炼铁流程中铁矿石评价体系构建
|
摘要
目前,国内多数的钢铁企业大量依赖铁矿石的进口,个别企业的进口矿比例甚至达到了80%以上。从2004年开始,进口矿的价格开始飙升;2009年,达到了惊人的150美元/吨。5年的时间,价格增长了5倍多。2004年后,每次铁矿石价格谈判过程都十分艰难。由于我国迫于铁矿石的需求和维持钢铁厂正常生产经营的压力,谈判往往处于被动。即使价格攀升,也只能无奈接受。昂贵的进口矿使得国内的钢铁企业积极的利用国内便宜的低品位矿和含有害元素多的矿石,一些含铁的工业物料和废料也被广泛的利用。混合料的化学成分波动频繁,原料的不稳定性给制粒和烧结带来很大困难。在这种情况下,开发一个全面的、准确的铁矿石评价体系,对矿石在全炼铁流程中表现进行评价,并提出优化配料方案,无疑是每个企业所亟需的。
本文围绕铁矿石评价体系这一研究目标,按照工艺流程,分别把铁矿石在混合料制粒、烧结、炼铁中的表现进行了理论计算和实验研究。
针对混合料制粒过程中加水量的优化问题,提出了湿容量的概念,并开发出了测试设备和测量方法。对数种铁矿粉湿容量的测量和分析表明:随着矿物粒度的减小,湿容量逐渐增大。基于影响矿物湿容量的因素,建立了表征矿物湿容量的数学模型。湿容量的无孔模型合理地解释了矿物的湿容量随颗粒尺寸减小而增加的现象。湿容量的有孔模型考虑了矿石颗粒表面孔隙对于矿物吸水能力的影响,并理论上推导了开孔和闭孔的差别,理论计算表明闭孔对于湿容量的影响很小,可以忽略。通过实验数据的回归,得到了以矿石的比表面积、孔容、堆密度和真密度之差为自变量的表达式。基于矿物的吸水特性曲线,建立了宏观和微观动力学模型。宏观动力学模型研究表明铁矿石的吸水过程符合Lagergren一阶动力学方程,并得到了水在不同矿物中的传质系数。研究还发现大颗粒矿物的吸水速率比小颗粒矿物大。矿物的微观动力学模型基于水在颗粒间传输时的受力分析。颗粒间的空隙尺寸和颗粒表面的闭孔体积是影响矿物吸水动力学的主要因素,其中空隙尺寸是主要因素。混料实验表明:湿容量(x)和铁矿粉最佳配水量(y)具有很好的线性关系。对于本研究所涉及的制粒系统而言,这个关系为y = 6 .94+0.12x。说明湿容量越大,料层得到最佳透气性时所需要的最佳配水量也应该越大。
通过人工神经网络研究了制粒效果评价指标及其影响因素,采用三层BP神经网络结构,确定了模型各层节点数、激励函数、训练函数、训练次数等网络参数,最终建立了基于矿物湿容量和实际加水量的多输入单输出的粒度和透气性预测模型,预测效果在样本趋势上取得较好吻合,精度基本在可接受范围内,可以指导制粒实验及实际生产。
采用FACTSage软件对铁矿粉烧结过程中的物相变化、液相量、热效应与温度的关系进行了理论计算,并利用多种实验方法对计算结果进行了验证。结果表明:FACTSage计算得到的矿物的物相变化规律、液相量与温度的关系与实验基本吻合,可以通过理论计算对铁矿粉的烧结进行优化。由于对矿物的原始物相缺乏准确的表征,FACTSage计算得到的理论热效应和实际差热分析得到的数据数量级一致,但次序吻合较差。
采用正交实验的方法对影响烧结矿各项指标的因素进行了考察,对实验数据的极差分析表明,配碳量对烧结矿的物理性能和技术指标影响最大;随着配碳量的增加,烧结速度、利用系数以及烧结强度均有不同程度改善;碱度升高对改善烧结强度也有促进作用。以正交试验的结果为基础,采用BP神经网络建立了烧结矿性能的预测模型。并对各烧结矿性能预测子模型的结构及参数进行了优化。经过检验,在误差范围内,还原度和利用系数的预测命中率可以达到75%以上,落下强度、转鼓强度、烧结速度的预测均命中率则达到87.5%以上,且预测趋势吻合,模型能够指导烧结实验及生产。
针对烧结过程的配料优化而言,对于关系简单、规模较小的模型,线性规划方便易用,求解效率高。对于大规模复杂问题,当约束条件的重要程度不同时,遗传算法能灵活有效地解决问题。随着变量和约束条件继续增多,模型规模和复杂度的增大,遗传算法能够满足高性能求解优化模型的要求,并且其独有的惩罚函数可以灵活地处理各种约束条件,通过控制惩罚度的大小对约束条件划分优先顺序,使配料过程的优化模型求解更符合烧结操作者的意愿,实现配料的人工智能。
为实现对烧结矿矿相的准确表征,采用图像处理技术对烧结矿的灰度值计算、灰度直方图的分布特征、矿相的纹理特征等内容进行了研究。其中,矿物的反射率计算模型合理、准确。矿相的灰度正态分布模型与实际矿物的灰度分布特征吻合,利用该模型统计得到了常见矿物的正态分布参数;并结合遗传算法,实现了矿物含量的智能计算。基于灰度共生矩阵的图像特征提取方法,研究了灰度共生矩阵的参数如灰度级数、图像窗口尺寸、共生距离和共生角度等与矿物纹理结构的关联性。并利用该方法和兰氏空间距离实现了对矿相的识别。在上述模型的基础上开发出了智能矿相识别处理软件。
综上所述,本文对铁矿石在每个工艺环节中的行为都进行了定量的评价,并且采用计算机编程语言和数据库技术开发出了炼铁体系铁矿石评价软件。该软件已经在工业过程中得到应用,效果良好。
There are many Iron and Steel companies in China which depend on the iron ores imported greatly. In some companies, the iron ore imported from abroad takes up even more than 80%. The price of the iron ores imported increased quickly because of the huge amount of demand in China, especially after 2004. The price increased from less than 30 to about 150 dollar per ton, meaning about 5 times as that 5 years ago. The Chinese association for steel negotiated about the price of iron each year with the iron ore providers from abroad since 1981. The negotiation is very hard after 2004. In every negotiation, the Chinese iron and steel companies are in the dock, because they need a huge of iron ores and cannot shut down due to the society pressure. Always, they cannot help but accept the high price. The high price of the iron ore made the iron ore incorporations use some low grade ores from domestic and some waste materials containing Fe to cut down the cost of raw materials. The chemical composition always vary a lot in the industry production. Therefore, the control of the process during granulation, sintering, and the blast furnace become difficult. Under this conditions, the system which can evaluate the iron ores in the whole process is very necessary for the iron and steel company in China.
