步进式加热炉燃烧过程智能控制策略及其应用
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摘要
为有效解决目前加热炉燃烧过程普遍存在的能耗高、钢坯温度波动严重、温度控制精度差等问题，论文针对步进式加热炉的工艺特点，提出了加热炉燃烧过程的智能控制策略，包括基于阀门开度的模糊专家控制策略、基于模糊专家规则的空燃比自动寻优策略，建立了智能优化控制系统。
论文提出了基于阀门开度的模糊专家控制策略，根据温度偏差和偏差变化率直接获取煤气流量，由空燃比获取所需空气流量，根据流量与阀门开度之间的关系模型直接获取煤气和空气阀门开度，从而解决了PID控制和传统模糊控制存在的响应慢、超调量大的问题，保证了加热控制满足生产实际要求。
同时，论文还针对煤气热值波动问题，提出了空燃比自寻优策略，针对炉温优化问题，采用改进的BP神经网络，建立了钢坯温度预报模型，基于炉温分布模型采用罚函数方法进行炉温优化设定。
论文采用基于OPC的系统嵌入技术，实现了智能控制算法与CS1000集散控制系统的信息交换，建立了步进式加热炉燃烧过程智能控制系统，对工况参数进行了集中监视和实时控制。
工业实际运行结果表明，加热炉燃烧过程智能控制系统可以确保在工况波动下的炉温控制精度，提高升降温速度，减少吨钢燃耗、电耗和钢坯烧损，提高加热炉的生产能力，实现加热炉操作过程的集中监控，取得了显著的经济效益和社会效益。
In order to solve the problems efficiently in the combustion process of reheating furnace such as the high consumption of energy, the big fluctuation of billet temperature, the poor delicacy of the temperature control, etc., the paper proposes an intelligent control strategy of the combustion process of reheating furnace, including the fuzzy expert control strategy based on the valve value, air gas ratio automatic optimal strategy based on the fuzzy expert rules, according to the technical characteristics of walking beam reheating furnace. The intelligent optimal control system is also established in this paper.
The fuzzy expert control strategy based on the valve value in the paper obtains the flux from the temperature warp, the warp ratio and the air-gas ratio. The valve value achieved from the model of the relation between flux and valve value. It settles the problems of slow-reaction and big overshoot in PID control and traditional fuzzy control, so as to guarantee the control to satisfy the demand of the practical produce.
Meanwhile, the paper has proposed the air gas ratio optimal strategy for the thermal value fluctuation of the gas, has established the prediction model of billet temperature for the furnace temperature optimization with the ameliorated BP neural network model, and has adopted the penalty function method to realize the temperature optimization enactment for the model of furnace temperature distribution.
The paper adopts the OPC system embedding technique to realize the information exchange between the intelligent control algorithm and CS1000 distributed control system, and establishes the burning process intelligent control system of walking beam reheating furnace so as to have collective scrutiny and real-time control on the station parameters.
The practical operation result in the industry offers the evidence that the burning process intelligent control system of reheating furnace can assure the delicacy of the furnace temperature control. The control system increases the speed of temperature adjustment and reduces the energy consume, electricity consumption and the billet combustion loss. As a result, it elevates the productive capacity of reheating furnace, fulfills the collective scrutiny in the operation process of reheating furnace, and has the prominent economic and social benefits.
