基于铝电解槽热平衡分析的氟化铝添加量控制策略研究
|
摘要
铝冶金是有色金属工业的重要组成部分。铝及铝加工材作为有色金属基础材料,被广泛应用于建筑、电力、交通、机械、国防等众多领域。近年来,我国铝冶金工业得到了迅速的发展,目前已成为世界主要的铝生产国。然而,铝冶金工业一直以来都是能耗大户。在铝冶金工业的所有流程中,电解工序占整个铝生产用能的80%～89%。因此,采取各种措施降低铝电解的能耗是铝冶金工业节能的关键,也是制约我国铝冶金工业健康发展的主要因素。
铝电解生产节能的有效途径是在维持电解槽热平衡稳定的条件下降低电解温度。降低电解温度的最佳方法之一是选择合适的添加剂,形成初晶温度较低的电解质体系。在所有的添加剂中,氟化铝所占比例最大,因此对电解质成分和电解温度的影响十分显著。本论文通过对铝电解槽热平衡特性与氟化铝添加量之间耦合关系的分析,寻求并应用适当的氟化铝添加量控制策略,在维持铝电解槽热平衡稳定的条件下,通过降低电解温度实现铝电解生产过程节能的目的。本论文完成的研究主要工作和创新点如下:
(1)在大量查阅中外文献的基础上,综述了铝电解槽热平衡分析的各种方法,评述了用于铝电解槽内传热分析的物理模型和数值模型。从铝电解槽内物料和能量平衡的角度出发,首次研究了过剩氟化铝含量与电解温度之间的特征联系,运用传热学规律建立了铝电解槽内耦合物料和能量平衡的理论模型,并通过适当的简化显式地给出了实用表达式,最后通过现场试验验证了该模型的正确性。
(2)采用多项式回归分析方法,以铝电解槽实测参数为依据,以氟化铝添加量为因变量,以电解温度为自变量,提出了基于回归分析的氟化铝添加量控制模型,利用该模型可根据前一天电解温度预测当天的氟化铝添加量。现场试验表明,该氟化铝添加量控制策略对于稳定电解温度有利,有助于提高电流效率和节约电能。
(3)首次运用遗传算法对氟化铝添加量进行控制,以日均槽电压和氟化铝添加量作为遗传操作变量,以过热度最小作为氟化铝添加量控制目标,以过热度的倒数作为适应度函数,得到不同槽电压下对应的氟化铝添加量最佳值。建模过程中,引入了最优保存策略,使适应度最好的个体尽可能地保留到下一代种群中。试验结果表明,该氟化铝添加量控制方法较之目前工业常规使用的方法具有明显的优势,可以使过热度大大降低。
(4)首次将支持向量机引入到氟化铝添加量控制问题的研究,建立了以电解温度、初晶温度、槽电压作为输入,以氟化铝添加量作为输出,分别以多项式函数和径向基函数为核函数的支持向量机模型。试验结果表明,经过训练后的支持向量机能够较好地预测氟化铝添加量,核函数的不同对支持向量机性能的影响不大。
(5)以某大型铝业公司电解铝厂160kA系列铝电解槽为对象,采用Delphi语言开发了氟化铝添加量控制决策系统,将基于回归分析、遗传算法、支持向量机的氟化铝添加量控制策略嵌入到现场上位机中,以实现实时地根据槽况动态调节氟化铝添加量。现场试验结果表明,所提出的氟化铝添加量控制策略及开发的相应软件系统有效可行,实用性强,节能效果显著。
The reduction of aluminium is an important part of the nonferrous metallurgy industry. As the basis material of nonferrous metals, the aluminium and its processed materials have been widely used in building, electricity, transportation, machinery, national defence and other fields. In recent years, the Al-metallurgical industry of China has obtained rapid development and China is being the world's leading aluminium producer. However, the Al-metallurgical industry is always a kind of industry which consumes a large amount of energy. In all processes of Al-metallurgical industry, the electrolysis occupies 80% - 89% of the total energy consumption. As a result, taking various measures to reduce the energy consumption in aluminium electrolysis is the key to energy saving in the Al-metallurgical industry and is also the main factor which constrains the healthy development of China's Al-metallurgical industry.
An effective way to reduce the energy consumption in the aluminium electrolysis process is to lower the electrolysis temperature under the premise of maintaining the heat balance in the aluminium reduction cell. One of the best methods for reducing the electrolysis temperature is to choose suitable additives to form an electrolyte system whose liquidus temperature is low. In all additives adopted now, the aluminium fluoride occupies the largest proportion. Therefore, the aluminium fluoride can influence the electrolyte composition and electrolysis temperature significantly. In this paper, through the analysis on the coupling relationship between the heat balance characteristics of the aluminum reduction cell and the amount of aluminium fluoride addition, the author tries to seek and apply the control strategy which is concerned the appropriate amount of aluminium fluoride addition, and also tries to lower the electrolysis temperature under the condition of maintaining the heat balance in the aluminium reduction cell to achieve the goal of reducing the energy consumption in the aluminium electrolysis process. The main contributions of this dissertation are summarized as follows:
(1) On the basis of extensive searching and reading domestic and foreign literatures, various methods about how to analyse the heat balance in the aluminium reduction cell was summarized, and the physical models and numerical models which were used in the aluminium reduction cell's heat transfer analysis were reviewed. Based on the balance of the mass and energy in the aluminium reduction cell, the characteristics relationship between the amount of the excess aluminum fluoride and the electrolysis temperature was explored. The coupled mass and energy balance model in the aluminium reduction cell was established through the law of heat transfer, and a practical expression was given explicitly by appropriate simplifications. Finally, the validity of the model was verified by the on-site tests.
(2) A control model for aluminum fluoride addition was put forward based on the regression analysis, which adopts the aluminum fluoride addition as the dependent variable and adopts the electrolysis temperature as the independent variable. Through this model, the amount of the present-day aluminum fluoride addition can be forecasted according to the yesterday's electrolysis temperature. The field tests show that this control strategy for the aluminum fluoride addition is beneficial to stabilize the electrolysis temperature, and it is helpful to enhance the current efficiency and to reduce the electrical energy consumption.
