转炉提钒智能控制模型的研究与应用
|
摘要
钒是一种具有战略价值的金属材料，在钢铁冶金、有色金属加工、电子和航空航天等方面有着广泛的应用。我国目前已掌握了提钒技术，是世界四大产钒国之一。转炉提钒是我国主要的生产方式。转炉提钒是一个多元多相的高温化学反应过程，反应机理复杂，影响因素众多，受原材料、工艺环境变化的影响较大，不便于人工控制。为提高提钒技术水平，改变生产过程中存在的指标波动大的缺点，需要对生产过程实现计算机控制，以减少提钒操作中人为因素的影响。
论文从工程应用的角度出发，针对我国转炉提钒手工操作的现状，在开发提钒静态模型的过程中，以转炉提钒为特定研究对象，考虑了传统数学模型适应性差、难于移植的缺点，利用神经网络、事例推理等人工智能技术开发出具有较好自学习和自适应能力的静态模型。
论文首先介绍了建立提钒静态模型的意义，讨论了国内外转炉模型的研究应用情况及发展趋势，深入分析了各种建模方法的优缺点。针对提钒过程的复杂性，提出转炉提钒建模应以神经网络为主要技术手段，结合其他人工智能技术，建立起比传统数学模型具有更高精度的模型。
论文结合工艺原理和实际提钒过程的特点，找出提钒过程的内在影响因素和影响静态模型的主要因素。然后分别应用传统BP算法、全量法和增量法建立冷却剂子模型，对三者的拟合和预报精度作了深入分析，并结合统计学习理论针对传统的BP算法存在的问题提出了改进的算法。仿真结果表明该算法具有较快的收敛速度和较强的学习能力。本文同时应用事例推理技术建立枪位控制模型，并给出了具体的实现步骤。
最后，本文使用面向对象的可视化Visual C++ 6 .0编程语言，开发出提钒静态控制模型软件。
As a metallic material with strategy value ,vanadium have got extensive application in steel metallurgy 、metallic machining 、electron and aviation. Now our country has hold the devanadium technology and become one of the four greatest devanadium countries in the world. Oxygen converter devanadium now has become the main production method in our country. The process of devanadium is a high temperature chemical reaction process and the mechanism of reaction is very complex, the affect factor is also so many, and the influence by the variety of raw material and technics condition is also great, so it is very inconvenient to operate manually. In order to improve the level of devanadium technology and change the status that the target in production process is always variable, it is necessary to realization the computer control in production process and reduce the influence by the manual factor.
On the process of developing the static devanadium model and thinking of the status that the devanadium process is still manual operation in our country, the oxygen converter devanadium is chosen as a specific research object from the point of view of industrial application in this paper. And on the basis of considering the demerits of the traditional mathematical model, the paper has developed a perfect self-study and self-adaptive static model by means of some artificial intelligence technology such as NN, CBR etc.
Firstly this paper introduce the meaning of developing devanadium static model and discuss the application and development of the domestic and international research, and then thoroughly analyze the merits and demerits in these model-making methods. Aiming at the complexity of devanadium process, this paper put forward an advice that the more precise model should be made by means of the NN and other AI technology.
On the basis of combining the characteristic of the technics theory and practical devanadium process, this paper conclude the internal affect factor of devanadium process and the main factor of static model. Then the paper develop a refrigerant model by means of traditional BP algorithm、statistics theory 、experience theory and thoroughly analyze the combination of these three methods and their predict precision .And then the paper put forward an advanced algorithm on the basis of combining the statistics theory and the problems on the traditional BP algorithm .The imitate results show that the algorithm has an ability of fast convergence speed and
strong study power. At the same time this paper discuss some technical problem about how to use the CBR technology to establish the spear position control model.
At last, this paper develop a software for devanadium static control model by means of object-oriented programming language Visual C++ 6.0.
