不同的透气砖数目及其不同的布置方式下,针对搅拌效果开展了工艺优化研究,确定出搅拌效果较好和匀混时间较短的工艺参数,用于指导实际生产。
The bottom blowing argon technology is an important technology in refining furnace for the secondary refining. It has great significance to enhance the quality of steel. At present, the workers operate bottom blowing argon in our country, and manipulate the flow of argon gas through their experience. This increases ratio of lapsus and decreases work efficiency. The laggard monitor and control and management mechanism can't suit to the request of the yield and development of science and technology. Today, precision, convenience, and Real-time control become the developing trend of bottom blowing argon control, along with the progress of intelligent control.
First, it has introduced the background of the topic, the development and current situation of bottom blowing argon technology home and abroad. There is an overview on handicraft, principle and function of furnace bottom blowing argon process. On this basis, there is a analysis about bottom blowing argon gas supply system and establishment its mathematics model. Then researched the control methods of the system. It is not easy for identify the modle accurately of the system because of there are more disruptive factors at the scene. So the BP neural network PID control method of bottom blowing argon gas supply system was researched. The article conduct research and analysis of simulation for the established model make use of the software named MATLAB and apply BP neural network PID control method to bottom blowing argon gas supply system.
It designed a set of refining furnace bottom blowing argon gas supply system make use of C++ under the environment of VC++6.0. The aim is ensure the argon rate of flow into steel ladle reposefully according to set value. Firstly, it introduced the model selection of hardware and the design of simulation ladle. The system has a well human-computer interface.Its fuction includes set flow, dynamic curve display real time data and save real time data. Then did the water model experiment using physical sumulation on the basis of the system. It carries out optimization research to mixing effect at different agron flowrate, different air brick number and different arrange. At last, found the technological parameter what better effect of argon stirring and little mixing time to instructive to active producton mixing time.
本文首先介绍了论文的选题背景、底吹氩技术的国内外发展现状,对精炼炉底吹氩过程的工艺、原理、以及功能进行了概述。在此基础上对底吹氩供气系统进行分析,建立了底吹氩供气系统的数学模型。对精炼炉底吹氩供气系统的控制方法进行了研究。考虑到现场许多的干扰因素,底吹氩供气系统模型不易精确确定,对精炼炉底吹氩系统的BP神经网络PID控制方法进行了研究。本文利用MATLAB软件对所建立的模型进行了仿真研究和分析,将BP神经网络PID控制方法应用于底吹氩供气系统。
在VC++6.0的环境下,利用C++语言设计了一套精炼炉底吹氩供气系统,以保证氩气流量能够按照设定值平稳的吹入钢包。首先介绍了系统的硬件选型以及模拟钢包的设计。本系统具有良好的人机界面,主要包括流量的设定、实时数据的动态曲线显示、实时数据的保存等功能。利用物理模拟的方法,在所建立的底吹氩供气系统上进行了底吹氩水模实验。在不同的吹氩量、不同的透气砖数目及其不同的布置方式下,针对搅拌效果开展了工艺优化研究,确定出搅拌效果较好和匀混时间较短的工艺参数,用于指导实际生产。
The bottom blowing argon technology is an important technology in refining furnace for the secondary refining. It has great significance to enhance the quality of steel. At present, the workers operate bottom blowing argon in our country, and manipulate the flow of argon gas through their experience. This increases ratio of lapsus and decreases work efficiency. The laggard monitor and control and management mechanism can't suit to the request of the yield and development of science and technology. Today, precision, convenience, and Real-time control become the developing trend of bottom blowing argon control, along with the progress of intelligent control.
First, it has introduced the background of the topic, the development and current situation of bottom blowing argon technology home and abroad. There is an overview on handicraft, principle and function of furnace bottom blowing argon process. On this basis, there is a analysis about bottom blowing argon gas supply system and establishment its mathematics model. Then researched the control methods of the system. It is not easy for identify the modle accurately of the system because of there are more disruptive factors at the scene. So the BP neural network PID control method of bottom blowing argon gas supply system was researched. The article conduct research and analysis of simulation for the established model make use of the software named MATLAB and apply BP neural network PID control method to bottom blowing argon gas supply system.