(3) The genetic algorithm was applied to control the amount of the aluminum fluoride addition with the daily average cell voltage and the amount of aluminum fluoride added as genetic operation variables. The control target is to minimize the superheat degree of the aluminum electrolyte with the reciprocal of the superheat degree as the sufficiency function. The best value of aluminum fluoride added was gained under different cell voltages. In the modeling process, the elitist strategy was introduced to retain the best individuals to the next generation of the population as much as possible. The field test results show that this control method about the amount of the aluminium fluoride added is better than the conventional method which was used in the industry and it can reduce the degree of overheat markedly.
(4) The support vector machine was introduced into the investigation on the control problem of the aluminum fluoride addition. A support vector machine model was established using the electrolysis temperature, the liquidus temperature, the cell voltage as inputs and the aluminium fluoride addition as output. The polynomial function and the radial basis function were adopted as the kernel function respectively. The results showed that the trained support vector machine can predict the optimal amount of the amount of aluminum fluoride added, and the difference in the kernel function has little impact on the performance of the support vector machine.
(5) Based on 160kA aluminium reduction cells in a large aluminum company, a decision-making program for the aluminium fluoride addition was developed using the Delphi language. The aluminium fluoride addition control strategies based on regression analysis, genetic algorithm, support vector machine were embedded in the program to adjust the aluminium fluoride addition according to the real-time cell state. Field test results show that the three proposed aluminum fluoride addition control strategies and the corresponding software system are feasible, practical and effective in energy saving.
铝电解生产节能的有效途径是在维持电解槽热平衡稳定的条件下降低电解温度。降低电解温度的最佳方法之一是选择合适的添加剂,形成初晶温度较低的电解质体系。在所有的添加剂中,氟化铝所占比例最大,因此对电解质成分和电解温度的影响十分显著。本论文通过对铝电解槽热平衡特性与氟化铝添加量之间耦合关系的分析,寻求并应用适当的氟化铝添加量控制策略,在维持铝电解槽热平衡稳定的条件下,通过降低电解温度实现铝电解生产过程节能的目的。本论文完成的研究主要工作和创新点如下:
(1)在大量查阅中外文献的基础上,综述了铝电解槽热平衡分析的各种方法,评述了用于铝电解槽内传热分析的物理模型和数值模型。从铝电解槽内物料和能量平衡的角度出发,首次研究了过剩氟化铝含量与电解温度之间的特征联系,运用传热学规律建立了铝电解槽内耦合物料和能量平衡的理论模型,并通过适当的简化显式地给出了实用表达式,最后通过现场试验验证了该模型的正确性。
(2)采用多项式回归分析方法,以铝电解槽实测参数为依据,以氟化铝添加量为因变量,以电解温度为自变量,提出了基于回归分析的氟化铝添加量控制模型,利用该模型可根据前一天电解温度预测当天的氟化铝添加量。现场试验表明,该氟化铝添加量控制策略对于稳定电解温度有利,有助于提高电流效率和节约电能。
(3)首次运用遗传算法对氟化铝添加量进行控制,以日均槽电压和氟化铝添加量作为遗传操作变量,以过热度最小作为氟化铝添加量控制目标,以过热度的倒数作为适应度函数,得到不同槽电压下对应的氟化铝添加量最佳值。建模过程中,引入了最优保存策略,使适应度最好的个体尽可能地保留到下一代种群中。试验结果表明,该氟化铝添加量控制方法较之目前工业常规使用的方法具有明显的优势,可以使过热度大大降低。
(4)首次将支持向量机引入到氟化铝添加量控制问题的研究,建立了以电解温度、初晶温度、槽电压作为输入,以氟化铝添加量作为输出,分别以多项式函数和径向基函数为核函数的支持向量机模型。试验结果表明,经过训练后的支持向量机能够较好地预测氟化铝添加量,核函数的不同对支持向量机性能的影响不大。
(5)以某大型铝业公司电解铝厂160kA系列铝电解槽为对象,采用Delphi语言开发了氟化铝添加量控制决策系统,将基于回归分析、遗传算法、支持向量机的氟化铝添加量控制策略嵌入到现场上位机中,以实现实时地根据槽况动态调节氟化铝添加量。现场试验结果表明,所提出的氟化铝添加量控制策略及开发的相应软件系统有效可行,实用性强,节能效果显著。
The reduction of aluminium is an important part of the nonferrous metallurgy industry. As the basis material of nonferrous metals, the aluminium and its processed materials have been widely used in building, electricity, transportation, machinery, national defence and other fields. In recent years, the Al-metallurgical industry of China has obtained rapid development and China is being the world's leading aluminium producer. However, the Al-metallurgical industry is always a kind of industry which consumes a large amount of energy. In all processes of Al-metallurgical industry, the electrolysis occupies 80% - 89% of the total energy consumption. As a result, taking various measures to reduce the energy consumption in aluminium electrolysis is the key to energy saving in the Al-metallurgical industry and is also the main factor which constrains the healthy development of China's Al-metallurgical industry.
An effective way to reduce the energy consumption in the aluminium electrolysis process is to lower the electrolysis temperature under the premise of maintaining the heat balance in the aluminium reduction cell. One of the best methods for reducing the electrolysis temperature is to choose suitable additives to form an electrolyte system whose liquidus temperature is low. In all additives adopted now, the aluminium fluoride occupies the largest proportion. Therefore, the aluminium fluoride can influence the electrolyte composition and electrolysis temperature significantly. In this paper, through the analysis on the coupling relationship between the heat balance characteristics of the aluminum reduction cell and the amount of aluminium fluoride addition, the author tries to seek and apply the control strategy which is concerned the appropriate amount of aluminium fluoride addition, and also tries to lower the electrolysis temperature under the condition of maintaining the heat balance in the aluminium reduction cell to achieve the goal of reducing the energy consumption in the aluminium electrolysis process. The main contributions of this dissertation are summarized as follows:
(1) On the basis of extensive searching and reading domestic and foreign literatures, various methods about how to analyse the heat balance in the aluminium reduction cell was summarized, and the physical models and numerical models which were used in the aluminium reduction cell's heat transfer analysis were reviewed. Based on the balance of the mass and energy in the aluminium reduction cell, the characteristics relationship between the amount of the excess aluminum fluoride and the electrolysis temperature was explored. The coupled mass and energy balance model in the aluminium reduction cell was established through the law of heat transfer, and a practical expression was given explicitly by appropriate simplifications. Finally, the validity of the model was verified by the on-site tests.
