连铸二冷过程建模及配水的智能优化研究
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摘要
随着连铸技术的发展,提高连铸坯产量和质量成为连铸技术研究的主要问题之一。由于铸坯产品的质量与铸坯凝固过程密切相关,而连铸坯的凝固基本上是在二次冷却区内完成的,所以研究二冷热传输过程对了解铸坯凝固行为和保证铸坯质量具有重要的意义。多年来,人们对铸坯内部质量的研究主要通过各种试验、检测手段以及数值模拟技术完成。随着计算机技术的发展,数值模拟技术已成为研究连铸二冷过程的一种重要手段。
首先,本文针对连铸小方坯的凝固热传输过程,结合南钢公司的生产实际进行了机理模型的研究,建立了方坯凝固传热的二维移动薄片模型。在此基础上采用有限单元方法数值求解模型,并运用热焓法处理凝固潜热,得到了一定工艺条件下的稳态凝固温度场。利用南钢现场对三种不同钢种的射钉试验数据,对比模型计算的数据验证了数值模型的准确性和有效性。此外还分析了拉速、过热度和冷却水量等因素对稳态温度场的影响。
其次,通过建立和求解三维瞬态热传输模型来研究铸坯凝固温度场的瞬态特性。实际生产中的铸坯温度场总是处于一个较长过渡的过程,这说明了铸坯凝固系统是一个大惯性的系统。以往的研究很少考虑凝固温度场的瞬态调节过程,而忽略温场瞬态特性的配水控制方法显然不能保证铸坯的产品质量。特别是生产条件变化频繁的时候,铸坯表面温度将发生较大的波动,甚至产生温度“尖峰”,这在一定程度上影响了铸坯产品的质量。本文通过大量的仿真研究了当拉速、过热度和冷却水量发生突变时,铸坯凝固温度场的动态响应过程。并通过引入“有效拉速”的处理方法对实测拉速值进行滤波处理,很大程度上避免了由于生产条件变化而造成的铸坯表面温度的“尖峰”波动,减少了对铸坯质量的不利影响。
第三,针对二冷制度的定量优化问题,研究了二冷冶金准则函数优化法,通过多目标准则函数之间的相互关系建立了二冷制度优化总体目标函数。研究了基于群智能的搜索算法——粒子群算法,并采用嵌入局部搜索策略的手段提出了一种改进的混合粒子群算法,以此来提高连铸二冷配水的函数优化搜索效率,增强优化的实时性。在实现二冷水量离线优化的基础上,分析了钢水过热度对铸坯凝固过程的影响,并通过数据拟合的方法得到了一种基于拉速—过热度—水量最优关系方程的动态控制模型。
最后,本文采用Matlab和C语言混合编程的方法开发了连铸坯二维和二维传热模型的求解程序。在此基础上结合优化算法程序建立了二冷配水优化仿真平台,该平台通过友好的人机界面实现模型参数的输入、温度场数据图形表示和优化结果显示等功能。
With the development of continuous casting technique, improving output and quality of products becomes an important problem in continuous casting research. Quality of billets is related to the solidifying process and casting billets solidify completely in secondary cooling zone, so the reaserch of solidifying in secondary cooling zone is significant to understand solidification behavior and insure the products quality. For many years, people study continuous casting by means of experiments, measurement and numerical simulating. With the development of computing tecnique, numerical simulating has become an important method for continuous casting study.Firstly, for heat transfer process in billets continuous casting, this paper studies heat transfer model and develops 2D moving-slice model combining the practice of NanGang steel plant. Using enthalpy to deal with latent heat of solidification the numerical model is solved by finite element method, and a steady temperature field can be obtained under a certain condition. Accuracy and reliability of numerical model are validated by comparing measuring data with model computing data. Furthermore, casting speed, superheat and cooling water's effects on steady temperature field are analyzed in this paper.Secondly, temperature transition speciality is studied through developing and solving 3D transient heat transfer model. In practice, beillets temperature field is always in a long transition, which shows that solidification in continuous casting is an inertial system. Temperature transition is almost not considered in previous study on continuous casting, and products quality can't be insured with control method that ignores temperature transition. Especially, when production setting varies frequently, billets surface temperature will undulate greatly, even appears "pinnacle". This phenomenon impacts products quality strongly. Temperature field dynamic response to the saltation of casting speed, superheat and cooling water is investigated in this paper based on a lot of simulation computing. "Effective casting speed" is adopted and measured speed is filtered in order to avoid appearing "pinnacle" during cooling water saltation, which can reduce disadvantages to billets quality.Thirdly, to optimizng quantificationally secondary cooling system in continuous casting, optimization method besed on metallurgy criterion function is studied. Based on the relation among multiobjective functions, a general objective function for secondary cooling is formulated. Moreover, swarm intillegenec-based search algorithm-particle swarm algorithm is investigated,
and a new hybrid evolutionary-based method combining particle swarm algorithm and the chaotic search is proposed in order to improve search efficiency in optimizing secondary cooling water. With the aid of cooling water optimization offline, a new dynamic control model based on optimized relation among speed, superheat and cooling water is obtained considering superheat's effects on temperature field.Lastly, software for solving 2D, 3D heat transfer model is developed combining MATLAB and C. Based on intillegence search algorithms, a simulating platform for secondary cooling water optimization in continuous casting is established in order to realize inputing parameters, outputing parameters, showing temperature field data and displaying optimization results by friendly interface.