In present study, the iron making process is divided into granulation, sintering, and blast furnace process, and the behavior of the iron ore in each of the procedure were studied through the theoretical and experimental methods.
The conception of moisture capacity, equipments, and measurement method was suggested for the optimization in the granulation process. The measurements indicated that the moisture capacity increases with the decrease of particle size. The mathematical models were developed for the prediction of the moisture capacity. The no-pore model give a good explanation of the phenomenon that the moisture capacity increase with decreasing the particle size. The pore model consider the effect of the pore size on the ability of water absorption, and calculate the difference between the open pore and closed pore. The calculations indicated that the closed pore has little effect on the moisture capacity. The equation was got by fitting the measurement that the moisture capacity is expressed by surface area per unit mass, pore volume, bulk density and the real density. The macro and micro kinetic models were developed based on the water absorption curves. The macro kinetic model indicated that the water absorption into the iron ore particles agree with the first order Lagergren kinetic equation, and got the mass transfer coefficients of the water in the iron ores. The micro kinetic model was based on the force analysis of the water in the particles. The calculations indicated that the size of space between the particles and the closed pore volume in the particle surface are the main factors for the kinetic. The granulation experiments with the rotating cylinder indicated that the optimal water content for the granulation increases with increasing the moisture capacity, and they obey a good linear relationship. For the system used in this study, the equation is y = 6 .94+0.12x.
The artificial neural network was used to build the prediction model of the granulation results. The model used three layer BP structure. The number of nodes in the three layer, activation function, training function, training times were optimized. Finally built the multi-input and one output model with moisture capacity and water content added. The tendency predicted agrees well with the measurements, and the prediction accuracy is accepted. The models can be used to improve the industrial production.
On the physical and chemical behaviors of the ore in the sintering, the phase transformation, mass of liquid, thermal effect with the temperature were calculated with FACTSage, and the results were validated by various experimental data. It was indicated that the calculation by FACTSage agreed well with the measurements. This calculation can be used to optimize the sintering parameters under various raw materials conditions. The thermal effect calculated have the same scale, but had a great deviation with the measurements. The probable reason is lack of right characterization of the chemical composition.