(2) A control model for aluminum fluoride addition was put forward based on the regression analysis, which adopts the aluminum fluoride addition as the dependent variable and adopts the electrolysis temperature as the independent variable. Through this model, the amount of the present-day aluminum fluoride addition can be forecasted according to the yesterday's electrolysis temperature. The field tests show that this control strategy for the aluminum fluoride addition is beneficial to stabilize the electrolysis temperature, and it is helpful to enhance the current efficiency and to reduce the electrical energy consumption.
(3) The genetic algorithm was applied to control the amount of the aluminum fluoride addition with the daily average cell voltage and the amount of aluminum fluoride added as genetic operation variables. The control target is to minimize the superheat degree of the aluminum electrolyte with the reciprocal of the superheat degree as the sufficiency function. The best value of aluminum fluoride added was gained under different cell voltages. In the modeling process, the elitist strategy was introduced to retain the best individuals to the next generation of the population as much as possible. The field test results show that this control method about the amount of the aluminium fluoride added is better than the conventional method which was used in the industry and it can reduce the degree of overheat markedly.
(4) The support vector machine was introduced into the investigation on the control problem of the aluminum fluoride addition. A support vector machine model was established using the electrolysis temperature, the liquidus temperature, the cell voltage as inputs and the aluminium fluoride addition as output. The polynomial function and the radial basis function were adopted as the kernel function respectively. The results showed that the trained support vector machine can predict the optimal amount of the amount of aluminum fluoride added, and the difference in the kernel function has little impact on the performance of the support vector machine.
(5) Based on 160kA aluminium reduction cells in a large aluminum company, a decision-making program for the aluminium fluoride addition was developed using the Delphi language. The aluminium fluoride addition control strategies based on regression analysis, genetic algorithm, support vector machine were embedded in the program to adjust the aluminium fluoride addition according to the real-time cell state. Field test results show that the three proposed aluminum fluoride addition control strategies and the corresponding software system are feasible, practical and effective in energy saving.
引文
[1]吕定雄,干益人.我国开发大容量预焙铝电解槽技术进展.铝镁通讯,2000,(4):6-13
[2]中华人民共和国国家统计局.中国统计年鉴-2007.北京:中国统计出版社,2007
[3]冯乃祥.我国铝电解工业现状和与国外先进技术水平的差距.轻金属,2000,37(7):29-33
[4]梅炽.有色冶金炉设计手册.北京:冶金工业出版社,2000
[5]文华里.轻冶工业实用技术知识手册.沈阳:东北大学出版社,2001
[6]阎吉凯.对铝冶炼行业节能降耗潜力的预测.轻金属,1988,26(2):27-30
[7]戴小平,吴智明.200kA预焙率电解槽生产技术与实践.长沙:中南大学出版社,2006
[8]王元.160kA预焙铝电解槽节能途径探讨.青海科技,2005(3):59-60
[9]邵志博,吴举.试论电铝企业的经济运行.世界有色金属,1999(6):17-19
[10]王洪.铝电解槽节能现状及潜力.轻金属,1994,31(7):4-11
[11]李清.大型预焙槽炼铝生产工艺与操作实践.长沙:中南大学出版社,2006
[12]Grjotheim K,Kvande H.Understanding the Hall-Heroult Process for Production of Aluminum.Dusseldorf:Aluminum-Verlag,1986
[13]邱竹贤.预焙槽炼铝.北京:冶金工业出版社,2005,第3版
[14]邱竹贤.铝电解.北京:冶金工业出版社,1995,第2版
[15]霍庆发.电解铝工业技术与设备.沈阳:辽海出版社,2002
[16]殷恩生.160kA中心下料预焙铝电解槽生产工艺及管理.长沙:中南工业大学出版社,1997
[17]王捷.电解铝生产工艺与设备.北京:冶金工业出版社,2006
[18]Lai Y Q,Tian Z L,Li J,et al.Preliminary testing of NiFe_2O_4-NiO-Ni cermet as inert anode in Na_3AlF_6-AlF_3 melts.Transactions of Nonferrous Metals Society of China,2006,16(3):654-658
[19]Kan H M,Wang Z W,Ban Y G,et al.Electrical conductivity of Na_3AIF_6-AlF_3-Al_2O_3-CaF_2-LiF(NaCl) system electrolyte.Transactions of Nonferrous Metals Society of China,2007,17(1):181-186
[20]Piskazhova T V,Mann V C.The use of a dynamic aluminum cell model.JOM,2006,58(2):48-52
[21]Arai K,Ramazaki K.Heat balance and thermal losses in advanced prebaked anode cells.Light Metals,1975:193-198
[22]Dupuis M.Thermo-electric design of a 400 kA cell using mathematical models:a tutorial.Light Metals,2000:297-302
[23]冯乃祥.铝电解.北京:化学工业出版社,2006
[24]梅炽,王前普.铝电解槽热场研究.轻金属,1992,29(1):29-31
[25]Moreau R J,Ziegler D.Stability of aluminum cells - a new approach.Light Metals,1986:359-364
[26]Palsen K A.Variations of lining temperature anode position and current/voltage load in aluminum reduction cells.Light Metals,1980:325-341
[27]冯乃祥,孙阳,刘刚.铝电解槽热场、磁场和流场及其数值计算.沈阳:东北大学出版社,2001
[28]梅炽,汤洪清,孟柏庭.铝电解槽电、热解析数学模型及数值仿真试验.中南矿冶学院学报,1986,52(6):29-37
[29]林燕.铝电解槽槽膛内形在线监测的研究及应用:[硕士学位论文].长沙:中南大学,2007
[30]Merz R M,Shannon D J.Monitoring of freeze profiles in operating cells.Light Metals,1987:257-260