首先,本文针对连铸小方坯的凝固热传输过程,结合南钢公司的生产实际进行了机理模型的研究,建立了方坯凝固传热的二维移动薄片模型。在此基础上采用有限单元方法数值求解模型,并运用热焓法处理凝固潜热,得到了一定工艺条件下的稳态凝固温度场。利用南钢现场对三种不同钢种的射钉试验数据,对比模型计算的数据验证了数值模型的准确性和有效性。此外还分析了拉速、过热度和冷却水量等因素对稳态温度场的影响。
其次,通过建立和求解三维瞬态热传输模型来研究铸坯凝固温度场的瞬态特性。实际生产中的铸坯温度场总是处于一个较长过渡的过程,这说明了铸坯凝固系统是一个大惯性的系统。以往的研究很少考虑凝固温度场的瞬态调节过程,而忽略温场瞬态特性的配水控制方法显然不能保证铸坯的产品质量。特别是生产条件变化频繁的时候,铸坯表面温度将发生较大的波动,甚至产生温度“尖峰”,这在一定程度上影响了铸坯产品的质量。本文通过大量的仿真研究了当拉速、过热度和冷却水量发生突变时,铸坯凝固温度场的动态响应过程。并通过引入“有效拉速”的处理方法对实测拉速值进行滤波处理,很大程度上避免了由于生产条件变化而造成的铸坯表面温度的“尖峰”波动,减少了对铸坯质量的不利影响。
第三,针对二冷制度的定量优化问题,研究了二冷冶金准则函数优化法,通过多目标准则函数之间的相互关系建立了二冷制度优化总体目标函数。研究了基于群智能的搜索算法——粒子群算法,并采用嵌入局部搜索策略的手段提出了一种改进的混合粒子群算法,以此来提高连铸二冷配水的函数优化搜索效率,增强优化的实时性。在实现二冷水量离线优化的基础上,分析了钢水过热度对铸坯凝固过程的影响,并通过数据拟合的方法得到了一种基于拉速—过热度—水量最优关系方程的动态控制模型。
最后,本文采用Matlab和C语言混合编程的方法开发了连铸坯二维和二维传热模型的求解程序。在此基础上结合优化算法程序建立了二冷配水优化仿真平台,该平台通过友好的人机界面实现模型参数的输入、温度场数据图形表示和优化结果显示等功能。
With the development of continuous casting technique, improving output and quality of products becomes an important problem in continuous casting research. Quality of billets is related to the solidifying process and casting billets solidify completely in secondary cooling zone, so the reaserch of solidifying in secondary cooling zone is significant to understand solidification behavior and insure the products quality. For many years, people study continuous casting by means of experiments, measurement and numerical simulating. With the development of computing tecnique, numerical simulating has become an important method for continuous casting study.Firstly, for heat transfer process in billets continuous casting, this paper studies heat transfer model and develops 2D moving-slice model combining the practice of NanGang steel plant. Using enthalpy to deal with latent heat of solidification the numerical model is solved by finite element method, and a steady temperature field can be obtained under a certain condition. Accuracy and reliability of numerical model are validated by comparing measuring data with model computing data. Furthermore, casting speed, superheat and cooling water's effects on steady temperature field are analyzed in this paper.Secondly, temperature transition speciality is studied through developing and solving 3D transient heat transfer model. In practice, beillets temperature field is always in a long transition, which shows that solidification in continuous casting is an inertial system. Temperature transition is almost not considered in previous study on continuous casting, and products quality can't be insured with control method that ignores temperature transition. Especially, when production setting varies frequently, billets surface temperature will undulate greatly, even appears "pinnacle". This phenomenon impacts products quality strongly. Temperature field dynamic response to the saltation of casting speed, superheat and cooling water is investigated in this paper based on a lot of simulation computing. "Effective casting speed" is adopted and measured speed is filtered in order to avoid appearing "pinnacle" during cooling water saltation, which can reduce disadvantages to billets quality.Thirdly, to optimizng quantificationally secondary cooling system in continuous casting, optimization method besed on metallurgy criterion function is studied. Based on the relation among multiobjective functions, a general objective function for secondary cooling is formulated. Moreover, swarm intillegenec-based search algorithm-particle swarm algorithm is investigated,
and a new hybrid evolutionary-based method combining particle swarm algorithm and the chaotic search is proposed in order to improve search efficiency in optimizing secondary cooling water. With the aid of cooling water optimization offline, a new dynamic control model based on optimized relation among speed, superheat and cooling water is obtained considering superheat's effects on temperature field.Lastly, software for solving 2D, 3D heat transfer model is developed combining MATLAB and C. Based on intillegence search algorithms, a simulating platform for secondary cooling water optimization in continuous casting is established in order to realize inputing parameters, outputing parameters, showing temperature field data and displaying optimization results by friendly interface.
