基于热分析及其时间序列分析的高磷铁矿还原过程动力学研究
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摘要
云南省高磷铁矿资源十分丰富,其中惠民铁矿、罗茨铁矿等高磷铁矿总储量超过20亿吨,但因其磷含量高,难以大量应用于炼铁工业。而惠民矿属于高磷褐铁矿的种,富含结晶水、比重小、磁性弱、可浮性低,故难以通过物理选矿法提高其品位。由于传统高炉炼铁生产发展受到资源、环保等方面的制约,我国不仅要继续完善和改进高炉炼铁工艺,而且还应适度发展直接还原和熔融还原等技术,特别是直接利用粉矿、粉煤为原料的非高炉炼铁新技术。为了揭示高磷铁矿粉的还原过程动力学行为特征及传热传质规律,本文采用数学建模与实验研究相结合的方法对惠民铁矿粉在高温下的普通煤粉还原过程动力学行为开展研究,通过热分析和时间序列分析方法对矿粉的还原过程非平衡态进行阶段建模、特征提取和还原过程中传热传质行为的考察,并使用分析测试技术进行实验验证,为昆明钢铁集团早日应用Hismelt熔融还原技术处理矿粉提供理论依据。
     本文在实验的基础上运用失重法研究了40℃~1400℃条件下,普通煤粉还原惠民高磷铁矿粉的动力学过程。首先利用热重分析仪对惠民铁矿进行还原实验来获取熔融还原过程的TG(质量变化)-DSC(热量变化)曲线,分别根据其曲线的升降变化和峰值特征得出反应的热力学数据及动力学数据,将整个过程分为:(1)结晶水脱除阶段:(2)赤铁矿预还原阶段;(3)熔化态阶段;(4)Fe2O3完全还原阶段:(5)Fe3O4还原为FeO阶段;(6)FeO还原为Fe阶段。其次运用热分析动力学中的积分法结合48个经验机理模型构建出备反应阶段的表观动力学模型,包括动力学参数,如表观活化能、频率因子和最概然机理函数(E,A,f(α))的计算,发现随着温度的升高,关键阶段所需的表观活化能呈现了递增的趋势,说明主元反应依次序进行,而通过纵向的补偿效应分析后发现E和1gA之间相互依赖且协同变化明显,说明还原过程中各类反应进行的难易程度与碰撞概率之间相互影响显著。然后通过公式推导和数据拟合建立了表观活化能随温度、升温速率的分布函数表达式,其函数曲线呈现出类高斯分布,并表现为后拖尾现象,表明表观活化能在高温段的分布稀疏且变化较快,体现了与熔融还原的能量补给需要一致;经过拟合得出了还原过程的频率因子表达式,其曲线拐点揭示出还原过程在熔化状态下活化分子之间的碰撞次数明显增加,有利于还原的进行。
     经过综合考虑提炼出能反映还原系统运行全貌及其细节的物理量—表观反应速率(DTG时间序列数据),该物理量不仅能够反映冶炼系统进行过程中的动力学行为,也能体现过程中的传热传质行为。具体内容如下:(1)运用动力系统理论中的相重构技术对实验所获取的DTG序列数据进行重构分析,并从表观反应速率的演化中恢复熔融还原过程中的动力学行为,计算结果表明:该序列的一阶时间延迟和二维嵌入能够准确地反映系统的动力学行为;该确定性复杂冶炼系统求属随机离散动力系统范畴,具有双“∞”的混沌吸引子结构,我们将其命名为铁还原过程吸引子:通过文献比较推断双“∞”曲线中内“∞”上的点表征着冶炼系统进入界面化学反应控制阶段,外“∞”L的点则表征着系统进入扩散控制阶段,而散布在该曲线周围的点表征着混合控制的结果及其偏离程度。通过对DTG序列的递归图分析、庞加莱截面和关联维数的计算以及最大李亚普诺指数的计算过程共同表明了惠民铁矿还原过程呈现出了弱混沌现象,且该行为是铁的还原引起的,因此确定该混沌行为为有用过程,加强该行为有助于实现冶炼的强化。(2)铁还原过程吸引子不同于Lorenz系统、Rossler系统和logistic I映射系统,而混沌性态完全不同于它们,则方程也无法参照上述的类型进行构建。普适方程的计算结果表明DTG的影响因素繁多,难以应用通常的微分方程进行求解,基于此运用时间序列分析中的预测建模方法对DTG进行表达,局部加权线性模型的良好预测性能体现出反应系统内部存在着短程相关性(局部)以及主次差别性(加权),进一步建立的EMD-wAR模型能够准确的表达出能量传输过程的频率分布特征(EMD),体现了反应系统内部存在着短程相关性(AR)以及主次差别性(黄金加权),EMD-wAR较局部加权线性法误差下降了一个数量级的,而EMD-wAR-CPSO-SVR模型的建立使得误差又较EMD-wAR模型下降了一个数量级。EMD-wAR-CPSO-SVR模型更有利于建立还原过程吸引子方程。(3)通过DTG信号的功率谱分析以及比较发现纯的氧化铁还原过程其能量传输集中在低频段,而矿石还原过程集中在中高频,因此在电炉冶炼中适当加强高频段的加热方式或调整工艺参数使得冶炼过程中的中高频段能量传输加强,这些都有助于实现冶炼过程的强化。最后在几个关键温度段对惠民铁矿进行竖式电炉还原实验,利用各阶段渣相的表观形貌变化推测反应过程的动力学行为特征。
The resources of high-phosphorus iron ore are very abundant in Yunnan Province, the reserves such as the Huimin, Luoci iron ore, etc. have over2billion tons. Because of high phosphorus content, it is difficultly to be employed with large scale by the iron making industry. The Huimin iron ore is one kind of high phosphorus and Limonite, due to its some embedded features, such as rich crystallization water, the small proportion, and the weak magnetic flotability, the iron grade of ore is difficult to be improved by physical beneficiation approaches. The development of the traditional blast furnace iron production is recently constrained by resources and environmental protection consideration, in the meanwhile, our country not only has to continue to refine and improve iron making process of blast furnace, but also should moderately develop the technology of direct reduction and smelting reduction, particularly for the technology of non-blast furnace iron making focusing on taking iron ore fines or pink-coal as raw material directly. In order to reveal the kinetic behaviors and heat and mass transfer law for reduction of high-phosphorus iron ore fines, the combination methods of mathematical modeling and experimental are employed to study the kinetic behaviors in reduction process of the Huimin iron ore with the ordinary coal at high temperature. The thermal analysis and time series analysis approaches are applied to stage modeling, feature extraction slag and investigation of the behavior of heat and mass transfer process for reduction process non-equilibrium. From experimental verification of the analysis and testing technology, it can provide the theoretical application of Hismelt smelting reduction for Kunming Iron and Steel company.
     In this paper, the weight loss method is used to the kinetic process for coal reduction of the Huimin high phosphorus iron ore fines with the conditions from40℃to1400℃. Firstly, the TG-DSC curve of smelting reduction process is obtain by the iron ore experiment of Huimin reduction in TGA, and the change or peak of the curve characteristics is then used to explore the chemical reaction rule of various stages in the process of smelting reduction. The whole process is divided into six stages or segments:(1) crystalline water removal stage;(2) hematite pre-reduction stage;(3) melting transition phase;(4) Fe2O3complete conversion segment;(5) Fe3O4transformed to FeO phase;(6) FeO transformed to Fe phase. Secondly, the integration method of the Thermal analysis is employed to build the apparent kinetic model of the main stage from experience mechanism model, including the