It designed a set of refining furnace bottom blowing argon gas supply system make use of C++ under the environment of VC++6.0. The aim is ensure the argon rate of flow into steel ladle reposefully according to set value. Firstly, it introduced the model selection of hardware and the design of simulation ladle. The system has a well human-computer interface.Its fuction includes set flow, dynamic curve display real time data and save real time data. Then did the water model experiment using physical sumulation on the basis of the system. It carries out optimization research to mixing effect at different agron flowrate, different air brick number and different arrange. At last, found the technological parameter what better effect of argon stirring and little mixing time to instructive to active producton mixing time.
引文
[1]杨乃恒,巴德纯,刘坤.钢液炉外精炼设备真空系统的设计分析.真空, 2007, 44(4): 1-8
[2]王新华.钢铁冶金.北京:高等教育出版社, 2007
[3]李杨.精炼炉底吹氩过程智能控制方法研究. [河北科技大学硕士学位论文]. 2010: 1-2
[4]董锋斌,皇金锋. PLC在钢包底吹氩系统中的应用.仪表技术与传感器, 2006, (3): 25-27
[5]殷瑞钰.合理选择二次精炼技术,推进高效低成本“洁净钢平台”建设.炼钢.2010,26(1):1-6
[6]梁昭峰,李兵,裴旭东.过程控制工程.北京:北京理工大学出版社, 2010
[7]孙洪程,翁维勤,魏杰.过程控制系统及工程.第3版.北京:化学工业出版社, 2010
[8]方康玲.过程控制与集散系统.北京:电子工业出版社, 2009
[9]王树青.过程控制工程.第3版.北京:化学工业出版社, 2008
[10] L. JONSSON, P. JONSSON. Modeling of fluid flow conditions around the slag metal interface in a gas stirred ladle. ISIJ International, 1996, 36(9): 1127-1134
[11]何治斌.基于组态王DY200010-00液压克令吊模糊PID控制. [大连海事大学硕士学位论文]. 2005: 23-24
[12]刘金琨.智能控制.第2版.北京:电子工业出版社, 2009
[13]朱凯,王正林.精通MATLAB神经网络.北京:电子工业出版社. 2010
[14] X. L. SONG. Robust PID Control for Steam Superheater. International Conference on Machine Learning and Cybernetice, 2004, (1): 253-257
[15]师黎,陈铁军,李晓媛,等.智能控制理论及应用.北京:清华大学出版社, 2009
[16] X. L. WU, J. H. ZHANG, X. P. GUAN. Stability of Impulsive Cellular Neural Network with time delay. Advances in Systems Science and Applications, 2009, 9(3): 620~625
[17] R. BABUSKA, J. OOSTERHOFF, A. OUDSHOORNB. Fuzzy Self-tuning PI Control of PH in Fermentation. Engineering Applications of Artificial Intelligence, 2002, 15: 3-15
[18] X. L. WU, J. H. ZHANG, X. P. GUAN. Delay-dependent Asymptotic Stability of BAM Neural Networks with Time Delay. Kybernetes, 2010, 39(8): 1313-1321
[19] Z. Y. SONG, C. Y. LIU, X. L. SONG. Application of Self-Tuning PID Control Based on Diagonal Recurrent Neural Network in Crystallization Process. Proceedings of 2006 International Conference on Machine Learning and Cybernetics, 2006, 1(3): 380-384
[20]吴学礼,贾辉然,孟华.非线性过程的智能控制方法研究与应用.北京:国防工业出版社, 2006: 3-5
[21]史纪波,吴学礼,杜文霞,等.精炼炉底吹氩工艺实验系统的设计与实现.河北工业科技, 2010, 27(6): 393-395
[22]蒋宏伟.面向对象的可视化编程方法.信息技术, 2004, 28(6): 60-61
[23]谭浩强. C++程序设计.北京:清华大学出版社, 2006
[24] J. P. COHOON, J. W. DACIDSON. C++ program design.Beijing:Publishing House of Electronics Industry, 2002