引文
1.蔡开科.连续铸钢500问[M],北京:冶金工业出版社,1994,100-130
2.干勇,仇圣桃,萧泽强.连续铸钢过程数学物理模拟[M],北京:冶金工业出版社,2001,20-280
3.张富强,王久彬.连铸坯凝固过程中传输现象基础知识系列讲座[J],鞍钢技术,1999:35-40
4.姜永林.连铸生产概论[M],沈阳:东北工学院出版社,1992,45-107
5.马竹梧,邹立功等.钢铁工业自动化(炼钢卷)[M],北京:冶金工业出版社,2003,200-240
6.徐荣军.连铸二冷水热传输及人工智能优化模型与控制[D],上海:中国科学院上海冶金研究所,2000
7.郭戈,王伟,柴天佑.连铸过程自动化技术综述[J],冶金自动化,2002,2:10-14
8.张振山,柴天佑,王伟.抚钢合金钢方坯连铸机二次冷却控制[J],冶金自动化,1999,5:32-35
9.杨全,张真.金属凝固与铸造过程数值模拟[M],杭州:浙江大学出版社,1996,45-48
10. S. K. Choudhary, D Mazumdar. Mathematical modeling of heat transfer phenomena in continuous casting of steel[J], ISIJ, 1993, 33:764-774
11.李海梅,顾元宪,申长雨.平面相变热传导问题等效热容法的有限元解[J],大连理工大学学报,2000,40:45-48
12.孔祥谦.有限单元法在传热学中的应用(第三版)[M],北京:科学出版社,1998,12-13
13.王成,邵敏.有限单元法基本原理和数值方法[M],北京:清华大学出版社,1997,90-98
14. C. R. Swaminathan, V. R. Voller. A general enthalpy method for modeling solidification processes[J], Metallurgical transactions, 1992, 23:651-664
15.张铁,闫家斌.数值分析[M],北京:冶金工业出版社,2001,60-69
16. C. A. Santos, J. A. Spim, A. Garcia. Mathematical modeling and optimization strategies(genetic algorithm and knowledge base) applied to the continuous casting of steel[J], Engineering applications of artificial, 2003, 16:511-527
17.郭戈,乔俊飞,王伟.铸坯凝固过程计算机模拟[J],中国有色金属学报,1999,9-2:339-344
18. M. Salcudean, Z. Abdullah. On the numerical modeling of heat transfer during solidification processes[J], IJNME, 1988, 25: 445-473
19.孙海峰.威钢ROKOP高效方坯连铸二次冷却的研究[D],重庆:重庆大学,2002
20. Stefano Barozzl, Piergiorgio. Computer control and optimization of secondary cooling during continuous casting[J], Iron and steel Engineer, 1986, 11:21-26
21. D. Constales, J. Kacur, R. Van Keer. On the optimal cooling strategy for variable-speed continuous casting[J], Numerical methods in engineering, 2002, 53:539-565
22. N. Chakraborti, R. S. P. Gupta, T. K. Tiwari. Optimization of continuous casting process using genetic algorithms: studies of spray and radiation cooling regions[J], Ironmaking and steelmaking, 2003, 30(4): 273-278
23. Kaisa Miettinen. Optimal control of continuous casting by nondifferentiable multiobjective optimization[J], Computational optimization and application, 1998, 11:209-225
24. B. Lally, L. T. Biegler. Optimization and continuous casting: Part 1. Problem formation and solution strategy[J], Metallurgical transactions, 1991, 10:641-648
25. B. Lally, L. T. Biegler. Optimization and continuous casting: Part 2. Application to industrial casters[J]. Metallurgical transactions, 1991, 10:460-468
26. Carlos A. Santos, Jaime A. Spim Jr, Maria C. F. Ierardi. The use of artificial intelligence technique for the optimization of process parameters used in the continuous of steel[J], Applied Mathematical Modelling, 2002, 26: 102-115
27. N. Chakraborti, R. S. P. Gupta, T. K. Tiwari. Optimazation of continuous casting process using genetic algorithms: studies of spray and radiation cooling regions[J], Ironmaking and Dteelmaking 2003, 30:46-54
28.王小平,曹立明.遗传算法[M],西安:西安交通大学出版社,2002,300-350
29.王凌.智能优化算法及其应用[M],北京:清华大学出版社,2001,40-46