[31]刘海石.大型预焙槽的槽膛.轻金属,1994,31(7):34-36
[32]刘海石.再谈大型预焙槽的槽膛.轻金属,1996,33(10):33-35
[33]游旺.大型预焙铝电解槽槽膛内形在线动态仿真研究:[博士学位论文].长沙:中南大学,1997
[34]Haupin W E.Calculating thickness of containing walls frozen from melt.Light Metals,1971:305-309
[35]李景江,邱贤竹.铝电解槽槽帮结壳形状的计算机模拟.东北工学院学报,1989,10(3):231-237
[36]Peacy G,Medlin G W.Cell sidewall studies at Noranda aluminum.Light Metals,1979:475-482
[37]EK A,Flankmark G.Simulation of thermal,electrical and chemical behavior of an aluminum cell on a digital computer.Light Metals,1973:85-93
[38]Bruggeman J N,Danka D J.Two-dimensional thermal modeling of the Hall-Heroult Cell.Light Metals,1990:203-211
[39]Ahmed H A.Development of a thermal model for prebaked aluminum reduction at the aluminum company of Egypt.Light Metals,1993:258-266
[40]Clair J.Computer calculation of the thermal and electrical phenomena in the cathodes of aluminum electrolytic cells.Transactions of the Metal Society of AIME,1967,23(9):1365-1371
[41]Marc D.Thermo-electric coupled field analysis of aluminum reduction cells using the ANSYS Parametric Design Language.Proceedings of the 5~(th)International Conference and Exhibition,Pittsburgh,Pennsylvania,USA,May 20-24,1991:1-6
[42]Marc D.Thermo-electric analysis of aluminum reduction cells.Light Metals,1994:1-6
[43]Marc D.Usage of a full 3D transient thermo-electric F.E.Model to study the thermal gradient generated in the lining during a coke preheat.Light Metals,2001:757-762
[44]Marc D.Computation of aluminum reduction cell energy balance using ANSYS finite element models.Light Metals,1998:409-412
[45]Marc D.Towards the development of 3D full cell and external bus bars thermo-electric model.CIM,2002,25(1):592-599
[46]Hashimoto T,Ikeuchi H.Computer simulation of dynamic behavior of an aluminum reduction cell.Light Metals,1980:273-280
[47]Mark P T.The dynamics and performance of reduction cell electrolyte.Light Metals,1990:259-266
[48]Paulsen K A.Variations of side lining temperature,anode position and current /voltage load in aluminum reduction cells.Light Metals,1980:25-34
[49]Dow D W,Goodnow W H.Influence of operating variables on reduction cell bath emperature.Light Metals,1972:246-251
[50]Zhao H,Zhang M.Influence of environmental temperature on energy balance of electrolysis cells.Light Metals,1992:246-255
[51]张明杰,赵恒先.45KA自焙阳极电解槽动态热平衡.轻金属,1991,29(6):36-40
[52]Schmidt-Hatting W.Heat losses of different Pots.Light Metals,1985:120-126
[53]Mark P T.The dynamics and performance of reduction cell electrolyte.Light Metals,1990:259-266
[54]梅炽,游旺.铝电解槽槽膛内形在线显示仿真软件的研究与开发.中南工 业大学学报,1997,28(2):138-141
[55]游旺,王前普.铝电解槽槽膛内形在线仿真理论研究.中国有色金属学报,1998,8(4):695-699
[56]Marc D,Valdis B.Weakly coupled thermo-electric and MHD mathematical models of an aluminum electrolysis cell.Light Metals,2005:449-454
[57]Gennady V A,Alexander V R.The aluminum reduction cell closed system of 3D mathematical models.Light Metals,2005:589-592
[58]梅炽.有色冶金炉窑仿真与控制.北京:冶金工业出版社,2001
[59]周萍.铝电解槽槽膛内形的连续监测:[硕士学位论文].长沙:中南工业大学,1991
[60]吴乐谋.铝电解槽热场研究--熔体与槽帮之间传热系数的计算及研究:[硕士学位论文].长沙:中南工业大学,1988
[61]李德祥,郭天立.铝电解槽内熔体与槽帮间换热系数计算.有色金属,1993,25(1):45-45
[62]Evans J W.The evolution of technology for light metals over the last 50 years:Al,Mg,and Li.JOM,2007,59(2):30-38.
[63]Haupin W.The influence of additives on Hall-H(?)roult bath properties.JOM,1991,43(11):28-34.
[64]Salt D J.Bath chemistry control system.Light Metals,1990:369-374.
[65]Wilson M J.Practical considerations used in the development of a method for calculating aluminum fluoride additions based on cell temperatures.Light Metals,1992:375-378.
[66]Drengstig T,Ljungquist D,Foss B.On AlF_3 and temperature control of aluminum electrolysis cell.IEEE Transactions on Control Systems Technology,1998,6(2):157-171.
[67]McFadden F J S,Welch B J,Austin P C.The multivariable model-based control of the non-alumina electrolyte variables in aluminum smelting cells.JOM,2006,58(2):42-47.
[68]Entner P M.Control of AlF_3 concentration.Light Metals,1992:369-374.
[69]Entner P M.Further development of the AlF_3-model.Light Metals,1993:265-268.
[70]Meghlaoui A,Farsi Y A A,Aljabri N.Analytical and experimental study of fluoride evolution.Light Metals,2002:283-287.
[71]Meghlaoui A,Aljabri N.Aluminum fluoride control strategy improvement. Light Metals,2003:425-429.
[72]Kolas S.Defining and verifying the correlation line in aluminum electrolysis.JOM,2007,59(5):55-60.
[73]Feng N X,Liang F H,Sun Y,et al.Numerical calculation of thermal field and heat transfer coefficient for 160kA aluminum reduction cell.Transactions of Nonferrous Metals Society of China,2003,13(4):953-957.
[74]Bruggeman J N,Danka D J.Two-dimensional thermal modeling of the Hall-Heroult cell.Light Metals,1990:203-209.