论文提出了基于阀门开度的模糊专家控制策略，根据温度偏差和偏差变化率直接获取煤气流量，由空燃比获取所需空气流量，根据流量与阀门开度之间的关系模型直接获取煤气和空气阀门开度，从而解决了PID控制和传统模糊控制存在的响应慢、超调量大的问题，保证了加热控制满足生产实际要求。
同时，论文还针对煤气热值波动问题，提出了空燃比自寻优策略，针对炉温优化问题，采用改进的BP神经网络，建立了钢坯温度预报模型，基于炉温分布模型采用罚函数方法进行炉温优化设定。
论文采用基于OPC的系统嵌入技术，实现了智能控制算法与CS1000集散控制系统的信息交换，建立了步进式加热炉燃烧过程智能控制系统，对工况参数进行了集中监视和实时控制。
工业实际运行结果表明，加热炉燃烧过程智能控制系统可以确保在工况波动下的炉温控制精度，提高升降温速度，减少吨钢燃耗、电耗和钢坯烧损，提高加热炉的生产能力，实现加热炉操作过程的集中监控，取得了显著的经济效益和社会效益。
In order to solve the problems efficiently in the combustion process of reheating furnace such as the high consumption of energy, the big fluctuation of billet temperature, the poor delicacy of the temperature control, etc., the paper proposes an intelligent control strategy of the combustion process of reheating furnace, including the fuzzy expert control strategy based on the valve value, air gas ratio automatic optimal strategy based on the fuzzy expert rules, according to the technical characteristics of walking beam reheating furnace. The intelligent optimal control system is also established in this paper.
The fuzzy expert control strategy based on the valve value in the paper obtains the flux from the temperature warp, the warp ratio and the air-gas ratio. The valve value achieved from the model of the relation between flux and valve value. It settles the problems of slow-reaction and big overshoot in PID control and traditional fuzzy control, so as to guarantee the control to satisfy the demand of the practical produce.
Meanwhile, the paper has proposed the air gas ratio optimal strategy for the thermal value fluctuation of the gas, has established the prediction model of billet temperature for the furnace temperature optimization with the ameliorated BP neural network model, and has adopted the penalty function method to realize the temperature optimization enactment for the model of furnace temperature distribution.
The paper adopts the OPC system embedding technique to realize the information exchange between the intelligent control algorithm and CS1000 distributed control system, and establishes the burning process intelligent control system of walking beam reheating furnace so as to have collective scrutiny and real-time control on the station parameters.
The practical operation result in the industry offers the evidence that the burning process intelligent control system of reheating furnace can assure the delicacy of the furnace temperature control. The control system increases the speed of temperature adjustment and reduces the energy consume, electricity consumption and the billet combustion loss. As a result, it elevates the productive capacity of reheating furnace, fulfills the collective scrutiny in the operation process of reheating furnace, and has the prominent economic and social benefits.