论文从工程应用的角度出发，针对我国转炉提钒手工操作的现状，在开发提钒静态模型的过程中，以转炉提钒为特定研究对象，考虑了传统数学模型适应性差、难于移植的缺点，利用神经网络、事例推理等人工智能技术开发出具有较好自学习和自适应能力的静态模型。
论文首先介绍了建立提钒静态模型的意义，讨论了国内外转炉模型的研究应用情况及发展趋势，深入分析了各种建模方法的优缺点。针对提钒过程的复杂性，提出转炉提钒建模应以神经网络为主要技术手段，结合其他人工智能技术，建立起比传统数学模型具有更高精度的模型。
论文结合工艺原理和实际提钒过程的特点，找出提钒过程的内在影响因素和影响静态模型的主要因素。然后分别应用传统BP算法、全量法和增量法建立冷却剂子模型，对三者的拟合和预报精度作了深入分析，并结合统计学习理论针对传统的BP算法存在的问题提出了改进的算法。仿真结果表明该算法具有较快的收敛速度和较强的学习能力。本文同时应用事例推理技术建立枪位控制模型，并给出了具体的实现步骤。
最后，本文使用面向对象的可视化Visual C++ 6 .0编程语言，开发出提钒静态控制模型软件。
As a metallic material with strategy value ,vanadium have got extensive application in steel metallurgy 、metallic machining 、electron and aviation. Now our country has hold the devanadium technology and become one of the four greatest devanadium countries in the world. Oxygen converter devanadium now has become the main production method in our country. The process of devanadium is a high temperature chemical reaction process and the mechanism of reaction is very complex, the affect factor is also so many, and the influence by the variety of raw material and technics condition is also great, so it is very inconvenient to operate manually. In order to improve the level of devanadium technology and change the status that the target in production process is always variable, it is necessary to realization the computer control in production process and reduce the influence by the manual factor.
On the process of developing the static devanadium model and thinking of the status that the devanadium process is still manual operation in our country, the oxygen converter devanadium is chosen as a specific research object from the point of view of industrial application in this paper. And on the basis of considering the demerits of the traditional mathematical model, the paper has developed a perfect self-study and self-adaptive static model by means of some artificial intelligence technology such as NN, CBR etc.
Firstly this paper introduce the meaning of developing devanadium static model and discuss the application and development of the domestic and international research, and then thoroughly analyze the merits and demerits in these model-making methods. Aiming at the complexity of devanadium process, this paper put forward an advice that the more precise model should be made by means of the NN and other AI technology.
On the basis of combining the characteristic of the technics theory and practical devanadium process, this paper conclude the internal affect factor of devanadium process and the main factor of static model. Then the paper develop a refrigerant model by means of traditional BP algorithm、statistics theory 、experience theory and thoroughly analyze the combination of these three methods and their predict precision .And then the paper put forward an advanced algorithm on the basis of combining the statistics theory and the problems on the traditional BP algorithm .The imitate results show that the algorithm has an ability of fast convergence speed and