As for the influence of burden on the sintering, the orthogonal method was used to check the effect of various factors and levels. The results showed that the coal dosage the first factor influencing the sinter properties and technical index of the sintering. The sintering velocity, utilization coefficient, strength of the sinter were all improved with increasing the coal dosage. The increase of basicity will lead the improvement. The prediction model of the sintering was built with the artificial neural network and the measurements. The parameters in the model were all optimized. The validations showed that the hit rate can reach >75% for the reduction ratio and utilization coefficient in the error limit, and that of tumbler index, sintering velocity reach 87.5%. The tendency predicted agree well with the measurements, and the models can be used in industry.
The optimization model for the burden based on the physical and chemistry behavior in the sintering process was developed. However, the model is complex and huge, therefore, the linear program can not solve the model. The comparison among the many methods showed that the genetic algorithm can satisfied the demand of solution. On the importance of the restrictions, the punishments function in the algorithm can deal with this importance by adjust the punishments degree. This method can get the solution to agree with the operator’s goal, realizing the intelligent burden.
The mineralogy recognition and analysis were also studied. The reflective power model, mineralogy qualification model, and the texture features extraction mode were all developed based on the digital image processing techniques. The reflective power model is simple but reasonable, and the results are accuracy. The Gauss model of the gray distribution for the mineralogy agree with the feature of sinter. The distribution parameters were calculated by the model. The intelligent qualification of mineralogy was realized by combing the Gauss model and genetic algorithm. The texture feature extractive method based on the gray level co-existence matrix can get the features of various mineralogy exactly. The machine recognition of the mineralogy can be realized with canberra space distance. Finally the intelligent software was developed with the models above.
In summary, the behaviors of the iron ore in the iron making were qualified in the sub-section, and finally developed the evaluation system. The software was developed with C# program language and SQL database. The software has been used in the plant, and got a good application.
本文围绕铁矿石评价体系这一研究目标,按照工艺流程,分别把铁矿石在混合料制粒、烧结、炼铁中的表现进行了理论计算和实验研究。
针对混合料制粒过程中加水量的优化问题,提出了湿容量的概念,并开发出了测试设备和测量方法。对数种铁矿粉湿容量的测量和分析表明:随着矿物粒度的减小,湿容量逐渐增大。基于影响矿物湿容量的因素,建立了表征矿物湿容量的数学模型。湿容量的无孔模型合理地解释了矿物的湿容量随颗粒尺寸减小而增加的现象。湿容量的有孔模型考虑了矿石颗粒表面孔隙对于矿物吸水能力的影响,并理论上推导了开孔和闭孔的差别,理论计算表明闭孔对于湿容量的影响很小,可以忽略。通过实验数据的回归,得到了以矿石的比表面积、孔容、堆密度和真密度之差为自变量的表达式。基于矿物的吸水特性曲线,建立了宏观和微观动力学模型。宏观动力学模型研究表明铁矿石的吸水过程符合Lagergren一阶动力学方程,并得到了水在不同矿物中的传质系数。研究还发现大颗粒矿物的吸水速率比小颗粒矿物大。矿物的微观动力学模型基于水在颗粒间传输时的受力分析。颗粒间的空隙尺寸和颗粒表面的闭孔体积是影响矿物吸水动力学的主要因素,其中空隙尺寸是主要因素。混料实验表明:湿容量(x)和铁矿粉最佳配水量(y)具有很好的线性关系。对于本研究所涉及的制粒系统而言,这个关系为y = 6 .94+0.12x。说明湿容量越大,料层得到最佳透气性时所需要的最佳配水量也应该越大。
通过人工神经网络研究了制粒效果评价指标及其影响因素,采用三层BP神经网络结构,确定了模型各层节点数、激励函数、训练函数、训练次数等网络参数,最终建立了基于矿物湿容量和实际加水量的多输入单输出的粒度和透气性预测模型,预测效果在样本趋势上取得较好吻合,精度基本在可接受范围内,可以指导制粒实验及实际生产。
采用FACTSage软件对铁矿粉烧结过程中的物相变化、液相量、热效应与温度的关系进行了理论计算,并利用多种实验方法对计算结果进行了验证。结果表明:FACTSage计算得到的矿物的物相变化规律、液相量与温度的关系与实验基本吻合,可以通过理论计算对铁矿粉的烧结进行优化。由于对矿物的原始物相缺乏准确的表征,FACTSage计算得到的理论热效应和实际差热分析得到的数据数量级一致,但次序吻合较差。
采用正交实验的方法对影响烧结矿各项指标的因素进行了考察,对实验数据的极差分析表明,配碳量对烧结矿的物理性能和技术指标影响最大;随着配碳量的增加,烧结速度、利用系数以及烧结强度均有不同程度改善;碱度升高对改善烧结强度也有促进作用。以正交试验的结果为基础,采用BP神经网络建立了烧结矿性能的预测模型。并对各烧结矿性能预测子模型的结构及参数进行了优化。经过检验,在误差范围内,还原度和利用系数的预测命中率可以达到75%以上,落下强度、转鼓强度、烧结速度的预测均命中率则达到87.5%以上,且预测趋势吻合,模型能够指导烧结实验及生产。