30. K. E. Parsopoulos. Recent approaches to global optimization problems through particle swarm optimization[J], Natrual Computing, 2002, 1: 235-306
31. James Kennedy, Russel Eberhart. Particle swarm optimization[J], IEEE Proceeding of Neural Networks, 1995: 1942-1948
32.谢晓锋,张文俊,杨之廉.微粒群算法综述[J],控制与决策,2003,Vol.18 No.2:129-134
33.李爱国.粒子群优化算法[J],计算机工程与应用,2002,21:1-3
34. Maurice Clerc, James Kennedy. The particle swarm explosion, stability, and convergence in a multidimensional complex space[J], IEEE transaction on evolutionary computation, 2002, 6 (1): 1200-1206
35. Ioan Cristian Trelea. The particle swarm optimization algorithm: convergence analysis and parameter selection[J], Information Processing Letters, 2003, 85:11-45
36. J. F. Schutte. Parallel global optimization with the particle swarm algorithm[J], Int. J. Numer. Meth. Engng, 2003: 45-65
37.柯晶,钱积新,乔谊正.一种改进粒子群优化算法[J],电路与系统学报,2003,18(5):87-91
38. Xiao-Feng Xie. Adaptive particle swarm optimization on individual level[J], Proceeding ICSP, 2002: 1215-1218
39. Gwo-Ching Liao, Ta-Peng Tsao. Application embedded chaos search immune genetic algorithm for short-term unit commitment[J], Electric Power Systems Research, 2004, 71: 135-144
40. Matt Settles, Bark Rylander. Neural network learning using particle swarm optimizers[J], IEEE transaction on evolutionary computation, 2000: 600-604
41. Richard A. Hardin, Kailiu, Atul Kapoor. A Transient Simulation and Dynamic Spray Cooling Control Model for Continuous Steel Casting[J], Metallurgical and materials transactions, 2003, 34: 297-306
42. Ferdiando Roux, lan Keith Craig. Specification Framework for Control of the Secondary Cooling Zone in Continuous Casting[J], ISIJ International, 1998, 38:203-211
43.孟红记,武荣阳,谢植.小方坯连铸机二次冷却动态控制模型的研究[J],连铸二冷技术交流会论文集,2002:15-19
44. N. Cheung, A. Garcia. The use of a heuristic search technique for the optimization of quality of steel billets produced by continuous cating[J], Engineering applications of artificial intelligence, 2001, 14: 229-238
45. J. Szimmat. Numerical simulation of solidification processes in enclosures[J], Heat and Mass Transfer, 2002, 38: 279-293
46.薛定宇,陈阳泉.基于MATLAB/Simulink的系统仿真技术与应用[M],北京:清华大学出版社,2002,17-93
2.干勇,仇圣桃,萧泽强.连续铸钢过程数学物理模拟[M],北京:冶金工业出版社,2001,20-280
3.张富强,王久彬.连铸坯凝固过程中传输现象基础知识系列讲座[J],鞍钢技术,1999:35-40
4.姜永林.连铸生产概论[M],沈阳:东北工学院出版社,1992,45-107
5.马竹梧,邹立功等.钢铁工业自动化(炼钢卷)[M],北京:冶金工业出版社,2003,200-240
6.徐荣军.连铸二冷水热传输及人工智能优化模型与控制[D],上海:中国科学院上海冶金研究所,2000
7.郭戈,王伟,柴天佑.连铸过程自动化技术综述[J],冶金自动化,2002,2:10-14
8.张振山,柴天佑,王伟.抚钢合金钢方坯连铸机二次冷却控制[J],冶金自动化,1999,5:32-35
9.杨全,张真.金属凝固与铸造过程数值模拟[M],杭州:浙江大学出版社,1996,45-48
10. S. K. Choudhary, D Mazumdar. Mathematical modeling of heat transfer phenomena in continuous casting of steel[J], ISIJ, 1993, 33:764-774
11.李海梅,顾元宪,申长雨.平面相变热传导问题等效热容法的有限元解[J],大连理工大学学报,2000,40:45-48
12.孔祥谦.有限单元法在传热学中的应用(第三版)[M],北京:科学出版社,1998,12-13
13.王成,邵敏.有限单元法基本原理和数值方法[M],北京:清华大学出版社,1997,90-98
14. C. R. Swaminathan, V. R. Voller. A general enthalpy method for modeling solidification processes[J], Metallurgical transactions, 1992, 23:651-664