[75]George A,Alan J,Lee H.Linear Regression Analysis(2~(nd) edition).New York:John Wiley & Sons,2003
[76]胡上序.观测数据的分析与处理.杭州:浙江大学出版社,1994
[77]Allen L E.An Introduction to Linear Regression and Correlation.New York:Freeman,1984
[78]Imprenta P.Applied linear regression.New York:John Wiley & Sons,1985
[79]陈振林,许晔.基于多元线性回归分析的高精度温度测量.电子测量与仪器学报,2000,14(3):9-13
[80]李金海.多元回归分析在预测中的应用.河北工业大学学报,1996,25(3):57-61
[81]Samprit C,Ali S H.Sensitivity Analysis in Linear Regression.New York:John Wiley & Sons,1988
[82]Dietmar S,Katy De,Thienpont L M.Validity of linear regression in method comparison studies:is it limited by the statistical model or the quality of the analytical input data? Clinical Chemistry,1998,44(11):2340-2346
[83]Browne M W.Predictive validity of a linear regression equation.British Journal of Mathematical and Statistical Psychology,1975,28(1):79-87
[84]吴翊.应用数据统计.北京:国防科技大学出版社,1995
[85]George A F,Seber C J W.Nonlinear Regression.New York:John Wiley &Sons,2005
[86]Opfermann J.Kinetic analysis using multivariate nonlinear regression.I.basic concepts.Journal of Thermal Analysis and Calorimetry,2000,60(2):641-658
[87]张堃,林少琨,林木良.热分析动力学多元非线性拟合法简介及其应用.现代科学仪器,2002,12(5):15-18
[88]McElroy M B,Burmeister E.Arbitrage pricing theory as a restricted nonlinear multivariate regression model:iterated nonlinear seemingly unrelated regression estimates.Journal of Business and Economic Statistics,1988,6(1):29-42
[89]王勇,阮奇.多元非线性多项式智能拟合法及其应用研究.计算机与应用化学,2004,21(1):157-162
[90]陶菊春,吴建民.可线性化非线性回归预测模型的剖析与改进.数学的实践与认识,2003,33(2):7-12
[91]Melanie M.An Introduction to Genetic Algorithms.Cambridge:MIT Press,1996
[92]Randy L.H,Sue E H.Practical Genetic Algorithms(2~(nd) edition).New York:John Wiley & Sons,2004
[93]李敏强,寇纪淞.遗传算法的基本理论与应用.北京:科学出版社,2002
[94]Goldberg D E.Genetic Algorithms in Search,Optimization and Machine Learning.Boston:Addison-Wesley Longman Publishing Company,1989
[95]Fonseca C M,Fleming P J.Genetic Algorithms for Multiobjective Optimization:Formulation,Discussion and Generalization.In:Forrest S,ed.Proceedings of the Fifth International Conference on Genetic Algorithms.San Mateo:Morgan Kaufmann,1993.15-22
[96]杨启文,蒋静坪,张国宏.遗传算法控制速度改进.软件学报,2001,12(2):270-275
[97]Weile D S,Michielssen E.Genetic algorithm optimization applied to electromagnetics:a review.IEEE Transactions on Antennas and Propagation,1997,45(3):343-353
[98]Busacca P G,Marseguerra M,Zio E.Multiobjective optimization by genetic algorithms:application to safety systems.Reliability Engineering & System Safety,2001,72(1):59-74
[99]Tang K S,Man K F,Kwong S,He Q.Genetic algorithms and their applications.IEEE Signal Processing Magazine,1996,13(6):22-37
[100]陈国良.遗传算法及其应用.北京:人民邮电出版社,1996
[101]苑希民,李鸿雁,刘树坤,等.神经网络和遗传算法在水科学领域中的应用.北京:中国水利水电出版社,2002
[102]张文修,梁怡.遗传算法的数学基础.西安:西安交通大学出版社,2000
[103]张宏建,孙志强.现代检测技术.北京:化学工业出版社,2007
[104]王小平,曹立明.遗传算法--理论应用与软件实现.西安:西安交通大学出版社,2002
[105]周明,孙树栋.遗传算法原理及应用.北京:国防工业出版社,1999
[106]云庆夏,黄光球,王战权.遗传算法和遗传规划.北京:冶金工业出版社,1997
[107]玄光南著,陈润伟译.遗传算法与工程设计.北京:科学出版社,2000
[108]梁芳慧,冯乃祥,孙阳.铝电解槽电压电流强度变化对电解槽热平衡的影响计算.轻金属,2000,37(12):33-36
[109]毕惟红,任红民,吴庆标.一种新的遗传算法最优保存策略.浙江大学学报(理学版),2006,33(1):7-10
[110]Vapnik V N.The Nature of Statistical Learning Theory.New York:Springer-Verlag,1995
[111]Bernhard E B,Isabelle G,Vapnik V N.A training algorithm for optimal margin classifiers.David Haussler eds,Proceedings of the 4th Workshop on Computational Learning Theory.San Mateo,CA:ACM Press,1992:144-152
[112]宋晓峰,陈德钊.支持向量机中控制算法.计算机科学,2003,30(3):12-20
[113]张学工.关于统计学习理论与支持向量机.自动化学报,2000,26(1):32-42
[114]Vapnik V N.Estimation of Dependencies Based on Empirical Data.Berlin:Spfinger-Verlag,1982
[115]Scholkopf B,Smola A,Muller K R.Nonlinear component analysis as kernel eigenvalue problem.Neural Computation,1998,10(5):1299-1319
[116]刘江华 程君实.支持向量机训练算法综述.信息与控制,2002,31(1):45-50
[117]Anguita D,Ridella S,Rovetta S.Circuital implementation of support vector machines.Electronics Letters,1998,34(16):1596-1597
[118]Hearst M A,Scholkopf B,Dumais S,et al.Trends and controversies - support vector machines,IEEE Intelligent Systems,1998,13(4):18-28
