铅锌烧结过程状态控制方法研究
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摘要
铅锌被广泛应用于国防、电子等众多工业领域。烧结块是铅锌密闭鼓风炉熔炼的主要原料,铅锌烧结块质量的好坏,对铅锌冶炼生产效率的高低有着举足轻重的影响。
烧结过程状态是铅锌烧结生产状况的反映,状态的稳定和优化是提高烧结块产量质量的有效手段。首先,通过分析烧结过程工艺特点,阐述了铅锌烧结过程状态控制对于烧结块产量质量优化的重要性。然后,针对烧结过程强非线性、大滞后性和不确定性等控制难点,提出了基于模糊T-S预测模型的透气性模糊专家控制和烧穿点模糊预测控制的基本思想。
针对影响透气性的因素多、可调节透气性的控制参数少,以及透气性描述和评判的模糊性等问题,建立了透气性的模糊T-S预测模型,提高了透气性的预测精度。基于透气性的预测值,利用模糊推理技术对不确定性信息的处理能力和专家控制方法适用于复杂工业过程的优点,结合烧结生产加水操作专家经验,提出了透气性的模糊专家控制策略,实现了对透气性的有效控制。
针对铅锌烧结过程烧穿点存在的不确定性,现有预测模型对不确定性考虑不足以及预测精度不够理想等问题,提出了烧穿点的模糊预测控制方法。通过分析烧穿点的直接影响因素,可知在不同垂直燃烧速度的作用下,烧穿点和台车速度之间表现出分段线性化关系。采用模糊T-S模型辨识方法建立烧穿点的模糊T-S模型,提高了烧穿点的建模效果。基于烧穿点的动态模糊模型,采用隐式广义预测控制方法求取台车速度的预测控制律,实现烧穿点的模糊预测控制。
基于现场数据,对所提出的透气性和烧穿点控制方法进行仿真分析,结果表明,透气性的模糊专家控制策略能有效稳定料层透气性,并对透气性的优化设定值有很好的跟踪效果;烧穿点的模糊T-S模型能有效抑制过程不确定信息地影响,建模效果优于基于烧结机理的神经网络模型;模糊预测控制方法与烧穿点的模糊控制方法相比,具有响应快、超调小、调节时间短的优点,能快速有效地跟踪烧穿点优化设定值的变化。
Lead and Zinc is widely used in many industry fields, such as military industry, electronic industry and etc. As the main material of Lead-Zinc Imperial Smelting Process, the agglomerate which is produced by sintering process is very important to the quality and quantity of Lead and Zinc production.
The states are the reflection of the sintering process. To stabilize and optimize the sintering process state can improve the quantity and quality of the agglomerate. Based on the analysis of sintering process mechanism, we learn that the control of sintering process state is vital to the quantity and quality of agglomerate. To deal with the control problems of strong nonlinearity, long time delay and uncertainty, a fuzzy expert control method based on fuzzy T-S predictive model for permeability and a fuzzy predictive control method for burning through point are proposed.
The permeability is influenced by many factors. However, the operation parameter can be used to control these states is limited. The description and judgment of permeability is fuzzy, which makes the control of permeability much difficulty. To deal with these control problems, a fuzzy T-S predictive model of permeability is established. Then, based on the prediction of permeability, a fuzzy expert control method is proposed.
Based on some features in Lead-Zinc sintering process, such as time-delay, nonlinearity and uncertainty, a fuzzy predictive control method for burning through point is proposed. First, to deal with the uncertainty in Lead-Zinc sintering process, a fuzzy T-S model for burning through point is established based on the piecewise linearization characteristics between burning through point and sinter strand velocity. Then, the model predictive controller is designed based on the global model obtained by dynamic linearization method.
The simulation results illustrate the effectiveness of the control methods proposed for sintering process state. The fuzzy expert control method for permeability can stabilize the permeability and track the permeability set point effectively. The fuzzy T-S model is much more precise than the neural network model for burning through point based on process parameters. The rise time and regulating time of fuzzy predictive control method are both much shorter than those by using fuzzy control method for burning through point in Lead-Zinc sintering process.
烧结过程状态是铅锌烧结生产状况的反映,状态的稳定和优化是提高烧结块产量质量的有效手段。首先,通过分析烧结过程工艺特点,阐述了铅锌烧结过程状态控制对于烧结块产量质量优化的重要性。然后,针对烧结过程强非线性、大滞后性和不确定性等控制难点,提出了基于模糊T-S预测模型的透气性模糊专家控制和烧穿点模糊预测控制的基本思想。
针对影响透气性的因素多、可调节透气性的控制参数少,以及透气性描述和评判的模糊性等问题,建立了透气性的模糊T-S预测模型,提高了透气性的预测精度。基于透气性的预测值,利用模糊推理技术对不确定性信息的处理能力和专家控制方法适用于复杂工业过程的优点,结合烧结生产加水操作专家经验,提出了透气性的模糊专家控制策略,实现了对透气性的有效控制。
针对铅锌烧结过程烧穿点存在的不确定性,现有预测模型对不确定性考虑不足以及预测精度不够理想等问题,提出了烧穿点的模糊预测控制方法。通过分析烧穿点的直接影响因素,可知在不同垂直燃烧速度的作用下,烧穿点和台车速度之间表现出分段线性化关系。采用模糊T-S模型辨识方法建立烧穿点的模糊T-S模型,提高了烧穿点的建模效果。基于烧穿点的动态模糊模型,采用隐式广义预测控制方法求取台车速度的预测控制律,实现烧穿点的模糊预测控制。
基于现场数据,对所提出的透气性和烧穿点控制方法进行仿真分析,结果表明,透气性的模糊专家控制策略能有效稳定料层透气性,并对透气性的优化设定值有很好的跟踪效果;烧穿点的模糊T-S模型能有效抑制过程不确定信息地影响,建模效果优于基于烧结机理的神经网络模型;模糊预测控制方法与烧穿点的模糊控制方法相比,具有响应快、超调小、调节时间短的优点,能快速有效地跟踪烧穿点优化设定值的变化。
Lead and Zinc is widely used in many industry fields, such as military industry, electronic industry and etc. As the main material of Lead-Zinc Imperial Smelting Process, the agglomerate which is produced by sintering process is very important to the quality and quantity of Lead and Zinc production.