calculation of the kinetic parameters such as the apparent activation energy, frequency factor and infer the most probable mechanism function (E, A,f(α)). It is found the fact that as the temperature rises, the apparent activation energy of the key stage shows the increasing trend, in the other words, the reaction has the descending order. The longitudinal compensation effect analysis shows that the interdependence and synergy between E and lgA change significantly, the degree of difficulty and the probability of collision with each other significantly affect for all kinds of reaction during the reduction process. The apparent activation energy is got by the derivation and numerical fitting with the distribution function of the temperature and heating rate as follow:as the fitting function curve appears right tailling, the activation energy presents the sparse distribution and fast changes in the high-temperature section, the need is consistent with for energy supply in smelting reduction. After fitting process, the frequency factor expression of the reduction process can be obtained, the inflection point of the curve reveals the number of collisions between the activation of molecular significantly increase during the process in the molten state, it is helpful reduction.
     The apparent reaction rate (DTG data) of physical quantities in the system is extracted, and it can reflect the whole reduction process and details for comprehensive consideration. The physical quantity can not only reflect the dynamic behavior of the smelting system, but also can reflect the behavior for process of heat and mass transfer. The specific contents are as follows.(1) the phase reconstruction technique of dynamical systems theory is employed to reconstruction analysis, the evolution of the characteristics of the apparent reaction rate reveals the dynamic behavior in the smelting reduction process, the results show that the dynamic behavior of the sequence of a first-order time delay and two-dimensional embedding was accurate for system. it is found that the complex uncertainty smelting system which belongs to the random discrete dynamical system has the chaotic attractor structure, and the structure shows the graphic feature of double '∞' it is so-called iron reduction attractor. The points inside '∞' within the curve of double '∞' characterize that the smelting reduction system comes into the stage of interfacial chemical reaction control; it can be inferred that points outside '∞' characterize the system comes into the stage of diffusion-controlled while the points are scattered around the curve characterized the stage of the mixed control. The recurrence plot of DTG sequence, Poincare section, the calculation of the correlation dimension and the largest Lyapunov index calculation process all show that reduction of Huimin iron ore is provided with weak chaos, and the behavior is caused by the reduction of iron, so we determine the chaotic behavior as useful process, strengthen contribute to the smelting.(2) The iron reduction attractor is different from the Lorenz system, Rossler system and logistic map system, and the chaotic behavior is completely different from them, then the equation can not refer to the type. The universal equation calculation results show that there are many factors influencing the DTG, it is difficult to apply the usual differential equations to solve. Thus the predictive modeling methods in the analysis of time series is employed, the locally weighted linear prediction perfonnance reflects the reaction within the system appear the short-range correlation (local) as well as the differences of primary and secondary (weighted), In further the EMD-wAR model can accurately express the frequency distribution characteristics of the energy transfer process (EMD), also reflects that there is the short-range correlation (AR) as well as primary and secondary difference in nature (golded weighted) within the reaction system, the EMD-wAR declined an order of magnitude error than the locally weighted, the establishment of the EMD-wAR-CPSO-SVR model made errors again declined an order of magnitude, the expression of DTG is more accurate, so that it is helpful the establishment of the attractor equation in the reduction process.(3)The power spectral analysis was employed to the DTG signal and it is found the frequency concentrated in the low during energy transfermamtion of the reduction process for pure iron oxide, the ore reduction process is concentrated in the middle and high frequency. It can be appropriate to strengthen heating method in the process of smelting furnace, it will also help in the strengthening of the smelting process.