引文
[1] 吕勇哉．工业过程模型化及计算机控制．化学工业出版社，1986
[2] 王继烈，范功友．轧钢加热炉计算机控制的现状与发展趋势，山东冶金，1995，17(2)：11-13
[3] 张东波，朱建林，黄辉先．用于加热炉的专家模糊温度控制器．机电工程，2002，19(1)：51-56
[4] 杨波，葛芦生．蓄热式加热炉的神经网络燃烧控制．安徽工业大学学报，2002，19(1)：50-57
[5] Pike P E, Citron S J. Optimization study of a slab reheating furnace [J]. Automation, 1970, 6 (1): 41-50
[6] 王锡淮，李柠，李少远．步进式加热炉建模和炉温优化设定策略．上海交通大学学报，2001，35(9)：1306-1309
[7] 柴天佑，王中杰，张莉．加热炉的炉温优化设定模型．自动化学报，2000，26(4)：537-541
[8] 蒋炯，杨永耀，吕勇哉．步进加热炉计算机控制．冶金自动化，1989，13(1)：3-8
[9] 杨泽宽，康国仁．加热炉优化控制数学模型．钢铁，1990，25(12)：59-63
[10] 王中杰，柴天佑，邵诚．加热炉多模式动态优化控制策略．控制与决策，1999，14(5)：465-468
[11] 张卫军，陈海耿等．对我国轧钢加热炉计算机控制应用现状的分析及发展中几个问题的探讨．冶金能源，1997，16(6)：52-55
[12] 王晓冬．工业加热炉的模糊控制仿真研究．天津理工学院学报，2001，17(1)：55-57
[13] 赵守庭，刘洹君．计算机在加热炉燃烧控制系统中的应用．工业控制计算机，1997，3：5-6
[14] 张志杰．加热炉控制系统的优化设计与应用．工业炉，2000，22(3)：26-30
[15] 颜昌学，夏厚德．基于模糊枝木的加热炉．中南民族学院学报(自然科学版)，1999，18(4)：22-25
[16] 肖兵，叶乐年．基于趋势分析的模糊智能燃烧控制．华南理工大学学报，1998，26(5)：36-39
[17] 李庆尧．带钢冷连轧机过程控制计算机及应用软件设计．冶金工业出版社，
1995
[18] 日本山武公司．涟钢四扎厂虚热式加热炉设计手册．日本山武公司，1996：5-42
[19] 田小果、陈智华．模糊控制在加热炉上的应用．自动化与仪器仪表，2001，5：45-47
[20] 李文涛，李胜玉等．步进式连续加热炉炉温最优控制方案的实施．包头钢铁学院学报，1994，13(3)：30-33
[21] 俞海斌，裘峰．锅炉燃烧控制的先进控制软件及工业应用．机电工程，1999，5：16-19
[22] 吴雨川．梅山热轧厂加热炉燃烧控制系统．基础自动化，1997，5：6-10
[23] J. Valente, de Oliveira, J.M.Lemos. Techniques and Applications of Fuzzy System Interface Optimizers in various system Problems. Fuzzy Theory Systems, 1999,52(4): 1522-1559
[24] 蔡自兴．智能控制—基础与应用．北京：国防工业出版社，1998
[25] 李士勇．模糊控制·神经控制和智能控制论．哈尔滨：哈尔滨工业大学出版社，1996
[26] 孙增忻．智能控制理论与技术．北京：清华大学出版社，1997
[27] 李友善，李军．模糊控制理论及其在过程控制中的应用．北京：国防工业出版社，1993
[28] Qian Y, Tessier P. Fuzzy Logic Based Optimization and Application. Fuzzy Optimization:Recent Advances, 1994:379-394
[29] Kazuo Tanaka, Tsuyoshi Hori. An Integrated Algorithm of Fuzzy Modeling and Controller Design for Nonlinear Systems. IEEE International Conference on Fuzzy Systems, 1999, 2:887-892
[30] Maiers J, Sherif Y S. Applications of fuzzy set theory. IEEE Trans Syst .Man Cybem, 1985, 15(1): 175-189
[31] Won Chul Kim. Stability Analysis and Stabilization of Fuzzy State Space Models. Fuzzy Sets and Systems,1995, 71:131-142
[32] 杨杰，郭英凯等．基于计算智能的模糊规则自动生成．上海交通大学学报，1999，33(11)：1409-1413
[33] 李平，孙优贤等．最优模糊控制器的系统设计．控制理论与应用，1995，12(1)：46-52
[34] Lim M H, Rahardja S. A Paradigm for Leaming Fuzzy Rules. Fuzzy Sets and Systems, 1996, 82:177-186
[35] 曹秀敏．模糊控制及其在工业过程中的应用．科技情报开发与经济，2002，12(2)：89-91
[36] Hua Owang, Kazuo Tanaka. An Analytical Framework of Fuzzy Modeling and Control of Nonlinear System Stability and Design Issues. America Control Conference, 1995, 2: 2272-2276