15.张铁,闫家斌.数值分析[M],北京:冶金工业出版社,2001,60-69
16. C. A. Santos, J. A. Spim, A. Garcia. Mathematical modeling and optimization strategies(genetic algorithm and knowledge base) applied to the continuous casting of steel[J], Engineering applications of artificial, 2003, 16:511-527
17.郭戈,乔俊飞,王伟.铸坯凝固过程计算机模拟[J],中国有色金属学报,1999,9-2:339-344
18. M. Salcudean, Z. Abdullah. On the numerical modeling of heat transfer during solidification processes[J], IJNME, 1988, 25: 445-473
19.孙海峰.威钢ROKOP高效方坯连铸二次冷却的研究[D],重庆:重庆大学,2002
20. Stefano Barozzl, Piergiorgio. Computer control and optimization of secondary cooling during continuous casting[J], Iron and steel Engineer, 1986, 11:21-26
21. D. Constales, J. Kacur, R. Van Keer. On the optimal cooling strategy for variable-speed continuous casting[J], Numerical methods in engineering, 2002, 53:539-565
22. N. Chakraborti, R. S. P. Gupta, T. K. Tiwari. Optimization of continuous casting process using genetic algorithms: studies of spray and radiation cooling regions[J], Ironmaking and steelmaking, 2003, 30(4): 273-278
23. Kaisa Miettinen. Optimal control of continuous casting by nondifferentiable multiobjective optimization[J], Computational optimization and application, 1998, 11:209-225
24. B. Lally, L. T. Biegler. Optimization and continuous casting: Part 1. Problem formation and solution strategy[J], Metallurgical transactions, 1991, 10:641-648
25. B. Lally, L. T. Biegler. Optimization and continuous casting: Part 2. Application to industrial casters[J]. Metallurgical transactions, 1991, 10:460-468
26. Carlos A. Santos, Jaime A. Spim Jr, Maria C. F. Ierardi. The use of artificial intelligence technique for the optimization of process parameters used in the continuous of steel[J], Applied Mathematical Modelling, 2002, 26: 102-115
27. N. Chakraborti, R. S. P. Gupta, T. K. Tiwari. Optimazation of continuous casting process using genetic algorithms: studies of spray and radiation cooling regions[J], Ironmaking and Dteelmaking 2003, 30:46-54
28.王小平,曹立明.遗传算法[M],西安:西安交通大学出版社,2002,300-350
29.王凌.智能优化算法及其应用[M],北京:清华大学出版社,2001,40-46
30. K. E. Parsopoulos. Recent approaches to global optimization problems through particle swarm optimization[J], Natrual Computing, 2002, 1: 235-306
31. James Kennedy, Russel Eberhart. Particle swarm optimization[J], IEEE Proceeding of Neural Networks, 1995: 1942-1948
32.谢晓锋,张文俊,杨之廉.微粒群算法综述[J],控制与决策,2003,Vol.18 No.2:129-134
33.李爱国.粒子群优化算法[J],计算机工程与应用,2002,21:1-3
34. Maurice Clerc, James Kennedy. The particle swarm explosion, stability, and convergence in a multidimensional complex space[J], IEEE transaction on evolutionary computation, 2002, 6 (1): 1200-1206
35. Ioan Cristian Trelea. The particle swarm optimization algorithm: convergence analysis and parameter selection[J], Information Processing Letters, 2003, 85:11-45
36. J. F. Schutte. Parallel global optimization with the particle swarm algorithm[J], Int. J. Numer. Meth. Engng, 2003: 45-65
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