     Finally, the reduction experiment with key temperature ranges of high phosphorus iron ore by vertical electric stove were carried out, the reaction process and kinetic behavior characteristics were speculated through the phase morphology changes of various stages in slag, so as to verify the characteristics of the redcution process.
引文
[1]周渝生,钱晖,张友平等.现有主要炼铁工艺的优缺点和研发方向[J].钢铁,2009,44(2):1-9.
    [2]International Energy Agency. Biofuels for Transport-An International Perspective [M]. Paris:Office of Energy Efficiency, Technology and Research and Development, OECD/IEA,2004.
    [3]The International Energy Outlook 2008 [M]. Washington:Energy Information Administration,2008
    [4]http://www.mzyfz.com/cms/jienengjianpai/xinwenzhongxin/xinwenkuaixun/html/1110/2 012-11-10/content-567486.html.
    [5]陈津,林万明,赵晶.非焦煤冶金技术[M].北京:化学工业出版社,2007:1-5.
    [6]周渝生,钱晖,张友平等.非高炉炼铁技术的发展方向和策略[J].世界钢铁,2009,(1):1-8.
    [7]http://news.163.com/12/0705/08/85KUA0DR00014AEE.html.
    [8]http://info.china.alibaba.com/detail/1063221101.html.
    [9]冯燕波,曹维成,杨双平等.中国直接还原技术的发展现状及展望 [J].中国冶金,2006,16(5):10-11.
    [10]Zervas T, McMullan J T, Williams B C-Gas-Based Direct Reduction Processes for Iron and Steel Production[J]. International Journal of Energy Research,1996,20(2): 157.
    [11]Zervas T, McMullan J T, Williams B C. Solid-Based Processes for the Direct Reduction of Iron [J]. International Journal of Energy Research,1996,20(3):255.
    [12]HOFFMANGE, HARADA. A Status Report on FASTMET Process From the Kakogawa Demonstration Plant[J]. Iron and Steelmaker,1997,24(5):51-53.
    [13]Chatterjee A, Beyond the Blast Furnace [M]. Boca Raton:CRC,1994.
    [14]周渝生.煤基熔融还原炼铁新工艺开发现状评述[J].钢铁,2005,40(11):1-8.
    [15]周渝生,陈宏,曹传根.HIsmelt熔融还原工艺的现状与评析[J].世界钢铁2001,(5):1-2.
    [16]况志华,陈旭东.COREX C-3000技术创新上的思考[J].炼铁,2009,28(1):59-60.
    [17]唐恩,周强,翟兴华等.适合我国发展的非高炉炼铁技术[J].炼铁,2007,26(4):59-61.
    [18]Fruehan R J. Direct Reduced Iron-Technology and Economicsof Production and Use [M]. Warrendale:ISS,1999.
    [19]Neil GOODMAN. Operations at the HIsmelt Kwinana Plant[J]. GM Technical Marketing & Services, HIsmelt Corporation Pty. Limited:1-5.
    [20]Chunbao (Charles) XU, CANG Da-qiang. A Brief Overview of Low CO2 Emission Technologies for Iron and Steel Making[J]. JOURNAL OF IRON AND STEEL RESEARCH, INTERNATIONAL,2010,17(3):01-07.
    [21]Andreas Orth, Nikola Anastasijevic, Heinz Eichberger. Low CO2 emission technologies for iron and steelmaking as well as titania slag production[J]. Minerals Engineering, 2007,20(9):854-861.
    [22]Iwamasa, P.K., Fruehan, R.J.. Effect of FeO in the Slag and Silicon in the Metal on the Desulfurization of Hot Metal[J]. Metallurgical and Materials Transactions B: Process Metallurgy and Materials Processing Science,1997,28(1):47.
    [23]薛永强,赵红,杜建平.纳米氧化铜的粒度对多相反应动力学参数的影响[J].无机化学学报,2006,22(11):1952-1956.
    [24]刘明言.多相反应器能量最小多尺度建模及非线性分析-气液固三相流化床能量最小尺度模型及气液鼓泡塔非线性分析[D].中国科学院化工研究所士学位论文,2000.
    [25]肖纯,杨声海.硫化锌精矿空气氧化硫酸浸出的动力学研究[J].有色金属(冶炼部分),2008,(1):7-10.
    [26]汪金良.重金属短流程冶金炉渣活度研究与过程数值模拟[D].中南大学博士学位论文,2009.
    [27]杜挺,杜昆.含碳球团-铁浴熔融还原法的关键技术的应用基础研究[J].96年全国冶金物理化学学术年会,1996.
    [28]董凌燕.铁浴式熔融还原熔化及活度研究[D].重庆大学博士学化论文,2000.