[119]杜树新,吴铁军.用于回归估计的支持向量机方法.系统仿真学报,2003,15(11):1580-1586
[120]阎威武,朱宏栋,邵惠鹤.基于最小二乘支持向量机的软测量建模.系统仿真学报,2003,15(10):1494-1496
[121]Pabitra M,Murthy C A,Sankar K P.A probabilistic active support vector learning algorithm.IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(3):413-418
[122]马勇,黄德先,金以慧.基于支持向量机的软测量建模方法.信息与控制.2004,33(4):417-421
[123]张健沛 徐华.支持向量机主动学习方法研究与应用.计算机应用,2004,24(1):1-3
[124]Cristianini N,Shawe-Taylor J.An introduction to support Vector Machines and other kernel-based learning methods.New York:Cambridge University Press,1999
[125]Platt J C.Advances in kernel methods:support vector learning.New York:Cambridge University Press,1999
[126]Hearst M A.Support Vector Machines.IEEE Intelligent Systems,1998,13(4):18-28
[127]胡寿松,王源.基于支持向量机的非线性系统故障诊断.控制与决策,2001,16(5):617-620
[128]Kao W,Chung K M,Sun C L,et al.Decomposition methods for linear support vector machines.Neural Computation,2004,16(8):1689-1704
[129]Zanghirati G,Zanni L.A parallel solver for large quadratic programs in training support vector machines.Parallel Computing,2003,29(4):535-551
[130]Smola A J,Scholkopf B.A tutorial on support vector regression.Statistics and Computing,2004,14(3):199-222
[131]Widodo A,Yang B S.Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors.Expert Systems with Applications,2007,33(1):241-250
[132]周经国.Delphi程序设计.北京:机械工业出版社,2007
[133]张世明,曹德胜.Delphi程序设计基础教程.北京:北京大学出版社,2006
[2]中华人民共和国国家统计局.中国统计年鉴-2007.北京:中国统计出版社,2007
[3]冯乃祥.我国铝电解工业现状和与国外先进技术水平的差距.轻金属,2000,37(7):29-33
[4]梅炽.有色冶金炉设计手册.北京:冶金工业出版社,2000
[5]文华里.轻冶工业实用技术知识手册.沈阳:东北大学出版社,2001
[6]阎吉凯.对铝冶炼行业节能降耗潜力的预测.轻金属,1988,26(2):27-30
[7]戴小平,吴智明.200kA预焙率电解槽生产技术与实践.长沙:中南大学出版社,2006
[8]王元.160kA预焙铝电解槽节能途径探讨.青海科技,2005(3):59-60
[9]邵志博,吴举.试论电铝企业的经济运行.世界有色金属,1999(6):17-19
[10]王洪.铝电解槽节能现状及潜力.轻金属,1994,31(7):4-11
[11]李清.大型预焙槽炼铝生产工艺与操作实践.长沙:中南大学出版社,2006
[12]Grjotheim K,Kvande H.Understanding the Hall-Heroult Process for Production of Aluminum.Dusseldorf:Aluminum-Verlag,1986
[13]邱竹贤.预焙槽炼铝.北京:冶金工业出版社,2005,第3版
[14]邱竹贤.铝电解.北京:冶金工业出版社,1995,第2版
[15]霍庆发.电解铝工业技术与设备.沈阳:辽海出版社,2002
[16]殷恩生.160kA中心下料预焙铝电解槽生产工艺及管理.长沙:中南工业大学出版社,1997
[17]王捷.电解铝生产工艺与设备.北京:冶金工业出版社,2006
[18]Lai Y Q,Tian Z L,Li J,et al.Preliminary testing of NiFe_2O_4-NiO-Ni cermet as inert anode in Na_3AlF_6-AlF_3 melts.Transactions of Nonferrous Metals Society of China,2006,16(3):654-658
[19]Kan H M,Wang Z W,Ban Y G,et al.Electrical conductivity of Na_3AIF_6-AlF_3-Al_2O_3-CaF_2-LiF(NaCl) system electrolyte.Transactions of Nonferrous Metals Society of China,2007,17(1):181-186
[20]Piskazhova T V,Mann V C.The use of a dynamic aluminum cell model.JOM,2006,58(2):48-52
[21]Arai K,Ramazaki K.Heat balance and thermal losses in advanced prebaked anode cells.Light Metals,1975:193-198
[22]Dupuis M.Thermo-electric design of a 400 kA cell using mathematical models:a tutorial.Light Metals,2000:297-302
[23]冯乃祥.铝电解.北京:化学工业出版社,2006
[24]梅炽,王前普.铝电解槽热场研究.轻金属,1992,29(1):29-31
[25]Moreau R J,Ziegler D.Stability of aluminum cells - a new approach.Light Metals,1986:359-364
[26]Palsen K A.Variations of lining temperature anode position and current/voltage load in aluminum reduction cells.Light Metals,1980:325-341
[27]冯乃祥,孙阳,刘刚.铝电解槽热场、磁场和流场及其数值计算.沈阳:东北大学出版社,2001
[28]梅炽,汤洪清,孟柏庭.铝电解槽电、热解析数学模型及数值仿真试验.中南矿冶学院学报,1986,52(6):29-37
[29]林燕.铝电解槽槽膛内形在线监测的研究及应用:[硕士学位论文].长沙:中南大学,2007
[30]Merz R M,Shannon D J.Monitoring of freeze profiles in operating cells.Light Metals,1987:257-260
[31]刘海石.大型预焙槽的槽膛.轻金属,1994,31(7):34-36
[32]刘海石.再谈大型预焙槽的槽膛.轻金属,1996,33(10):33-35
[33]游旺.大型预焙铝电解槽槽膛内形在线动态仿真研究:[博士学位论文].长沙:中南大学,1997
[34]Haupin W E.Calculating thickness of containing walls frozen from melt.Light Metals,1971:305-309
[35]李景江,邱贤竹.铝电解槽槽帮结壳形状的计算机模拟.东北工学院学报,1989,10(3):231-237
[36]Peacy G,Medlin G W.Cell sidewall studies at Noranda aluminum.Light Metals,1979:475-482
[37]EK A,Flankmark G.Simulation of thermal,electrical and chemical behavior of an aluminum cell on a digital computer.Light Metals,1973:85-93
[38]Bruggeman J N,Danka D J.Two-dimensional thermal modeling of the Hall-Heroult Cell.Light Metals,1990:203-211