针对烧结过程的配料优化而言,对于关系简单、规模较小的模型,线性规划方便易用,求解效率高。对于大规模复杂问题,当约束条件的重要程度不同时,遗传算法能灵活有效地解决问题。随着变量和约束条件继续增多,模型规模和复杂度的增大,遗传算法能够满足高性能求解优化模型的要求,并且其独有的惩罚函数可以灵活地处理各种约束条件,通过控制惩罚度的大小对约束条件划分优先顺序,使配料过程的优化模型求解更符合烧结操作者的意愿,实现配料的人工智能。
为实现对烧结矿矿相的准确表征,采用图像处理技术对烧结矿的灰度值计算、灰度直方图的分布特征、矿相的纹理特征等内容进行了研究。其中,矿物的反射率计算模型合理、准确。矿相的灰度正态分布模型与实际矿物的灰度分布特征吻合,利用该模型统计得到了常见矿物的正态分布参数;并结合遗传算法,实现了矿物含量的智能计算。基于灰度共生矩阵的图像特征提取方法,研究了灰度共生矩阵的参数如灰度级数、图像窗口尺寸、共生距离和共生角度等与矿物纹理结构的关联性。并利用该方法和兰氏空间距离实现了对矿相的识别。在上述模型的基础上开发出了智能矿相识别处理软件。
综上所述,本文对铁矿石在每个工艺环节中的行为都进行了定量的评价,并且采用计算机编程语言和数据库技术开发出了炼铁体系铁矿石评价软件。该软件已经在工业过程中得到应用,效果良好。
There are many Iron and Steel companies in China which depend on the iron ores imported greatly. In some companies, the iron ore imported from abroad takes up even more than 80%. The price of the iron ores imported increased quickly because of the huge amount of demand in China, especially after 2004. The price increased from less than 30 to about 150 dollar per ton, meaning about 5 times as that 5 years ago. The Chinese association for steel negotiated about the price of iron each year with the iron ore providers from abroad since 1981. The negotiation is very hard after 2004. In every negotiation, the Chinese iron and steel companies are in the dock, because they need a huge of iron ores and cannot shut down due to the society pressure. Always, they cannot help but accept the high price. The high price of the iron ore made the iron ore incorporations use some low grade ores from domestic and some waste materials containing Fe to cut down the cost of raw materials. The chemical composition always vary a lot in the industry production. Therefore, the control of the process during granulation, sintering, and the blast furnace become difficult. Under this conditions, the system which can evaluate the iron ores in the whole process is very necessary for the iron and steel company in China.
In present study, the iron making process is divided into granulation, sintering, and blast furnace process, and the behavior of the iron ore in each of the procedure were studied through the theoretical and experimental methods.
The conception of moisture capacity, equipments, and measurement method was suggested for the optimization in the granulation process. The measurements indicated that the moisture capacity increases with the decrease of particle size. The mathematical models were developed for the prediction of the moisture capacity. The no-pore model give a good explanation of the phenomenon that the moisture capacity increase with decreasing the particle size. The pore model consider the effect of the pore size on the ability of water absorption, and calculate the difference between the open pore and closed pore. The calculations indicated that the closed pore has little effect on the moisture capacity. The equation was got by fitting the measurement that the moisture capacity is expressed by surface area per unit mass, pore volume, bulk density and the real density. The macro and micro kinetic models were developed based on the water absorption curves. The macro kinetic model indicated that the water absorption into the iron ore particles agree with the first order Lagergren kinetic equation, and got the mass transfer coefficients of the water in the iron ores. The micro kinetic model was based on the force analysis of the water in the particles. The calculations indicated that the size of space between the particles and the closed pore volume in the particle surface are the main factors for the kinetic. The granulation experiments with the rotating cylinder indicated that the optimal water content for the granulation increases with increasing the moisture capacity, and they obey a good linear relationship. For the system used in this study, the equation is y = 6 .94+0.12x.