    [29]件雄刚.渣金反应的电化学控制研究[D].上海大学博士学位论文,2001.
    [30]张波.铁浴式碳氢复吹终还原反应器动力学研究[D].上海大学博士学位论文,2011.
    [31]陶东平.粗糙表面化学反应动力学模型[J].金属学报,2001.37(10):1073-1078.
    [32]J.Zhang,O.Ostrovski. Iron ore reduction/cementation:experimental results and kinetic modelling[J]. Ironmaking and Steelmaking,2002.29(1):15-21.
    [33]Kang H.W.,Chung W.S Review of applicability of unreacted core model based on Ishida-Wen model [J]. Ironmaking and steelmaking.2004,31:117-124.
    [34]马兴亚,姜茂发,汪琦,王向辉.铁矿-煤球团反应过程动力学及模型[J].东北大学学报(自然科学版),2002.23(5):440-444.
    [35]王伟丽,董凌燕,陈登福.高磷铁矿直接还原动力学研究[J].甘肃冶金,2007.29(5):1-3.
    [36]李秋菊.微/纳米级铁矿粉气相还原动力学研究[D].上海大学博士学位论文,2008.
    [37]杨雪峰,张竹明,唐启荣等.昆钢应用HISmelt工艺的可行性[J].昆钢科技,2005,(4):1-4.
    [38]李广涛,张宗华,张昱等.四川某高磷鲕状赤褐铁矿石选矿试验研究[J].金属矿山,2008,(4):43.
    [39]袁启东,翁金红.云南东川包子铺高磷赤褐铁矿石选矿工艺研究[J].金属矿山,2007,(4):30-33.
    [40]余永富,张汉泉.我国钢铁发展对铁矿石选矿科技发展的影响[J].武汉理工大学学报,2007,29(1):6-7.
    [41]张锦瑞,胡力可,梁银英.我国难选铁矿石的研究现状及利用途径[J].金属矿山,2007,(11):8-9.
    [42]崔忠圻,覃耀春,金属学与热处理(第2版)[M].北京:机械工业出版社,2008.
    [43]郝先耀,戴惠新,赵志强.高磷铁矿石降磷的现状与存在问题探讨[J].金属矿山,2007,(1):7-10
    [44]何姜毅,周平,庄友章等.某高磷铁矿提质降磷工艺研究[J].矿业工程,2008,(2):29-31.
    [45]纪军.高磷铁矿石脱磷技术研究[J].矿冶,2003,12(2):33-37
    [46]毕学工,周进东,黄志成等. Hlsmelt法冶炼高磷矿可能性分析.2008年全国 炼铁生产技术会议暨炼铁年会论文集,宁波,中国金属学会,2008:1289-1299.
    [47]杨双平.冶金炉料处理工艺[M].北京,冶金工业出版社,2008
    [48]C. M. Lee and R. J. Fruehan. Phosphorus equilibrium between hot metal and slag [J]. Ironmaking and Steelmaking,2005,32(6):503-508.
    [49]唐洪乐,汪洪峰,孙晓辉.梅钢中磷铁水低磷钢冶炼问题的探讨[J].钢铁,2008,43(10):34-37.
    [50]魏颖娟,袁守谦,张西锋等.预熔脱磷剂进行铁水脱磷的试验研究[J].钢铁,2008,43(10):42-46.
    [51]Abraham M C,Ghosh A. Kinetics of reduction of iron oxide by carbon[J]. Ironmaking and steelmaking,1979.6(1):14-23.
    [52张翔宇,李家林,刘小银.某褐铁矿脱水反应动力学的研究[J].2010,38(4):10-13.
    [53]Bryk C, W-K LU. Reduction phenomena in composites of iron ore concentrates and coals[J]. Ironmaking and steelmaking,1986.13(2):70-75.
    [54]Dutta S K,Ghost A. Study of nonisothermal reduction of iron ore-coal composite pellet[J]. Metallurgical and Materials Transaction B.1994,25B:15.
    [55]黄典冰,杨学民,杨天钧等.含碳球团还原过程动力学及模型[J].金属学报,1996,32(6):629-636.
    [56]黄典冰,孔令坛.内配碳赤铁矿球团反应动力学及其模型[J].钢铁,1995,30(11):1-6.
    [57]Wang Q,Yang Z,Tian J,et al.Mechanisms of reduction in iron ore-coal composite pellet[J]. Ironmaking and steelmaking,1997.24(6):457-460.
    [58]范莉娟,吕清刚,那永洁.铁矿石粉煤基直接还原的热重分析[J].化工学报2010,61(12):3228-3233.
    [59]Meyer. D. Kinetics of reduction of iron oxide in molten slag by coat 1873K[J]. Ironmaking and steelmaking,1985,12:157-162.
    [60]Akahira, T., Sunose, T., Joint convention of four electrical institutes Research Report Chiba Institute and Technology,1971.16,22-31.
    [61]Avrami, M., Kinetics of phase change Ⅰ. General theory. Journal of Chemical Physics,1939.7,1103-1112.
    [62]Avrami, M., Kinetics of phase change Ⅱ. Transformation-time relations for random distribution of nuclei. Journal of Chemical Physics,1940,8,212-224.
    [63]Avrami, M. Granulation, phase change, and microstructure kinetics of phase change. III. Journal of Chemical Physics,1941,9,177-184.