The states are the reflection of the sintering process. To stabilize and optimize the sintering process state can improve the quantity and quality of the agglomerate. Based on the analysis of sintering process mechanism, we learn that the control of sintering process state is vital to the quantity and quality of agglomerate. To deal with the control problems of strong nonlinearity, long time delay and uncertainty, a fuzzy expert control method based on fuzzy T-S predictive model for permeability and a fuzzy predictive control method for burning through point are proposed.
The permeability is influenced by many factors. However, the operation parameter can be used to control these states is limited. The description and judgment of permeability is fuzzy, which makes the control of permeability much difficulty. To deal with these control problems, a fuzzy T-S predictive model of permeability is established. Then, based on the prediction of permeability, a fuzzy expert control method is proposed.
Based on some features in Lead-Zinc sintering process, such as time-delay, nonlinearity and uncertainty, a fuzzy predictive control method for burning through point is proposed. First, to deal with the uncertainty in Lead-Zinc sintering process, a fuzzy T-S model for burning through point is established based on the piecewise linearization characteristics between burning through point and sinter strand velocity. Then, the model predictive controller is designed based on the global model obtained by dynamic linearization method.
The simulation results illustrate the effectiveness of the control methods proposed for sintering process state. The fuzzy expert control method for permeability can stabilize the permeability and track the permeability set point effectively. The fuzzy T-S model is much more precise than the neural network model for burning through point based on process parameters. The rise time and regulating time of fuzzy predictive control method are both much shorter than those by using fuzzy control method for burning through point in Lead-Zinc sintering process.
引文
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[3]吴荣庆.我国铅锌矿产资源特点与综合利用[J].中国金属通报,2008,(9):32-33
[4]王志刚.密闭鼓风炉炼铅锌技术改进及展望[J].湖南有色金属,2003,19(6):19-22
[5]Cumming M J,Rankin W J,Siemaon J R.Modeling and Simulation of Iron Ore Sintering[C].In 4th International Symposium on Agglomeration,Toranto,Canada,1985,20(4):763-766
[6]Upadhyaya G S.Some issues in sintering science and technology[J].Materials chemistry and physics,2001,6(7):1-5
[7]Takumi F.Sinter Operation Control System with Artificial Intelligence[J].Kawasaki Steel Technical Report,1991,23(3)
[8]Watanabe M,Sasaki Y.Development of Operation Guide System and its Application to Chiba No.4 Sintering Plant[C].In 4th International Symposium on Agglomeration,Toranto,Canada,1985,21(3):147-152
[9]Wyborn BJ,Bowden M W.Optimising moisture control for zinc-lead sintering [C].Australasian Institute of Mining and Metallurgy,1985,41(1):93-97
[10]程武山.理钢三烧混合料加水控制系统[J].烧结球团,1999,24(5):40-43
[11]张学金,陈星,吕民.烧结混合料水分测量与控制系统[J].工业计量,2005,15(4):18-21
[12]孙乐,金伟.神经网络在烧结混合料水分控制中的应用[J].大连轻工业学院学报,2003,22(3):211-213
[13]Niloy N K,Kishalay M.Optimization of Coke Rate and Sinter Quality by Genetic Algorithm for Two-layer Sintering of Iron Ore[C].Materials Science and Technology 2003 Meeting Modeling Control and Optimization in Ferrous and Non-Ferrous Industry,2003,6(3):109-124
[14]郑建新,李代平,万金德.武钢一烧过程优化控制系统的开发[J].烧结球团,2005,30(5):22-26
[15]Siemon J R,Kowalczyk E,Tuppurainen J.Estimation of burn-through point on a lead sinter plant[C].National Conference Publication,1986,84(4):191-195
[16]Siemon J R,Kowalczyk E,Fitzgibbons D P,et al.Peak bed temperature prediction on a lead/zinc sinter plant[J].Minerals Engineering,1991,4(1):63-78
[17]Li M H,Wang J.The research for soft measuring technique of sintering burning through point[C].2006 1st IEEE Conference on Industrial Electronics and Applications,2006,1(1):p 4025-4027
[18]刘玉长,桂卫华,周孑民.基于软测量技术的模糊烧结终点控制研究[J].烧结球团,2002,27(2):27-30
[19]Wu M,Xu C H,Du Y X.Intelligent optimal control for lead-zinc sintering process state[J].Transactions of Nonferrous Metals Society of China,2006,16(4):975-981
[20]王雅琳.智能集成建模理论及其在有色冶炼过程优化控制中的应用研究[D].长沙:中南大学,2001:86-92
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