The artificial neural network was used to build the prediction model of the granulation results. The model used three layer BP structure. The number of nodes in the three layer, activation function, training function, training times were optimized. Finally built the multi-input and one output model with moisture capacity and water content added. The tendency predicted agrees well with the measurements, and the prediction accuracy is accepted. The models can be used to improve the industrial production.
On the physical and chemical behaviors of the ore in the sintering, the phase transformation, mass of liquid, thermal effect with the temperature were calculated with FACTSage, and the results were validated by various experimental data. It was indicated that the calculation by FACTSage agreed well with the measurements. This calculation can be used to optimize the sintering parameters under various raw materials conditions. The thermal effect calculated have the same scale, but had a great deviation with the measurements. The probable reason is lack of right characterization of the chemical composition.
As for the influence of burden on the sintering, the orthogonal method was used to check the effect of various factors and levels. The results showed that the coal dosage the first factor influencing the sinter properties and technical index of the sintering. The sintering velocity, utilization coefficient, strength of the sinter were all improved with increasing the coal dosage. The increase of basicity will lead the improvement. The prediction model of the sintering was built with the artificial neural network and the measurements. The parameters in the model were all optimized. The validations showed that the hit rate can reach >75% for the reduction ratio and utilization coefficient in the error limit, and that of tumbler index, sintering velocity reach 87.5%. The tendency predicted agree well with the measurements, and the models can be used in industry.
The optimization model for the burden based on the physical and chemistry behavior in the sintering process was developed. However, the model is complex and huge, therefore, the linear program can not solve the model. The comparison among the many methods showed that the genetic algorithm can satisfied the demand of solution. On the importance of the restrictions, the punishments function in the algorithm can deal with this importance by adjust the punishments degree. This method can get the solution to agree with the operator’s goal, realizing the intelligent burden.
The mineralogy recognition and analysis were also studied. The reflective power model, mineralogy qualification model, and the texture features extraction mode were all developed based on the digital image processing techniques. The reflective power model is simple but reasonable, and the results are accuracy. The Gauss model of the gray distribution for the mineralogy agree with the feature of sinter. The distribution parameters were calculated by the model. The intelligent qualification of mineralogy was realized by combing the Gauss model and genetic algorithm. The texture feature extractive method based on the gray level co-existence matrix can get the features of various mineralogy exactly. The machine recognition of the mineralogy can be realized with canberra space distance. Finally the intelligent software was developed with the models above.
In summary, the behaviors of the iron ore in the iron making were qualified in the sub-section, and finally developed the evaluation system. The software was developed with C# program language and SQL database. The software has been used in the plant, and got a good application.
引文
[1] Ball, D. F., J. Dartnell, J. Davison, A Grieve, R. Wild. Agglomeration of iron ores[M]. New York: American Elsevier Publishing Company, 1973.
[2]王悦祥.烧结矿与球团矿生产[M].北京:冶金工业出版社, 2006.
[3]文光远.铁冶金学[M].重庆:重庆大学出版社, 1993.
[4]王筱留.钢铁冶金学(炼铁部分)[M].北京:冶金工业出版社, 2000.
[5] Kapur, P. C. The crushing layering mechanism of granule growth[J]. Chemical Engineering Science, 1971, 26 (7):1093-1099.
[6] Kapur, P. C. . kinetics of granulation by non-random coalescense mechanism[J]. Chemical engineering Sciense, 1972, 27:1863-1869.
[7] Ouchiyama, Norio, Tatsuo Tanaka. mathematical model in the kinetic of granulation[J]. Indian Engineering Chemistry Process Design and Development, 1974, 13 (4):383-388.
[8] Pulvermacher, B., E. Ruckenstein. time evolution of the size spectrum in granulation[J]. Chemical engineering journal, 1975, 9:21-29.
[9] Cross, M. Mathmatical model of rotary kilns used in the produiction [J]. Ironmaking and Steelmaking, 1976, 3 (1):129-137.
[10] Cross, M. . Mathematical model of balling-drum circiut of a pelletizing plant[J]. Ironmaking and Steelmaking, 1977, 4 (3):159-169.
[11] Furui, Takeo. Technology for Preparation of Raw Materials to Be Sintered[J]. Nippon steel technical report, 1977, (10):36-46.
[12] Kapur, P C. kinetic of green pellet groth by the layering mechanism[J]. 1977,
[13] Kapur, P. C. . Balling and granulation[M], 1978.
[14] Young, R M. Mathematical model of grate-lilin-clooer process used for induration of iron ore pellets[J]. Ironmaking and Steelmaking, 1979, 6 (1):1-13.