    [64]Bandrowski, J., Bickling, C.R., Yang, K.H., Hougen, O.A. Kinetics of the reduction of nickel oxide. Chemical Engineering Science,1962.17,379-390.
    [65]Benton, A.F., Emmett, P.H The reduction of nickelous and Ferric oxides by hydrogen. Journal of American Chemical Society,1924.46,2728-2737.
    [66]Budrugeac, P., Homentcovschi, D., Segal, E. Critical analysis of the isoconversional methods for evaluating the activation energy. I. Theoretical background. Journal of Thermal Analysis and Calorimetry,2001,63,457-463.
    [67]Coats, A.W., Redfern, J.P.. Kinetic parameters from thermogravimetric data. Nature, 1964,201,68-69.
    [68]Delmon, B. In:Ertl, G., Knozinger, H., Weitkamp, J. (Eds.), Handbook of Hete rogeneous Catalysis. Wiley-VCH, New York,,1997.p.264.
    [69]Doyle, C.D.,. Estimating thermal stability of experimental polymers by empirical thermogravimetric analysis. Analytical Chemistry 1961a,33,77-79.
    [70]Doyle, C.D.. Kinetic analysis of thermogravimetric data. Journal of Applied Polymer Science,1961b,5,285-292.
    [71]Flynn, J.H. The isoconversional method for determination of energy of activation at constant heating rates. Journal of Thermal Analysis,1983,27,95-102.
    [72]Flynn, J.H., Wall, L.A. General treatment of the thermogravimetry of polymers. Journal of Research of the National Bureau of Standards-A. Physics and Chemistry., 1966,70,487-523.
    [73]Friedman, H.L.. Kinetics of thermal degradation of char-forming plastics from thcrmo-gravimetry. Applications to a phenol plastic. Journal of Polymer Science Part C,19636,183-195.
    [74]Furstenau, R.P., McDougall, G., Langell, M.A.. Initial stages of hydrogen reduction of NiO (100). Surface Science,1985 150,55-79.
    [75]Jankovi'c, B., AdnaYevi'c, B., Mentus, S., The kinetic analysis of non-isothermal nickel oxide reduction in hydrogen atmosphere using the invariant kinetic parameters method. Thermochimica Acta,2007.456,48-55.
    [76]Kissinger, H.E. Reaction kinetics in differential thermal analysis. Analytical Chemistry, 1957,29,1702-1706.
    [77]Klari'c, I., Roje, U., Kova'ci'c, T.. Kinetics of isothermal thermogravimetrical degradation of PVC/ABS blends. Journal of Thermal Analysis and Calorimetry,1995 45,1373-1380.
    [78]Lescop, B., Jay, J.-Ph., Fanjoux, G.. Reduction of oxygen pre-treated Ni by H2 exposure:UPS and MIES studies compared with Monte Carlo simulations. Surface Science,2004,548,83-94.
    [79]Malek, J.. The kinetic analysis of non-isothermal data. Thermochimica Acta,1992,200, 257-269.
    [80]GUNES M, GUNES S. A direct search method for determination of DAEM kinetic parameters from nonisothermal TGA data[J]. Applied Mathematics and Computation, 2002,130(23):619-628.
    [81]HEIDENREICH C A, YAN H M, ZHANG D K. Mathematical modelling of pyrolysis of large coal particles-estimation of kinetic parameters for methane evolution[J]. Fuel, 1999,78(5):557-566.
    [82]J.Bednarek, A.Plonka, B.Pacewska, J.Pysiak, Thermochim.Acta,1996,51,282-283.
    [83]黄希祜.钢铁冶金原理(第三版)M].北京,冶金工业出版社,2007.
    [84]胡荣祖等.热分析动力学[M].西安:科学出版社.2007.
    [85]H.-P. Wiendahl and J. Worbs:Simulation based analysis of complex production systems with methods of non-linear dynamics. Journal of Materials Processing Technology.2003,139,28.
    [86]Saffet Yagiz and Candan Gokceoglu Application of fuzzy inference system and nonlinear regression models for predicting rock brittlencss. Expert Systems with Applications.2010,37,2265.
    [87]Zui-Cha Deng, Liu Yang, Jian-Ning Yu andGuan-Wei Luo:An inverse problem of identifying the coefficient in a nonlinear parabolic equation. Nonlinear Analysis:Theory, Methods & Applications,2009,71,6212.
    [88]D. P. Tao The kinetic models of chemical reaction of fluids on rough surfaces. ACTA METALURGICA SINICA.2010,37,1073.
    [89]D. P. Tao Fractal Pore Diffusion of Fluids in Porous Media. ACTA METALLURGICA SINICA.2000,13,877.
    [90]M. Cross, T.N. Croft, G. Djambazov, K. Pericleous, Computational modelling of bubbles, droplets and particles in metals reduction and refining. Applied Mathematical Modelling,2006,30,1445.
    [91]Mark P, Cross M, Schwarz P Third International Conference on CFD in the Minerals and Process Industries CSTRO, Melbourne, Australia,2003, December:10.
    [92]罗世华、刘祥官,高炉铁水含硅量的分形结构分析,物理学报.2006,55,3343.