[25]孙鑫,余安萍. VC++深入详解.北京:电子工业出版社, 2006
[26]刘书智,李琳娜. Visual C++实践与提高.北京:中国铁道出版社, 2009
[27]孔鹏. Visual C++ 6.0完全自学手册.北京:机械工业出版社, 2007
[28]吕凤翥. C++语言简明教程.北京:清华大学出版社, 2003
[29]胡志坤. Visual C++ 6.0通信编程工程实例详解.北京:机械工业出版社, 2007
[30]张越. Visual C++ 6.0网络程序设计实例详解.北京:人民邮电出版社, 2006
[31] K. ZENG, L. WANG, S. X. ZHENG. A Simple and Easy Communication Realization Between Microcomputer and PLC Based on VC++6.0. Journal of Hunan Institute of Engineering. 2003, 25(9): 61-63
[32] M. YUAN. Research of Visual C++ Accessing Database Using ODBC and OLE DB. Computer Knowledge and Technology. 2009, 32(5): 2550-2552
[33]刘影. PCL-818L数据采集卡的应用.现代制造技术与装备, 2006, (4): 72-73
[34]张德明,吴光强,程慧勇. C++ Builder环境下PCL-818L在车辆换挡仿真试验中的应用.传动技术, 2008, 22(1): 45-48
[35]郭清华,赵英红,郭利,等.研华PCL-818L数据采集卡在染色机监控系统中的应用.工业.控制计算机, 2006, 19(7): 27-28
[36]李燕飞,陈国均,张卫东,等.组态王环境下数据库访问技术及其应用.微计算机信息, 2004, 20(12): 5-6
[37]孙明革,朱喜林.基于组态王软件下的SQL数据库技术.微计算机信息, 2006, 22(7): 109-111
[38]吉杰,董智勇,王炜.基于DataSet和ADO.NET的文献检索列表转换软件设计.情报杂志, 2009, 8(6): 116-121
[39]吴清涛.基于ADO.NET的数据访问模型研究.武夷学院学报, 2009, 28(5): 67-70
[40]侯其锋,李晓华,李莎. Visual C++数据库通用模块开发与系统移植.北京:清华大学出版社, 2007
[41]徐照兴,王斌. ADO.NET访问数据库的方法及步骤.中国科技信息, 2009, (22): 105-132
[42]张振国,谈恩燕. ADO.NET在农业信息网上的应用研究.安徽农业科学, 2006, 34(3): 603-605
[43]郑阿奇. SQL Server教程.北京:清华大学出版社, 2005
[44]李伟红. SQL Server 2000数据库及应用.北京:人中国水利水电出版社, 2007
[45]李丹丹,史秀璋. SQL Server2000数据库实训教程.北京:清华大学出版社, 2007
[46]朱苗勇,肖泽强.钢的精炼过程数学物理模拟.北京:冶金工业出版社, 1998
[47] S. TANIGUCHI, S. KAWAGUCHI, A. KIKUCHI. Fluid flow and gas-liquid mass transfer in gas-injected vessels. Applied Mathematical Modeling, 2002,26: 249-262
[48] K. NAKANISHI, T. FUJII, J. SZEKELY. Quality and yield improvement using method of blowing Argon stirring.Ironmaking and Steelmaking, 1995, 2: 193
[49] T. AKIHIKO, O. HIROYUKI. Influence of micro-hardness and inclusion on stress oriented hydrogen induced cracking of line pipe steels.ISIJ International, 1996, 36(3): 334-336.
[50]巨建涛,杜俊峰.60吨LF炉底吹氩的水模研究.四川冶金, 2005, 27(3): 1-3
[51]任三兵,陈义胜.大型钢包双孔吹氩最佳位置的探讨.包头钢铁学院学报, 2003, 22 (3): 193-197
[52] T. GLADMAN. Developments in inclusions and their effects on steel properties. Ironmaking and Steelmaking, 1992, 19(6): 457-453.
[53] D. C. GUO, L. GU, G. A. IRONS. Developments in modeling of gas injection and slag foaming. Applied Mathematical Modeling, 2002,26: 263-280
[54]幸伟.钢包底吹氩工艺开发. [武汉科技大学硕士论文]. 2005: 35-46
[55] F. D. RICHARDSON. Physical Chemistry of Melts in Metallurgy. New York: Academic Press, 1994
[56] D. MAZUMDAR, C. SEYBETR, D. STEINGART. Transient flows in gas stirred vessels during initial and post gas injection periods.ISIJ International, 2003, 23(1): 132-134.
[57] J. H. GREVET, J. SZEKELY, N. EI-KADDAH. Experimental and theoretical study of gas bubble driven circulation systems. Heat Mass Transfer, 1982, 25: 87-497.