[15] Ouchiyama, Norlo, Tatsuo Tanaka. estimation of the average number of contact between randomly mixed solid particle[J]. Indian Engineering Chemistry Fundemental, 1980, 19:338-340.
[16] Errigo, V., M. Pinti, C. Pignatelli. 1981. A New Method for Evaluating Particulate Material Granulation. In 3rd International Symposium on Agglomeration, 23-24.
[17] Ouchiyama, Norlo, Tatsuo Tanaka. kinetic analysis of continous pan granulation[J]. Indian Engineering Chemistry Process Design and Development, 1981, 20:340-348.
[18] Roller, P. W., B. A. Firth. Granulation of Iron Ore Sinter Feeds[J]. BHP technical bulletin,1981, 25 (1):79-84.
[19] Suzuki, M., K. Makino, M. Yamada. A study on the coordination number in a system of randonly packed uniform-sized spherical particle[J]. International chemical engineering, 1981, 21 (3):482-488.
[20] Roller, R. W. Granulation of Iron Ores[J]. BHP technical bulletin, 1982, 26 (1):44-45.
[21] Suzuki, Michitaka, Toshio Oshima. Estimation of the co-ordination number in a multi-component mixture of sphere[J]. Powder technology, 1983, 42:841-852.
[22] Suziki, M, T. Oshima. Co-ordination number of a multi-component randomly packed bed of sphere with size distribution[J]. Powder technology, 1985, 44:213-218.
[23] Kuriyama, K., S. Sato, T. Kawaguchi. Development of Iron Ore and Limestone Pre-Granulation Method for Sintering Process. IV. Properties of Iron Ore and Limestone Pre-Granulated Sinter[J]. Transactions ISIJ, 1986, 26 (8):B262-B264.
[24] Hounslow, M J. A discretized population balance for nucleation growth and aggregation[J]. AIChE Journal, 1988, 34 (11):1821-1832.
[25] Litster, J. D., A G Waters. Influence of the material properties of iron ore sinter feed on granulation effectiveness[J]. Powder technology, 1988, 55:141-151.
[26] Sahoo, K. C., D. K. Lahiri, V. Sundermurthy, S. Barpanda. Effect of Ultrafines on Sinter Quality[C].3rd International Symposium on Beneficiation and Agglomeration, 1991:421-433.
[27] Barnaba, P., A. Scarton, G. Quaranta. Control of chemical and physical characteristics of sintering mix to improve productivity and sinter quality[C]. Sixth International Symposium on Agglomeration, 1993:441-449.
[28] Kapur, P.C. An auto-layering model for the granulation of iron ore fines[J]. International of mineral processing, 1993, 39 (3-4):239-250.
[29] Kasai, E., Y. Omori. Subjects on the Evaluation of the Process and Products in the Sintering of Iron Ore[J]. Tetsu to Hagane, 1993, 79 (11):1217-1223.
[30] Migita, M., S. Mochizuki, T. Deno, K. Ando, J. Kitayama, Y. Hida. Realization of self-densification sintering process on Oita No. 1 sintering machine[C]. Sixth International Symposium on Agglomeration, 1993:279-284.
[31] Adetayo, A A, B J Ennis. Unifying approach to modeling granule coalecense mechanism[J]. AIChE Journal, 1997, 43 (4):927-934.
[32] Haga, T., A. Ohshio, Y. Hida, H. Fukuda, N. Ogata. Improvement of the melting reaction in sintering process by the fine part selective granulation of clayish iron ores[J]. Tetsu to Hagane, 1997, 83 (4):233-238.
[33] Venkataramana, R., S. S. Gupta, P. C. Kapur, N. Ramachandran. Modeling of iron ore sinterfeed granulation[J]. Tata Search, 1997:66-72.
[34] Chatterjee, Amit. Role of particle size in mineral processing at Tata Steel[J]. International of mineral processing, 1998, 53:1-14.
[35] Dawson, P. R. Recent Developments in Iron Ore Sintering. IV. The Sintering Process[J]. Ironmaking and Steelmaking, 1993, 20 (2):150-159.
[36] Dawson, P. R. Recent Developments in Iron Ore Sintering. I. Introduction[J]. Ironmaking and Steelmaking, 1993, 20 (2):135-136.
[37] Dawson, P. R. Recent Developments in Iron Ore Sintering. III. Granulation and Strand Feeding[J]. Ironmaking and Steelmaking, 1993, 20 (2):144-149.
[38] Dawson, P. R. Recent Development in Iron Ore Sintering. II. Research Studies on Sintering and Sinter Quality[J]. Ironmaking and Steelmaking, 1993, 20 (2):137-143.