    [93]赵敏、刘祥官、郜传厚,高炉铁水含硅量序列的动力学结构分析,物理学报.2008,57,2722.
    [94]王筱留.钢铁冶金学(炼铁部分)[M].北京,冶金工业出版社,2006.
    [95]吕金虎,陆君安,陈上华.混沌时间序列分析及其应用.武汉:武汉大学出版社,2002.
    [96]H. Kantz, T. Schreibcr. Nonlinear time series analysis(Second Edition)[M].Cambridge: Cambridge University Press,2003.
    [97]H. D. L Abarband. Analysis of observed chaotic data[M]. New York:Springer Verlag Press,1996.
    [98]A. S. Weigend, N. A. Gershenfeld. Time series prediction:forecasting the future and understanding the past[M]. New Mexico:Addison-Wesley,1994.
    [99]F.Takens. Detecting strange attractors in fluid turbulence. D. Rand. L. S. Young. Dynamical systems and turbulence. Berlin:Springer,1981:366-381.
    [100]J. R. Eckmann and D. Ruelle. Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems[J]. Physica D,1992,56:185.
    [101]Andrew M. Fraser and Harry L Swinney in Independent coordinates for strange attractors from mutual information. Phys. Rev. A,1986,33,1134.
    [102]J. W. Dippner, R. Heerkloss, J. P. Zbilut. Recurrence quantification analysis as a tool for characterization of non-linear mesocosm dynamics. MARINE ECOLOGY PROGRESS SERIES Mar Ecol Prog Ser 2002,242,29.
    [103]Jorge Belaire-Franch, Dulce Contreras. Recurrence Plots in Nonlinear Time Series Analysis:Free Software, COMMENT 1.2002,1.
    [104]Charles L, Webber, Jr. Recurrence Quantification Analysis of Nonlinear Dynamical Systems COMMENT 1.2005,92.
    [105]杜挺,杜昆.含碳球团-铁浴熔融还原法关键技术的应用基础研究[J].金属学报,1997,33(7):718-727.
    [106]J. L. Chen, M. Wan, J. Zhao, "Metallurgy of non-coking coal", Beijing:Chemical Industry Press,2007.
    [107]M. C. Abraham, A. Ghosh, "Kinetics of reduction of iron oxide by carbon", Ironmaking and steelmaking, vol.6, No.1, pp14-23,1979.
    [108]Tadeusz Trzaskalik, Sebastian Sitarz. Discrete dynamic programming with outcomes in random variable structures. European Journal of Operational Research,2007,177, 1535-1548.
    [109]M. Ataei, B. Lohmann, A. Khaki-Sedigh and C. Lueas. Model based method for estimating an attractor dimension from uni/multivariate chaotic time series with application to Bremen climatic dynamics[J]. Chaos, Solitons&Fractals,2004,19(5): 1131-1139.
    [110]N. Tanaka, H. Okamoto and M. Naito. Estimating the active dimension of the dynamics in a time series based on an information criterion[J]. Physica D,2001, 158(1):19-31.
    [111]R Hegger, H Kantz, T Schrciber. Practical implementation of nonlinear time series methods:The TISEAN package[J]. Chaos,1999,9(2):413-435.
    [112]V. I. Ponomarenko, M. D. Prokhorov. Extracting information masked by the chaotic signal of a time-delay system[J]. Phys. Rev. E,2002,66,3-5.
    [113]S. Heidari, C. L. Nikias. Characterizing chaotic attractors using fourth-order off-diagonal cumulant sliees[A].1993 Conference Record of The Twenty-Seventh Asilomar Conference on Signals Systems and Computers[C]. Pacific Grove(CA, USA):IEEE Computer Society Press.1993,1:466-470.
    [114]Guo-Feng Fan, Shan Qing, Hua Wang. Study on Apparent Kinetic Prediction Model of the Smelting Reduction based on the Time Series, Mathematical Problems in Engineering, v2012,2012.
    [115]X. Y. Zhang, J. L. Li, X. Y. Liu, "Study on the Dehydration Kinetics for the Limonitic", Metal Materials and Metallurgy Engineering vol.38, No.4 pp10-13,2010.
    [116]C. Bryk, W. K. LU, "Reduction phenomena in composites of iron ore concentrates and coals", Ironmaking and steelmaking, vol.13, No.2, pp70-75,1986.
    [117]Dutta S K,Ghost A. Study of nonisothermal reduction of iron ore-coal composite pellet. Metallurgical and Materials Transaction B. vol.25, No.B, pp15,1994.
    [118]D. B. Huang, X. M. Yang, T. J. Yang, et al, "Kinetics and mathematical model for reduction process of iron ore briquette containing carbon", Acta Metallurgica Sinica, vol.32, No.6, pp629-636,1996
    [119]Tao, D. P. The kinetic models of chemical reaction of fluids on rough surfaces. ACTA METALURGICA SINICA.2010,37,1073-1078.
    [120]Ni, W.; Ma, M. S. Thermodynamic and kinetic in recovery of iron from nickel residue. Journal of Beijing University of Science and Technology.2009,31,163-168.
    [121]Yang, Y.; Song, B.; Yang, S. b. Kinetics of vanadium smelting reduction in a CaO-SiO2-Al2O3-MgO-V2O5 slag system. Journal of Beijing University of Science and Technology.2006,28,1115-1120.