[58] R. AUSTNIJ, M. CAMPLNI. Mathematical Modelling of Thermal Stratification and Darinage of Stell Ladles. ISIJ Internation, 1992, 32(2): 196-202
[59] U. CMADALI, M. TUNC, F. DIKEE. A thermodymatic analysis of a steel production step carried out in the ladle furnace. Applied Thmeral Engineering, 2001, 21: 643-655
[60] C. G. MNEDE, N. NIGRO, A. CARDONA. Drag and non-drga force ifluences in Numerical simulations of metallurgical ldales. Journal of Materials Processing Technology, 2005, 160: 296-305
[2]王新华.钢铁冶金.北京:高等教育出版社, 2007
[3]李杨.精炼炉底吹氩过程智能控制方法研究. [河北科技大学硕士学位论文]. 2010: 1-2
[4]董锋斌,皇金锋. PLC在钢包底吹氩系统中的应用.仪表技术与传感器, 2006, (3): 25-27
[5]殷瑞钰.合理选择二次精炼技术,推进高效低成本“洁净钢平台”建设.炼钢.2010,26(1):1-6
[6]梁昭峰,李兵,裴旭东.过程控制工程.北京:北京理工大学出版社, 2010
[7]孙洪程,翁维勤,魏杰.过程控制系统及工程.第3版.北京:化学工业出版社, 2010
[8]方康玲.过程控制与集散系统.北京:电子工业出版社, 2009
[9]王树青.过程控制工程.第3版.北京:化学工业出版社, 2008
[10] L. JONSSON, P. JONSSON. Modeling of fluid flow conditions around the slag metal interface in a gas stirred ladle. ISIJ International, 1996, 36(9): 1127-1134
[11]何治斌.基于组态王DY200010-00液压克令吊模糊PID控制. [大连海事大学硕士学位论文]. 2005: 23-24
[12]刘金琨.智能控制.第2版.北京:电子工业出版社, 2009
[13]朱凯,王正林.精通MATLAB神经网络.北京:电子工业出版社. 2010
[14] X. L. SONG. Robust PID Control for Steam Superheater. International Conference on Machine Learning and Cybernetice, 2004, (1): 253-257
[15]师黎,陈铁军,李晓媛,等.智能控制理论及应用.北京:清华大学出版社, 2009
[16] X. L. WU, J. H. ZHANG, X. P. GUAN. Stability of Impulsive Cellular Neural Network with time delay. Advances in Systems Science and Applications, 2009, 9(3): 620~625
[17] R. BABUSKA, J. OOSTERHOFF, A. OUDSHOORNB. Fuzzy Self-tuning PI Control of PH in Fermentation. Engineering Applications of Artificial Intelligence, 2002, 15: 3-15
[18] X. L. WU, J. H. ZHANG, X. P. GUAN. Delay-dependent Asymptotic Stability of BAM Neural Networks with Time Delay. Kybernetes, 2010, 39(8): 1313-1321
[19] Z. Y. SONG, C. Y. LIU, X. L. SONG. Application of Self-Tuning PID Control Based on Diagonal Recurrent Neural Network in Crystallization Process. Proceedings of 2006 International Conference on Machine Learning and Cybernetics, 2006, 1(3): 380-384
[20]吴学礼,贾辉然,孟华.非线性过程的智能控制方法研究与应用.北京:国防工业出版社, 2006: 3-5
[21]史纪波,吴学礼,杜文霞,等.精炼炉底吹氩工艺实验系统的设计与实现.河北工业科技, 2010, 27(6): 393-395
[22]蒋宏伟.面向对象的可视化编程方法.信息技术, 2004, 28(6): 60-61
[23]谭浩强. C++程序设计.北京:清华大学出版社, 2006
[24] J. P. COHOON, J. W. DACIDSON. C++ program design.Beijing:Publishing House of Electronics Industry, 2002
[25]孙鑫,余安萍. VC++深入详解.北京:电子工业出版社, 2006
[26]刘书智,李琳娜. Visual C++实践与提高.北京:中国铁道出版社, 2009
[27]孔鹏. Visual C++ 6.0完全自学手册.北京:机械工业出版社, 2007
[28]吕凤翥. C++语言简明教程.北京:清华大学出版社, 2003
[29]胡志坤. Visual C++ 6.0通信编程工程实例详解.北京:机械工业出版社, 2007
[30]张越. Visual C++ 6.0网络程序设计实例详解.北京:人民邮电出版社, 2006
[31] K. ZENG, L. WANG, S. X. ZHENG. A Simple and Easy Communication Realization Between Microcomputer and PLC Based on VC++6.0. Journal of Hunan Institute of Engineering. 2003, 25(9): 61-63