[37] Ying H, Practical Design of nonlinear Fuzzy Controllers with Stability Analysis for Regulating Processes with Unknown Mathematical Models. Automatica, 1994, 30(7): 1185-1195
[38] J.Valente, de Oliveira, J. M. Lemos. Improving adaptive fuzzy control performance by speeding up identification. Intelligent and Fuzzy Systems, 1998, 6: 297-314
[39] 胡守仁，余少波，戴葵．神经网络导论．长沙：国防科技大学出版社，1993
[40] 丛爽编著．面向MATLAB工具箱的神经网络理论与应用．合肥：中国科学技术出版社，1999．6-9，64-72
[41] 戚德虎，康继昌．BP神经网络的设计．计算机工程与设计，1998，19(2)：48-50
[42] 赵凤治，尉继英．约束最优化计算方法．北京：科学出版社，1991．3-4
[43] 袁亚湘，孙文瑜．最优化理论与方法．北京：科学出版社，1997．46-50
[44] S.G.CAO, N.W.REES, G.FENG. Analysis and design for a class of complex control systems. IFAC, Automatica, 1997, 13(6): 1017-1039
[45] 王永初，任秀珍．工业过程控制系统设计范例．北京：科学出版社，1986
[46] Kostial I, Nemcovsky P, Dorcak L, Terpak J, Petras. Blast furnace control model. Proceedings of the ICAMC, 1998:501-504
[47] Holland J H. Adaptation in Nature and Artificial Systems. The University of Michigan Press, 1975, MIT Press, 1992
[48] Thoma Mc Avoy. Intelligent control applications in the process industries. Annual Reviews in Control, 2002, 26:75-86
[49] D A Ashok, Sirshendu C. Reheat furnace temperature control and performance at essar steel. Iron and Steel Engineer, 1998, 75(11): 31-36
[50] Wang Yaolan. An adaptive control using fuzzy neural network and application. Control Theory and application, 1995, 12(4): 437-444
[51] 颜纶亮等．微机在过程控制中的应用．北京：清华大学出版社，1989
[52] 裴景玉．基于Windows95平台的实时控制技术．工业控制计算机，2000，13(3)：36-40
[2] 王继烈，范功友．轧钢加热炉计算机控制的现状与发展趋势，山东冶金，1995，17(2)：11-13
[3] 张东波，朱建林，黄辉先．用于加热炉的专家模糊温度控制器．机电工程，2002，19(1)：51-56
[4] 杨波，葛芦生．蓄热式加热炉的神经网络燃烧控制．安徽工业大学学报，2002，19(1)：50-57
[5] Pike P E, Citron S J. Optimization study of a slab reheating furnace [J]. Automation, 1970, 6 (1): 41-50
[6] 王锡淮，李柠，李少远．步进式加热炉建模和炉温优化设定策略．上海交通大学学报，2001，35(9)：1306-1309
[7] 柴天佑，王中杰，张莉．加热炉的炉温优化设定模型．自动化学报，2000，26(4)：537-541
[8] 蒋炯，杨永耀，吕勇哉．步进加热炉计算机控制．冶金自动化，1989，13(1)：3-8
[9] 杨泽宽，康国仁．加热炉优化控制数学模型．钢铁，1990，25(12)：59-63
[10] 王中杰，柴天佑，邵诚．加热炉多模式动态优化控制策略．控制与决策，1999，14(5)：465-468
[11] 张卫军，陈海耿等．对我国轧钢加热炉计算机控制应用现状的分析及发展中几个问题的探讨．冶金能源，1997，16(6)：52-55
[12] 王晓冬．工业加热炉的模糊控制仿真研究．天津理工学院学报，2001，17(1)：55-57
[13] 赵守庭，刘洹君．计算机在加热炉燃烧控制系统中的应用．工业控制计算机，1997，3：5-6
[14] 张志杰．加热炉控制系统的优化设计与应用．工业炉，2000，22(3)：26-30
[15] 颜昌学，夏厚德．基于模糊枝木的加热炉．中南民族学院学报(自然科学版)，1999，18(4)：22-25
[16] 肖兵，叶乐年．基于趋势分析的模糊智能燃烧控制．华南理工大学学报，1998，26(5)：36-39
[17] 李庆尧．带钢冷连轧机过程控制计算机及应用软件设计．冶金工业出版社，
1995
[18] 日本山武公司．涟钢四扎厂虚热式加热炉设计手册．日本山武公司，1996：5-42
[19] 田小果、陈智华．模糊控制在加热炉上的应用．自动化与仪器仪表，2001，5：45-47
[20] 李文涛，李胜玉等．步进式连续加热炉炉温最优控制方案的实施．包头钢铁学院学报，1994，13(3)：30-33
[21] 俞海斌，裘峰．锅炉燃烧控制的先进控制软件及工业应用．机电工程，1999，5：16-19
[22] 吴雨川．梅山热轧厂加热炉燃烧控制系统．基础自动化，1997，5：6-10
[23] J. Valente, de Oliveira, J.M.Lemos. Techniques and Applications of Fuzzy System Interface Optimizers in various system Problems. Fuzzy Theory Systems, 1999,52(4): 1522-1559