strong study power. At the same time this paper discuss some technical problem about how to use the CBR technology to establish the spear position control model.
At last, this paper develop a software for devanadium static control model by means of object-oriented programming language Visual C++ 6.0.
引文
[1]黄道鑫主编,提钒炼钢，冶金工业出版社，2000:1
[2]汪子和，国外钒钛工业与钡钛磁铁矿的综合利用，重庆科技情报所，1993
[3]古隆建，我国火法提钒技术的进展及现状，钒钛，1992.6,29-39
[4]攀枝花钢铁研究院情报室，国内提钒工艺的现状与发展，提钒文献汇编，1987，1
[5]陈家祥主编，钢铁冶金学，冶金工业出版社：1990:5
[6] 野板康雄编著，舒诒湘译，钢铁工业中的计算机控制，上海科学技术出版社:1982
[7] S.M. Galloway,M.J.Green,S.R.Balajee,Imporvement in Furnace Performance at Inland Steel Company's No 2 BOF Shop Through Models Utilization and Standardization of Operating Practices. STEELMAKING CONFERENCE PROCEEDINGS. 1991,389~396
[8] T Hara,H Nakamura,M Tawara,et al. DEVELOPMENT OF DIRECT TAPPING AT NO 2 STEELMAKING SHOP IN KURE WORKS,STEELMAKING CONFERENCE PROCEEDINGS ,1989,155-158
[9] A Abbulangelo,M Dalla Rena,M Palcbelli,et.al. BLOWING PATTERN COMPUTERISED CONTROL AT TARANTO STEELSHOP. STEELMAKING CONFERENCE PROCEEDINGS. 19909,337~342
[10] 夏升华，余达太，曹同乐，25t转炉自适应静态模型动态化控制，北京科技大学学报，1998，20，(6)，579~584
[11] KerrTwamura,Masao Furusawa,Masakazu Miyamoto,et al. New Endpoint Control System with Auto-Parameter Turning in BOF. Steelmaking conference proceedings,1995,715-719
[12] D Partington Expert Systems Developments within British Steel Used and Application of Expert Systems and Articial Intelligence,Brussels,Belgium,IISI,1989,15~29
[13]竹中正树，钿中陪男等，NKK技报，1991,(134):23~28
[14]樊俊飞，宝钢转炉吹炼控制模拟在线专家系统，北京科技大学学报1995.17(增刊)，6-11
[15]徐广新，郑德玲，刘宏才，转炉炼钢后期补吹专家系统的建立，冶金自动化，1991，15，(4)：31~34
[16]钢铁研究总院，北京科技大学，转炉炼钢过程控制中的人工智能技术，炼钢，1998(3)，59~63
[17]谢书明，柴天佑,陶钧，一种转炉炼钢动态终点预报的新方法，自动化学报，2001,27,(1),136~139
[18]潘树范，国内外氧气顶吹转炉提钒现状及对攀钢转炉提钒有关问题的探讨，钢铁钒钛，1995.16(1)，6-16
[19]王永骥，涂健编著，神经网络控制，北京，机械工业出版社，1999
[20]韩力群，人工神经网络在控制中的应用及发展前景，北京轻工业学院学报，1996.14(1),32-36
[21]Chandler S R,Mazlock L R. Using Existing Knowledge Sources(cases) to Build an Expert System. Expert System,1991,8(1):3-10
[22]Shouri F. An Expert System for Predicting Gas Demand : A Case Study. Omega,1993,21(3):307-317
[23]Ralph Barletta， 基于事例的推理导论 ，计算机科学,1993，20，(1)，10~14
[24]张建华，王占林，液压系统故障诊断中基于事例的方法，机床与液压，1997，4，44~45
[25]陈洁，刘力丰，基于事例推理的继电保护系统智能设计方法，广东电力，2001，14,(2),41~43
[26]周凯波，魏莹，冯珊，基于安全推理的金融危机预警支持系统，计算机工程与应用，2001，14，18~21
[27]沈钢，许晓鸣，张文渊，连续事例推理智能控制在复杂控制系统中的应用，中国有色金属学报，2002，10，(1)，144~147
[28]杨素波，戈文荪，王建等，攀钢转炉提钒工艺流程中钒的走向分析，钢铁钒钛，1998.19,(2),20-23
[29]过程辩识，方崇智，萧德云编著，北京,清华大学出版社，1998
[30]雷新泉，雷曼，钢铁研究，转炉炼钢静态模型的初步探讨，
钢铁研究，1994，2，8~12
[31]高等过程控制，王桂增，王诗宓编著,清华大学出版社，2002
[32] 渝舒仁，转炉炼钢过程静态控制及其数学模型，炼钢，1995,(6),55~60
[33]Honik K. Approximation Capabilities of Multilayer Feedforwad NetWorks. Neural Networks. 1991,4:551~557