    [122]Piotrowski, K. Effect of gas composition on the kinetics of iron oxide reduction in a hydrogen production process. Hydrogen Energy.2005,30,1543-1554.
    [123]W. C. Hong, Y. C. Dong, C. Y. Lai, L. Y. Chen, and S. Y. Wei, "SVR with hybrid chaotic immune algorithm for seasonal load demand forecasting," Energies, 2011,4(6):960-977.
    [124]W. C. Hong, "Hybrid evolutionary algorithms in a SVR-based electric load forecasting model," International Journal of Electrical Power and Energy Syxtems, 2009,31(7-8):409-417.
    [125]P. F. Pai and W. C. Hong, "A recurrent support vector regression model in rainfall forecasting," Hydrological Processes,2007,21(6):819-827.
    [126]P. F. Pai, C. S. Lin, W. C. Hong, and C. T. Chen, "A hybrid support vector machine regression for exchange rate prediction," International Journal of Information and Management Sciences,2006,17(2):19-32.
    [127]S.N. Sivanandam, S.N. Deepa, Introduction to Genetic Algorithms, Springer-Verlag, Berlin, Heidelberg,2008.
    [128]J. Kennedy, R. Eberhart, Particle swarm optimization, in:Proceedings of IEEE International Conference on Neural Networks, vol.4,1995, pp.1942-1948.
    [129]A.P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, John Wiley & Sons, New Jersey,2005.
    [130]M. Dorigo, L.M. Gambardella, Ant Colony System:a cooperative learning approach to the traveling salesman problem, IEEE Transactions on Evolutionary Computation 1 (1997)53-66.
    [131]S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing, Science:New Series 220 (1983) 671-680.
    [132]J. De Vicente, J. Lanchares, R. Hermida, Placement by thermodynamic simulated annealing, Physics Letters A 317 (2003) 415-423
    [133]Meng, Q; Peng, Y. A new local linear prediction model for chaotic time series. Physics Letters A.2007,370,465-470.
    [134]Xie, J. X.; Cheng, C. T. A New Direct Multi-step Ahead Prediction Model Based on EMD and Chaos Analysis. Journal of Automation,2008,34,684-689.
    [135]Guan, W.; Xie, G. W. Regression and time series in the application of power load forecasting. MODERN ENTERKS CULTURE.2008,3,152-153.
    [136]Norden, E.; Huang, M. L. Wu, C. A confidence limit for the empiricalmode decomposition and Hilbert spectral analysis. Proc. R. Soc. Lond. A.2003,459, 2317-2345.
    [137]Huang, N. E.; Shen, Z. A new View of nonliner water waves:The Hilbert spectrum. Rev.Fluid Mech.1999,31,417-457.
    [138]Chen, K..; Li, Y.; L. Chen. EEMD decomposition in power system fault detection. Computer Simulation.2010,3,263-266.
    [139]Cheng, J. S.; Yu, D.; Yang, J. Y. A fault diagnosis approach for roller bearings based on EMD method and AR model. Mechanical Systems and Signal Processing.2006, 12,350-362.
    [140]E, J. Q.; Wang, C. H.; Gong, J. K. Process on measurement data from copper pyrometallurgical heat dynamical system by using of EMD method. The Chinese Journal of Nonferrous Metals.2008,18,946-951.
    [141]Xie, J. X.; Cheng, C. T. A New Direct Multi-step Ahead Prediction Model Based on EMD and Chaos Analysis. Journal of Automation.2008,34,684-689.
    [142]徐匡迪.徐匡迪文选(钢铁冶金卷).上海:上海大学出版社.2005.
    [143]Gao, J. Asymptotic properties of some estimators for partly linear stationary autoregressive models. Commun. Statist.-Theory and Methods.1995,24, 2011-2026.
    [144]Li, H.B.; Wang H., Qi, Y. L.; Hu, J.H.; Li, Y. L. Ilmenite Smelted by Oxygen-Enriched Top-Blown Smelting Reduction. JOURNAL OF IRON AND STEEL RESEARCH, INTERNFXIONAL.2011,18,07-13
    [145][Nello Cristianini, John Shawe-Taylor著.李玉增译.,支持向量机导论[M].北京:电子工业出版社,2004
    [146]Vapnik著.张学工译.,统计学习理论[M].北京:电子工业出版社,2004.
    [147]Bernard, J. T.; Bolduc, D.; Yameogo, N. D.; Rahman, S. A pseudo-panel data model of household electricity demand. Resource and Energy Economics.2010,33,315-325.
    [148]Bianco, V.; Manca, O.; Nardini, S. Electricity consumption forecasting in Italy using linear regression models. Energy.2009,34,1413-1421.
    [149]Zhou, P.; Ang, B. W.; Poh, K. L. A trigonometric grey prediction approach to forecasting electricity demand. Energy.2006,31,2839-2847.
    [150]Afshar, K.; Bigdeli, N. Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA). Energy.2011,36,2620-2627.
    [151]Kumar, U.; Jain, V. K. Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India. Energy.2010,35,1709-1716.
    [152]Topalli, A. K.; Erkmen, I. A hybrid learning for neural networks applied to short term load forecasting. Neurocomputing.2003,51,495-500.