[32] M. YUAN. Research of Visual C++ Accessing Database Using ODBC and OLE DB. Computer Knowledge and Technology. 2009, 32(5): 2550-2552
[33]刘影. PCL-818L数据采集卡的应用.现代制造技术与装备, 2006, (4): 72-73
[34]张德明,吴光强,程慧勇. C++ Builder环境下PCL-818L在车辆换挡仿真试验中的应用.传动技术, 2008, 22(1): 45-48
[35]郭清华,赵英红,郭利,等.研华PCL-818L数据采集卡在染色机监控系统中的应用.工业.控制计算机, 2006, 19(7): 27-28
[36]李燕飞,陈国均,张卫东,等.组态王环境下数据库访问技术及其应用.微计算机信息, 2004, 20(12): 5-6
[37]孙明革,朱喜林.基于组态王软件下的SQL数据库技术.微计算机信息, 2006, 22(7): 109-111
[38]吉杰,董智勇,王炜.基于DataSet和ADO.NET的文献检索列表转换软件设计.情报杂志, 2009, 8(6): 116-121
[39]吴清涛.基于ADO.NET的数据访问模型研究.武夷学院学报, 2009, 28(5): 67-70
[40]侯其锋,李晓华,李莎. Visual C++数据库通用模块开发与系统移植.北京:清华大学出版社, 2007
[41]徐照兴,王斌. ADO.NET访问数据库的方法及步骤.中国科技信息, 2009, (22): 105-132
[42]张振国,谈恩燕. ADO.NET在农业信息网上的应用研究.安徽农业科学, 2006, 34(3): 603-605
[43]郑阿奇. SQL Server教程.北京:清华大学出版社, 2005
[44]李伟红. SQL Server 2000数据库及应用.北京:人中国水利水电出版社, 2007
[45]李丹丹,史秀璋. SQL Server2000数据库实训教程.北京:清华大学出版社, 2007
[46]朱苗勇,肖泽强.钢的精炼过程数学物理模拟.北京:冶金工业出版社, 1998
[47] S. TANIGUCHI, S. KAWAGUCHI, A. KIKUCHI. Fluid flow and gas-liquid mass transfer in gas-injected vessels. Applied Mathematical Modeling, 2002,26: 249-262
[48] K. NAKANISHI, T. FUJII, J. SZEKELY. Quality and yield improvement using method of blowing Argon stirring.Ironmaking and Steelmaking, 1995, 2: 193
[49] T. AKIHIKO, O. HIROYUKI. Influence of micro-hardness and inclusion on stress oriented hydrogen induced cracking of line pipe steels.ISIJ International, 1996, 36(3): 334-336.
[50]巨建涛,杜俊峰.60吨LF炉底吹氩的水模研究.四川冶金, 2005, 27(3): 1-3
[51]任三兵,陈义胜.大型钢包双孔吹氩最佳位置的探讨.包头钢铁学院学报, 2003, 22 (3): 193-197
[52] T. GLADMAN. Developments in inclusions and their effects on steel properties. Ironmaking and Steelmaking, 1992, 19(6): 457-453.
[53] D. C. GUO, L. GU, G. A. IRONS. Developments in modeling of gas injection and slag foaming. Applied Mathematical Modeling, 2002,26: 263-280
[54]幸伟.钢包底吹氩工艺开发. [武汉科技大学硕士论文]. 2005: 35-46
[55] F. D. RICHARDSON. Physical Chemistry of Melts in Metallurgy. New York: Academic Press, 1994
[56] D. MAZUMDAR, C. SEYBETR, D. STEINGART. Transient flows in gas stirred vessels during initial and post gas injection periods.ISIJ International, 2003, 23(1): 132-134.
[57] J. H. GREVET, J. SZEKELY, N. EI-KADDAH. Experimental and theoretical study of gas bubble driven circulation systems. Heat Mass Transfer, 1982, 25: 87-497.
[58] R. AUSTNIJ, M. CAMPLNI. Mathematical Modelling of Thermal Stratification and Darinage of Stell Ladles. ISIJ Internation, 1992, 32(2): 196-202
[59] U. CMADALI, M. TUNC, F. DIKEE. A thermodymatic analysis of a steel production step carried out in the ladle furnace. Applied Thmeral Engineering, 2001, 21: 643-655
[60] C. G. MNEDE, N. NIGRO, A. CARDONA. Drag and non-drga force ifluences in Numerical simulations of metallurgical ldales. Journal of Materials Processing Technology, 2005, 160: 296-305