[39] Direct reduced iron[M]. Warrendale, PA: The Iron & Steel Society 1999.
[40] Zhou, Y. S., H. Xu, Y. P. Zhang, H. T. Feng. Review of Energy Saving and Emission Reduction in Iron Making Process[J]. Journal of Iron and Steel Research International, 2009, 16:628-632.
[41] Zhang, Q., L. Guo, X. D. Chen. Analysis of Improving COREX-3000 Competence[J]. Journal of Iron and Steel Research International, 2009, 16:1225-1227.
[42] Wu, S. L., J. Xu, Q. Zhou, S. D. Yang, L. H. Zhang. Characteristics of Gas Flow Distribution in Reduction Shaft Furnace of COREX (R)[J]. Journal of Iron and Steel Research International, 2009, 16:1250-1253.
[43] Goodman, N., R Dry. Hismelt plant ramp-up[J]. Journal of Iron and Steel Research International, 2009, 16:1228-1233.
[44] Chang-Oh, K. The FINEX Process Emerging at New Steel Era[J]. Journal of Iron and Steel Research International, 2009, 16:87-92.
[45] Luo, Guoping, Lishun Yuan, Yanxia Zhao, Jingbo Bai, Yici Wang, Zhizhong Hao. Influence of MgO on mineral composition and microstructure of sinter of Baotou Iron and Steel(Group) Co.[J]. Journal of Inner Mongolia University of Science and Technology, 2008, 27 (01):1-4.
[46] Zhang, Shourong, Han Yin. The trends of ironmaking industry and challenges to chinese blast furnace ironmaking in the 21st centry[J]. Journal of Iron and Steel Research International, 2009, 2009 (16):1-13.
[47]吕学伟,白晨光,邱贵宝,欧阳奇,黄玉明.基于遗传算法的烧结配料综合优化研究[J].钢铁, 2007, 42 (04):12-20.
[48]高炉炼铁能耗与节能分析2008 Available from http://www.chinasie.org.cn/ newmore_tb.asp?id=5713.
[49]吴胜利,陈东峰,赵成显,薛方.基于烧结过程各带迁移规律的烧结终点预报方法[J].北京科技大学学报, 2009, 31 (09):1106-1109.
[50]吴胜利,裴元东,陈辉,彭鹏,杨帆.铁矿粉烧结液相流动性评价[J].北京科技大学学报, 2008, 30 (10):1095-1100.
[51]吴胜利,杜建新,马洪斌,田筠清,许海发.铁矿粉烧结液相流动特性[J].北京科技大学学报, 2005, 27 (03):291-293.
[52]吴胜利,杜建新,马洪斌,张作程,陈辉.铁矿粉烧结粘结相自身强度特性[J].北京科技大学学报, 2005, 27 (02):169-172.
[53]吴胜利,刘宇,杜建新,米坤,林鸿.铁矿石的烧结基础特性之新概念[J].北京科技大学学报, 2002, 24 (03):254-257.
[54] Rumpf. Agglomeration[M]. Pennsylvania: Interscience, 1962.
[55] Schubert, H., W. Herrmanna, H. Rumpfa. Deformation behaviour of agglomerates under tensile stress [J]. Powder technology, 1975, 11 (2):121-131.
[56] Cheng, D. C. H. The effect of secondary flow on the viscosity measurement using the cone-and-plate viscometer [J]. Chemical Engineering Science, 1968, 23 (8):895-899.
[57] Newitt, D. M., J. M. Conway-Jones. A Contribution to the Theory and Practice of Granulation[J]. Transactions of the Institution of Chemical Engineers, 1958, 36:422-440.
[58] Mason, G., W. C. Clark. Liquid bridges between spheres[J]. Chemical Engineering Science, 1965, 20 (10):859-866.
[59] Tigerschoid, M., P. A. Ilmoni. Proc. AIME Blast furnace Coke Oven Raw Materials, 1950:9-18.
[60] Capes, C. E., P. V. Danckwerts. Granule formation by the agglomeration of damp powders. Part I:The mechanism of granule growth[J]. Transactions of the Institution of Chemical Engineers, 1965, 43:T116-T124.
[61] Kapur, P C. Kinetic of green pelletization[J]. Transactions of the American Institute of Mining Engineers, 1964, 229:348-355.
[62] Hulbert, H. M., S. Katz. Some problems in particle technology[J]. Chemical Engineering Science, 1964, 19:555-574.
[63] Randolph, A. D., M. A. Larson. Theory of particulate processes[M]: Acadamic press, 1962.