    [153]Kandil, N.; Wamkeue, R.; Saad, M.; Georges, S. An efficient approach for short term load forecasting using artificial neural networks. International Journal of Electrical Power and Energy Systems.2006,28,525-530.
    [154]Beccali, M.; Cellura, M.; Brano, V. L.; Marvuglia, A. Forecasting daily urban electric load profiles using artificial neural networks. Energy Conversion and Management. 2004,45,2879-2900.
    [155]Qiu-Ju Li, Xin Hong, A non-isothermal kinetics model for reduction of ferrous oxide with hydrogen and carbon monoxide[J], ironmaking and steelmaking,2009,36:24-28.
    [156]Qiu-Ju Li, Xin Hong, Mathematical Simulation on Reduction of Fine Iron Oxide at Low Temperature[J], Mineral Processing and Extractive Metallurgy, 2008,117:209-213.
    [157]Yue Zhengchao, Qing Shan, Wang Hua. Numerical Modelling of A Slag-Metal Behavior of Smelting Reduction Process:Ironmaking Technology Based on the Hismelt[J]. ICEMI 2009-Proceedings of 9th International Conference on Electronic Measurement and Instruments,2009:4482-4485.
    [158]Jingyu Shi, E. Donskoi, D. L. S. Mcelwain, Modelling the Reduction of an Iron Ore-Coal Composite Pellet with Conduction and Convection in an Axisymmetric Temperature Field[J], Mathematical and Computer Modelling,2005,42:45-60.
    [159]W. Ni, M. S. Ma, Y. L. Wang, Z. J. Wang, and F. M. Liu. Thermodynamic and kinetic in recoveryof iron from nickel residue[J]. Journal of Beijing University of Science and Technology,2009,31 (2):163-168.
    [160]Y. Yang, B. Song, S. Yang, Y. Wen, and W. He. Kinetics of vanadium smelting reduction in a CaOSiO2-Al2O3-MgO-V2O5 slag system[J]. Journal of University of Science and Technology Beijing,2006,28(12):1115-1120.
    [161]K. Piotrowski, K. Mondal, H. Lorethova, L. Stonawski, T. Szymanski, and T. Wiltowski, Effect of gas composition on the kinetics of iron oxide reduction in a hydrogen production proecss[J]. International Journal of Hydrogen Energy,2005, 30(15):1543-1554.
    [162]K.ang H.W., Chung W.S., Review of applicability of unreacted core model based on Ishida-Wen model [J], Ironmaking and steelmaking,2004,31:117-124.
    [163]Sohn I., Fruehan R.J., The reduction of iron oxides by volatiles in a rotary hearth furnace process:part I. the role and kinetics of volatile reduction[J], Metallurgical and transactions,2005:36B,605-612.
    [164]Han-Taw Chen, Kuo-Chi Liu, Effect of the potential field on non-Fickian diffusion problems in a sphere[J], International Journal of Heat and Mass Transfer,2003, 150:31-42.
    [165]Huang H.Z., Shen Z, Long S.R., et al,The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis[J], Proc R soc Lond A, 1998,454:903-995.
    [166]Loh C. H., Application of the empirical mode decomposition Hilbert spectrum method to identify near-fault ground-motion characteristics and structural resonses[J], Bulletin of the Seismological Society of America,2001,91:1339-1357.
    [167]Echeverria J. C., Application of the empirical mode decomposition to heart rate variability analysis[J], Medical & Biological Engineering&Computing,2001, 39:471-479.
    [168]Bradshaw A. V., Matyas A.G., Structual change and kinetics in the gaseous reduction of hematite[J]. Metallurgical transactions B,1976,7B:81-87.
    [169]Dutta S K,Ghost A. Study of nonisothermal reduction of iron ore-coal composite pellet[J]. Metallurgical and Materials Transaction B.1994,25(B):15-25.
    [170]D. B. Huang, X. M. Yang, T. J. Yang, et al. Kinetics and mathematical model for reduction process of iron ore briquette containing carbon[J]. Acta Metallurgica Sinica, 1996,32(6):629-636.
    [171]Q. Wang, Z. Yang, J. Tian, et al. Mechanisms of reduction in iron ore-coal composite pcllct[J]. Ironmaking and steelmaking,1997,24(6):457-460.
    [172]L. J. Fan, Q. G. Lv, Y. J. Na. Thermo-gravimetric analysis for direct reduction of iron ore powder by coal[J]. CIESC Journal,2010,61(12):3228-3233.
    [173]Ziya Aslanoglu. Direct reduction of mechanically activated specular iron oxide[J]. Mineral Processing and Extractive Metallurgy (Trans. Inst. Min. Metall. C),2005,114: 240-244.
    [174]Nasr M. I., Omar A. A., Hessien M. M., Carbon monoxide reduction and accompanying swelling of iron oxide compacts[J]. ISIJ International,1996,36: 164-171.
    [175]EI-Geassy A. A., Nasr M.I., Influence of original Structure on the kinetics and mechanisms of carbon monoxide reduction of hematite compacts[J]. ISIJ Inernational, 30(1990):417-425.
    [176]Bonalde A., Henriquez A., Kinetics analysis of the iron oxide reduction using hydrogen-carbon monoxide mixtures as reducing agent[J]. ISIJ International 2005,45:1225-1260.