[34]潘正君 ,康立山，陈毓屏编著，演化计算，清华大学出版社，1998
[35]赵新昱，陈文伟，基于遗传建模的公式发现研究，计算机工程与科学，2000，22，(5)，84~87
[36]Vapnik V. The Nature of Statistical Learning Theory,New York:Springer-Verlag,1995
[37]Cortes C,Vapnik V.,Support Vector Networks,Machine Learning ,1995:20:273-297
[38]Vapnik V,Golowich S,Smola A, Support vector method for function approximation,regression estimation ,and signal processing ,In:Mozer M,Jordan M,Petsche T(eds). Neural Information Processing Systems,MIT Press,1997,9
[39] Muller K-R,Smola A J,Ratsch G et al. Predictiong time series with support vector machine . In : Proc. Of ICANN'97, Springer Lecture Notes in Computer Science,1997,999~1005
[40]Vladimir N.Vapnik An Overview of Statstical Learning ,IEEE TRANSACTIONS ON NEURAL 10,(5),1999
[41]张铃，张钹，神经网络中BP算法的分析，模式识别与人工智能，1994，7(9)：191-195
[42]许志端，杨兰蓉，张金隆，基于事例推理系统中事例相似度评估方法若干问题的探讨，厦
门大学学报(自然科学版)，2000.39,(4),441-445
[43]Montazemi A R,Gunpta K M. An adaptive agent for case description in diagnostic CBR system[J]. Journal of Computers in Industry,1996,29(3),209-224
[44]Improving human decision making through case-based decision aiding[J], AI Magazine,1991,12(2):52-68
[45] 吴斌 吴坚 涂序彦，快速遗传算法研究，电子科技大学学报，1999，Vol.28 No.1：49-53
[46] Robert J S，Rovert J M，Dynamic fuzzy control of genetic algorithm parameter coding，IEEE Trans Syst Man，Cybern，1999,29(3):426-433
[47] 董聪，关仪，遗传算法，大自然探索，1998，16（1）：37-41
[48] 董聪 郭晓华 袁曾任，基于广义遗传算法的全局优化方法，计算机科学，Vol.26 No.6 ,1999：7~10
[49] 李聪 张勇 高智，一种新的聚类算法，模式识别与人工智能，1999（6）：205-208
[50]Baker J E. Adaptive Selection Methods for Genetic Algorithms. ,Grefenstette J J(Ed.).Proc. of the 2nd Int'l. Conf. on Genetic Algorithms. Lawrence Earlbaum Associates,Hilladale,NJ,1985
[2]汪子和，国外钒钛工业与钡钛磁铁矿的综合利用，重庆科技情报所，1993
[3]古隆建，我国火法提钒技术的进展及现状，钒钛，1992.6,29-39
[4]攀枝花钢铁研究院情报室，国内提钒工艺的现状与发展，提钒文献汇编，1987，1
[5]陈家祥主编，钢铁冶金学，冶金工业出版社：1990:5
[6] 野板康雄编著，舒诒湘译，钢铁工业中的计算机控制，上海科学技术出版社:1982
[7] S.M. Galloway,M.J.Green,S.R.Balajee,Imporvement in Furnace Performance at Inland Steel Company's No 2 BOF Shop Through Models Utilization and Standardization of Operating Practices. STEELMAKING CONFERENCE PROCEEDINGS. 1991,389~396
[8] T Hara,H Nakamura,M Tawara,et al. DEVELOPMENT OF DIRECT TAPPING AT NO 2 STEELMAKING SHOP IN KURE WORKS,STEELMAKING CONFERENCE PROCEEDINGS ,1989,155-158
[9] A Abbulangelo,M Dalla Rena,M Palcbelli,et.al. BLOWING PATTERN COMPUTERISED CONTROL AT TARANTO STEELSHOP. STEELMAKING CONFERENCE PROCEEDINGS. 19909,337~342
[10] 夏升华，余达太，曹同乐，25t转炉自适应静态模型动态化控制，北京科技大学学报，1998，20，(6)，579~584
[11] KerrTwamura,Masao Furusawa,Masakazu Miyamoto,et al. New Endpoint Control System with Auto-Parameter Turning in BOF. Steelmaking conference proceedings,1995,715-719
[12] D Partington Expert Systems Developments within British Steel Used and Application of Expert Systems and Articial Intelligence,Brussels,Belgium,IISI,1989,15~29
[13]竹中正树，钿中陪男等，NKK技报，1991,(134):23~28