[39]Ahmed H A.Development of a thermal model for prebaked aluminum reduction at the aluminum company of Egypt.Light Metals,1993:258-266
[40]Clair J.Computer calculation of the thermal and electrical phenomena in the cathodes of aluminum electrolytic cells.Transactions of the Metal Society of AIME,1967,23(9):1365-1371
[41]Marc D.Thermo-electric coupled field analysis of aluminum reduction cells using the ANSYS Parametric Design Language.Proceedings of the 5~(th)International Conference and Exhibition,Pittsburgh,Pennsylvania,USA,May 20-24,1991:1-6
[42]Marc D.Thermo-electric analysis of aluminum reduction cells.Light Metals,1994:1-6
[43]Marc D.Usage of a full 3D transient thermo-electric F.E.Model to study the thermal gradient generated in the lining during a coke preheat.Light Metals,2001:757-762
[44]Marc D.Computation of aluminum reduction cell energy balance using ANSYS finite element models.Light Metals,1998:409-412
[45]Marc D.Towards the development of 3D full cell and external bus bars thermo-electric model.CIM,2002,25(1):592-599
[46]Hashimoto T,Ikeuchi H.Computer simulation of dynamic behavior of an aluminum reduction cell.Light Metals,1980:273-280
[47]Mark P T.The dynamics and performance of reduction cell electrolyte.Light Metals,1990:259-266
[48]Paulsen K A.Variations of side lining temperature,anode position and current /voltage load in aluminum reduction cells.Light Metals,1980:25-34
[49]Dow D W,Goodnow W H.Influence of operating variables on reduction cell bath emperature.Light Metals,1972:246-251
[50]Zhao H,Zhang M.Influence of environmental temperature on energy balance of electrolysis cells.Light Metals,1992:246-255
[51]张明杰,赵恒先.45KA自焙阳极电解槽动态热平衡.轻金属,1991,29(6):36-40
[52]Schmidt-Hatting W.Heat losses of different Pots.Light Metals,1985:120-126
[53]Mark P T.The dynamics and performance of reduction cell electrolyte.Light Metals,1990:259-266
[54]梅炽,游旺.铝电解槽槽膛内形在线显示仿真软件的研究与开发.中南工 业大学学报,1997,28(2):138-141
[55]游旺,王前普.铝电解槽槽膛内形在线仿真理论研究.中国有色金属学报,1998,8(4):695-699
[56]Marc D,Valdis B.Weakly coupled thermo-electric and MHD mathematical models of an aluminum electrolysis cell.Light Metals,2005:449-454
[57]Gennady V A,Alexander V R.The aluminum reduction cell closed system of 3D mathematical models.Light Metals,2005:589-592
[58]梅炽.有色冶金炉窑仿真与控制.北京:冶金工业出版社,2001
[59]周萍.铝电解槽槽膛内形的连续监测:[硕士学位论文].长沙:中南工业大学,1991
[60]吴乐谋.铝电解槽热场研究--熔体与槽帮之间传热系数的计算及研究:[硕士学位论文].长沙:中南工业大学,1988
[61]李德祥,郭天立.铝电解槽内熔体与槽帮间换热系数计算.有色金属,1993,25(1):45-45
[62]Evans J W.The evolution of technology for light metals over the last 50 years:Al,Mg,and Li.JOM,2007,59(2):30-38.
[63]Haupin W.The influence of additives on Hall-H(?)roult bath properties.JOM,1991,43(11):28-34.
[64]Salt D J.Bath chemistry control system.Light Metals,1990:369-374.
[65]Wilson M J.Practical considerations used in the development of a method for calculating aluminum fluoride additions based on cell temperatures.Light Metals,1992:375-378.
[66]Drengstig T,Ljungquist D,Foss B.On AlF_3 and temperature control of aluminum electrolysis cell.IEEE Transactions on Control Systems Technology,1998,6(2):157-171.
[67]McFadden F J S,Welch B J,Austin P C.The multivariable model-based control of the non-alumina electrolyte variables in aluminum smelting cells.JOM,2006,58(2):42-47.
[68]Entner P M.Control of AlF_3 concentration.Light Metals,1992:369-374.
[69]Entner P M.Further development of the AlF_3-model.Light Metals,1993:265-268.
[70]Meghlaoui A,Farsi Y A A,Aljabri N.Analytical and experimental study of fluoride evolution.Light Metals,2002:283-287.
[71]Meghlaoui A,Aljabri N.Aluminum fluoride control strategy improvement. Light Metals,2003:425-429.
[72]Kolas S.Defining and verifying the correlation line in aluminum electrolysis.JOM,2007,59(5):55-60.
[73]Feng N X,Liang F H,Sun Y,et al.Numerical calculation of thermal field and heat transfer coefficient for 160kA aluminum reduction cell.Transactions of Nonferrous Metals Society of China,2003,13(4):953-957.
[74]Bruggeman J N,Danka D J.Two-dimensional thermal modeling of the Hall-Heroult cell.Light Metals,1990:203-209.