[24] 蔡自兴．智能控制—基础与应用．北京：国防工业出版社，1998
[25] 李士勇．模糊控制·神经控制和智能控制论．哈尔滨：哈尔滨工业大学出版社，1996
[26] 孙增忻．智能控制理论与技术．北京：清华大学出版社，1997
[27] 李友善，李军．模糊控制理论及其在过程控制中的应用．北京：国防工业出版社，1993
[28] Qian Y, Tessier P. Fuzzy Logic Based Optimization and Application. Fuzzy Optimization:Recent Advances, 1994:379-394
[29] Kazuo Tanaka, Tsuyoshi Hori. An Integrated Algorithm of Fuzzy Modeling and Controller Design for Nonlinear Systems. IEEE International Conference on Fuzzy Systems, 1999, 2:887-892
[30] Maiers J, Sherif Y S. Applications of fuzzy set theory. IEEE Trans Syst .Man Cybem, 1985, 15(1): 175-189
[31] Won Chul Kim. Stability Analysis and Stabilization of Fuzzy State Space Models. Fuzzy Sets and Systems,1995, 71:131-142
[32] 杨杰，郭英凯等．基于计算智能的模糊规则自动生成．上海交通大学学报，1999，33(11)：1409-1413
[33] 李平，孙优贤等．最优模糊控制器的系统设计．控制理论与应用，1995，12(1)：46-52
[34] Lim M H, Rahardja S. A Paradigm for Leaming Fuzzy Rules. Fuzzy Sets and Systems, 1996, 82:177-186
[35] 曹秀敏．模糊控制及其在工业过程中的应用．科技情报开发与经济，2002，12(2)：89-91
[36] Hua Owang, Kazuo Tanaka. An Analytical Framework of Fuzzy Modeling and Control of Nonlinear System Stability and Design Issues. America Control Conference, 1995, 2: 2272-2276
[37] Ying H, Practical Design of nonlinear Fuzzy Controllers with Stability Analysis for Regulating Processes with Unknown Mathematical Models. Automatica, 1994, 30(7): 1185-1195
[38] J.Valente, de Oliveira, J. M. Lemos. Improving adaptive fuzzy control performance by speeding up identification. Intelligent and Fuzzy Systems, 1998, 6: 297-314
[39] 胡守仁，余少波，戴葵．神经网络导论．长沙：国防科技大学出版社，1993
[40] 丛爽编著．面向MATLAB工具箱的神经网络理论与应用．合肥：中国科学技术出版社，1999．6-9，64-72
[41] 戚德虎，康继昌．BP神经网络的设计．计算机工程与设计，1998，19(2)：48-50
[42] 赵凤治，尉继英．约束最优化计算方法．北京：科学出版社，1991．3-4
[43] 袁亚湘，孙文瑜．最优化理论与方法．北京：科学出版社，1997．46-50
[44] S.G.CAO, N.W.REES, G.FENG. Analysis and design for a class of complex control systems. IFAC, Automatica, 1997, 13(6): 1017-1039
[45] 王永初，任秀珍．工业过程控制系统设计范例．北京：科学出版社，1986
[46] Kostial I, Nemcovsky P, Dorcak L, Terpak J, Petras. Blast furnace control model. Proceedings of the ICAMC, 1998:501-504
[47] Holland J H. Adaptation in Nature and Artificial Systems. The University of Michigan Press, 1975, MIT Press, 1992
[48] Thoma Mc Avoy. Intelligent control applications in the process industries. Annual Reviews in Control, 2002, 26:75-86
[49] D A Ashok, Sirshendu C. Reheat furnace temperature control and performance at essar steel. Iron and Steel Engineer, 1998, 75(11): 31-36
[50] Wang Yaolan. An adaptive control using fuzzy neural network and application. Control Theory and application, 1995, 12(4): 437-444
[51] 颜纶亮等．微机在过程控制中的应用．北京：清华大学出版社，1989
[52] 裴景玉．基于Windows95平台的实时控制技术．工业控制计算机，2000，13(3)：36-40