[14]樊俊飞，宝钢转炉吹炼控制模拟在线专家系统，北京科技大学学报1995.17(增刊)，6-11
[15]徐广新，郑德玲，刘宏才，转炉炼钢后期补吹专家系统的建立，冶金自动化，1991，15，(4)：31~34
[16]钢铁研究总院，北京科技大学，转炉炼钢过程控制中的人工智能技术，炼钢，1998(3)，59~63
[17]谢书明，柴天佑,陶钧，一种转炉炼钢动态终点预报的新方法，自动化学报，2001,27,(1),136~139
[18]潘树范，国内外氧气顶吹转炉提钒现状及对攀钢转炉提钒有关问题的探讨，钢铁钒钛，1995.16(1)，6-16
[19]王永骥，涂健编著，神经网络控制，北京，机械工业出版社，1999
[20]韩力群，人工神经网络在控制中的应用及发展前景，北京轻工业学院学报，1996.14(1),32-36
[21]Chandler S R,Mazlock L R. Using Existing Knowledge Sources(cases) to Build an Expert System. Expert System,1991,8(1):3-10
[22]Shouri F. An Expert System for Predicting Gas Demand : A Case Study. Omega,1993,21(3):307-317
[23]Ralph Barletta， 基于事例的推理导论 ，计算机科学,1993，20，(1)，10~14
[24]张建华，王占林，液压系统故障诊断中基于事例的方法，机床与液压，1997，4，44~45
[25]陈洁，刘力丰，基于事例推理的继电保护系统智能设计方法，广东电力，2001，14,(2),41~43
[26]周凯波，魏莹，冯珊，基于安全推理的金融危机预警支持系统，计算机工程与应用，2001，14，18~21
[27]沈钢，许晓鸣，张文渊，连续事例推理智能控制在复杂控制系统中的应用，中国有色金属学报，2002，10，(1)，144~147
[28]杨素波，戈文荪，王建等，攀钢转炉提钒工艺流程中钒的走向分析，钢铁钒钛，1998.19,(2),20-23
[29]过程辩识，方崇智，萧德云编著，北京,清华大学出版社，1998
[30]雷新泉，雷曼，钢铁研究，转炉炼钢静态模型的初步探讨，
钢铁研究，1994，2，8~12
[31]高等过程控制，王桂增，王诗宓编著,清华大学出版社，2002
[32] 渝舒仁，转炉炼钢过程静态控制及其数学模型，炼钢，1995,(6),55~60
[33]Honik K. Approximation Capabilities of Multilayer Feedforwad NetWorks. Neural Networks. 1991,4:551~557
[34]潘正君 ,康立山，陈毓屏编著，演化计算，清华大学出版社，1998
[35]赵新昱，陈文伟，基于遗传建模的公式发现研究，计算机工程与科学，2000，22，(5)，84~87
[36]Vapnik V. The Nature of Statistical Learning Theory,New York:Springer-Verlag,1995
[37]Cortes C,Vapnik V.,Support Vector Networks,Machine Learning ,1995:20:273-297
[38]Vapnik V,Golowich S,Smola A, Support vector method for function approximation,regression estimation ,and signal processing ,In:Mozer M,Jordan M,Petsche T(eds). Neural Information Processing Systems,MIT Press,1997,9
[39] Muller K-R,Smola A J,Ratsch G et al. Predictiong time series with support vector machine . In : Proc. Of ICANN'97, Springer Lecture Notes in Computer Science,1997,999~1005
[40]Vladimir N.Vapnik An Overview of Statstical Learning ,IEEE TRANSACTIONS ON NEURAL 10,(5),1999
[41]张铃，张钹，神经网络中BP算法的分析，模式识别与人工智能，1994，7(9)：191-195
[42]许志端，杨兰蓉，张金隆，基于事例推理系统中事例相似度评估方法若干问题的探讨，厦
门大学学报(自然科学版)，2000.39,(4),441-445
[43]Montazemi A R,Gunpta K M. An adaptive agent for case description in diagnostic CBR system[J]. Journal of Computers in Industry,1996,29(3),209-224
[44]Improving human decision making through case-based decision aiding[J], AI Magazine,1991,12(2):52-68
[45] 吴斌 吴坚 涂序彦，快速遗传算法研究，电子科技大学学报，1999，Vol.28 No.1：49-53
[46] Robert J S，Rovert J M，Dynamic fuzzy control of genetic algorithm parameter coding，IEEE Trans Syst Man，Cybern，1999,29(3):426-433
[47] 董聪，关仪，遗传算法，大自然探索，1998，16（1）：37-41
[48] 董聪 郭晓华 袁曾任，基于广义遗传算法的全局优化方法，计算机科学，Vol.26 No.6 ,1999：7~10
[49] 李聪 张勇 高智，一种新的聚类算法，模式识别与人工智能，1999（6）：205-208
[50]Baker J E. Adaptive Selection Methods for Genetic Algorithms. ,Grefenstette J J(Ed.).Proc. of the 2nd Int'l. Conf. on Genetic Algorithms. Lawrence Earlbaum Associates,Hilladale,NJ,1985