[75]George A,Alan J,Lee H.Linear Regression Analysis(2~(nd) edition).New York:John Wiley & Sons,2003
[76]胡上序.观测数据的分析与处理.杭州:浙江大学出版社,1994
[77]Allen L E.An Introduction to Linear Regression and Correlation.New York:Freeman,1984
[78]Imprenta P.Applied linear regression.New York:John Wiley & Sons,1985
[79]陈振林,许晔.基于多元线性回归分析的高精度温度测量.电子测量与仪器学报,2000,14(3):9-13
[80]李金海.多元回归分析在预测中的应用.河北工业大学学报,1996,25(3):57-61
[81]Samprit C,Ali S H.Sensitivity Analysis in Linear Regression.New York:John Wiley & Sons,1988
[82]Dietmar S,Katy De,Thienpont L M.Validity of linear regression in method comparison studies:is it limited by the statistical model or the quality of the analytical input data? Clinical Chemistry,1998,44(11):2340-2346
[83]Browne M W.Predictive validity of a linear regression equation.British Journal of Mathematical and Statistical Psychology,1975,28(1):79-87
[84]吴翊.应用数据统计.北京:国防科技大学出版社,1995
[85]George A F,Seber C J W.Nonlinear Regression.New York:John Wiley &Sons,2005
[86]Opfermann J.Kinetic analysis using multivariate nonlinear regression.I.basic concepts.Journal of Thermal Analysis and Calorimetry,2000,60(2):641-658
[87]张堃,林少琨,林木良.热分析动力学多元非线性拟合法简介及其应用.现代科学仪器,2002,12(5):15-18
[88]McElroy M B,Burmeister E.Arbitrage pricing theory as a restricted nonlinear multivariate regression model:iterated nonlinear seemingly unrelated regression estimates.Journal of Business and Economic Statistics,1988,6(1):29-42
[89]王勇,阮奇.多元非线性多项式智能拟合法及其应用研究.计算机与应用化学,2004,21(1):157-162
[90]陶菊春,吴建民.可线性化非线性回归预测模型的剖析与改进.数学的实践与认识,2003,33(2):7-12
[91]Melanie M.An Introduction to Genetic Algorithms.Cambridge:MIT Press,1996
[92]Randy L.H,Sue E H.Practical Genetic Algorithms(2~(nd) edition).New York:John Wiley & Sons,2004
[93]李敏强,寇纪淞.遗传算法的基本理论与应用.北京:科学出版社,2002
[94]Goldberg D E.Genetic Algorithms in Search,Optimization and Machine Learning.Boston:Addison-Wesley Longman Publishing Company,1989
[95]Fonseca C M,Fleming P J.Genetic Algorithms for Multiobjective Optimization:Formulation,Discussion and Generalization.In:Forrest S,ed.Proceedings of the Fifth International Conference on Genetic Algorithms.San Mateo:Morgan Kaufmann,1993.15-22
[96]杨启文,蒋静坪,张国宏.遗传算法控制速度改进.软件学报,2001,12(2):270-275
[97]Weile D S,Michielssen E.Genetic algorithm optimization applied to electromagnetics:a review.IEEE Transactions on Antennas and Propagation,1997,45(3):343-353
[98]Busacca P G,Marseguerra M,Zio E.Multiobjective optimization by genetic algorithms:application to safety systems.Reliability Engineering & System Safety,2001,72(1):59-74
[99]Tang K S,Man K F,Kwong S,He Q.Genetic algorithms and their applications.IEEE Signal Processing Magazine,1996,13(6):22-37
[100]陈国良.遗传算法及其应用.北京:人民邮电出版社,1996
[101]苑希民,李鸿雁,刘树坤,等.神经网络和遗传算法在水科学领域中的应用.北京:中国水利水电出版社,2002
[102]张文修,梁怡.遗传算法的数学基础.西安:西安交通大学出版社,2000
[103]张宏建,孙志强.现代检测技术.北京:化学工业出版社,2007
[104]王小平,曹立明.遗传算法--理论应用与软件实现.西安:西安交通大学出版社,2002
[105]周明,孙树栋.遗传算法原理及应用.北京:国防工业出版社,1999
[106]云庆夏,黄光球,王战权.遗传算法和遗传规划.北京:冶金工业出版社,1997
[107]玄光南著,陈润伟译.遗传算法与工程设计.北京:科学出版社,2000
[108]梁芳慧,冯乃祥,孙阳.铝电解槽电压电流强度变化对电解槽热平衡的影响计算.轻金属,2000,37(12):33-36
[109]毕惟红,任红民,吴庆标.一种新的遗传算法最优保存策略.浙江大学学报(理学版),2006,33(1):7-10
[110]Vapnik V N.The Nature of Statistical Learning Theory.New York:Springer-Verlag,1995
[111]Bernhard E B,Isabelle G,Vapnik V N.A training algorithm for optimal margin classifiers.David Haussler eds,Proceedings of the 4th Workshop on Computational Learning Theory.San Mateo,CA:ACM Press,1992:144-152
[112]宋晓峰,陈德钊.支持向量机中控制算法.计算机科学,2003,30(3):12-20
[113]张学工.关于统计学习理论与支持向量机.自动化学报,2000,26(1):32-42
[114]Vapnik V N.Estimation of Dependencies Based on Empirical Data.Berlin:Spfinger-Verlag,1982
[115]Scholkopf B,Smola A,Muller K R.Nonlinear component analysis as kernel eigenvalue problem.Neural Computation,1998,10(5):1299-1319
[116]刘江华 程君实.支持向量机训练算法综述.信息与控制,2002,31(1):45-50
[117]Anguita D,Ridella S,Rovetta S.Circuital implementation of support vector machines.Electronics Letters,1998,34(16):1596-1597
[118]Hearst M A,Scholkopf B,Dumais S,et al.Trends and controversies - support vector machines,IEEE Intelligent Systems,1998,13(4):18-28
[119]杜树新,吴铁军.用于回归估计的支持向量机方法.系统仿真学报,2003,15(11):1580-1586
[120]阎威武,朱宏栋,邵惠鹤.基于最小二乘支持向量机的软测量建模.系统仿真学报,2003,15(10):1494-1496
[121]Pabitra M,Murthy C A,Sankar K P.A probabilistic active support vector learning algorithm.IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(3):413-418
[122]马勇,黄德先,金以慧.基于支持向量机的软测量建模方法.信息与控制.2004,33(4):417-421
[123]张健沛 徐华.支持向量机主动学习方法研究与应用.计算机应用,2004,24(1):1-3
[124]Cristianini N,Shawe-Taylor J.An introduction to support Vector Machines and other kernel-based learning methods.New York:Cambridge University Press,1999
[125]Platt J C.Advances in kernel methods:support vector learning.New York:Cambridge University Press,1999
[126]Hearst M A.Support Vector Machines.IEEE Intelligent Systems,1998,13(4):18-28
[127]胡寿松,王源.基于支持向量机的非线性系统故障诊断.控制与决策,2001,16(5):617-620
[128]Kao W,Chung K M,Sun C L,et al.Decomposition methods for linear support vector machines.Neural Computation,2004,16(8):1689-1704
[129]Zanghirati G,Zanni L.A parallel solver for large quadratic programs in training support vector machines.Parallel Computing,2003,29(4):535-551
[130]Smola A J,Scholkopf B.A tutorial on support vector regression.Statistics and Computing,2004,14(3):199-222
[131]Widodo A,Yang B S.Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors.Expert Systems with Applications,2007,33(1):241-250
[132]周经国.Delphi程序设计.北京:机械工业出版社,2007
[133]张世明,曹德胜.Delphi程序设计基础教程.北京:北京大学出版社,2006