[64] Sastry, K. V. S., D. W. Fuerstenau. Principles of Agglomerating Particulate Materials by Balling or Granulation [C]. Proceedings of 12th Biennial Conference of the Institute for Briquetting and Agglomeration, 1971:113-124.
[65] Lister, D. H., F. P. Glasser. Phase relations in the system CaO-Al2O3-Iron Oxide[J]. Transactions of the British Ceramic Society, 1967:293-305.
[66] Litster, J., B. Ennis. The science and engineering of granulation process[M]. Netherland: Kluwer academic publishers, 2004.
[67] Waters, A. G., J. D. Litster, S. K. Nicol. A mathematical model for the prediction of granule size distribution for multicomponent sinter feed[J]. ISIJ International, 1989, 29 (4):274-283.
[68] Linkson, P B, J R Glastonbury, G J Duffy. The mechanism of granule growth in wet pelleting[J]. Transaction of institute of chemical Engineering, 1973, 51:251.
[69] Iveson, S. M., S. Holt, S. Biggs. Advancing contact angle of iron ores as a function of their hematite and goethite content: implications for pelletising and sintering[J]. International Journal of Mineral Processing, 2004, 74:1-4.
[70] Iveson, S. M., S. Holt, S Rapmond, C. E. Loo, S. Biggs. The effect of solid-liquid contact angle on the granulation of porous materials: a case study on iron ore granulation[C]. Miami: Advanced Technologies for Particle Processing 1998:127-132.
[71] Iveson, S. M., K. F. Rutherford, S. R. Biggs. Liquid penetration rate into submerged porous particles: theory, experimental validation and implications for iron ore granulation and sintering[J]. Transactions of the Institution of Mining and Metallurgy, Section C, 2001, 110:C133-C143.
[72] Kapur, P. C., S. C. D. Arora, S. V. B. Subbarao. Water-bentonite interaction in balling of iron ores [J]. Chemical Engineering Science, 1973, 28 (8):1535-1540.
[73] Yoshinaga, M., S. Sato, T. Kawaguchi. Fundmental study on granulation of sinter raw material mixture[C]. Sydney, Australia: Australia Japan extractive metallurgy symposium, 1980:145-155.
[74] Khosa, J., J. Manuel. Predicting granulating behaviour of iron ores based on size distribution and composition[J]. ISIJ International, 2007, 47 (7):965-972.
[75]潘建,朱德庆,李建.铁精矿的及原料特性其对成球性能的影响[J].中国科技论文在线. http://www.paper.edu.cn/. 2006,
[76] Maeda, Takayuki, Chieko Fukumoto. Effect of adding moisture and wettability on granulation of iron ore[J]. ISIJ International, 2005, 45 (4):477~484.
[77] Bergstrand, Robin, Jasbir Khosa. The Effect of Marra Mamba Ore Addition on the Granulation Characteristics of Pisolite Based and Hematite Based Sinter Blends[J]. ISIJ International, 2005, 45 (4):492-499.
[78] Kasal, Eiki, J.RANKIN William, F.G. John. The Effect of Raw Mixture Properties on bed permeability during Sintering ANNON[J]. ISIJ International, 1989, 29 (1):33~42.
[79]廖国瑞.烧结原料混匀和制粒技术的研究[J].钢铁研究, 1994, 7 (2):40-44.
[80]黎成家,文剑平.烧结混合机最佳工艺参数选择的研究[J].烧结球团, 1992, 17(4):13-17.
[81]吴力华,陈雪峰.攀钢烧结混合料制粒因素的研究与优化[J].烧结球团, 2006, 31 (1):19-22.
[82]杜炽,何群,石军.强化攀钢烧结混合料制粒的试验研究[J].烧结球团, 1998, 23 (1):36-42.
[83] Knight, Peter. Challenges in granulation technology[J]. Powder technology, 2004, 140:156-162.
[84] Liu, Xiaoyan, E.Specht. Experimental study of the lower and upper angles of repose of granular materials in rotating drums[J]. Powder technology, 2005, 154:125-131.
[85] Forrest, Sarah, John Bridgwater. Flow patterns in granulating systems[J]. Powder technology, 2003, 130:91-96.
[86] Ding, Y. L., R. Forster. Granular motion in rotating drums: bed turnover time and slumping-rolling transition [J]. Powder technology, 2002, 124:18-27.
[87]李炳岳,高斌.对邢钢改善圆筒混合机制粒途径的探讨[J].河北冶金, 2006, (3):20-23.
[88] Venkatara, R., P. C. Kapur, S. S. Gupta. Modelling of granulation by a two-stage auto-layering mechanism in continuous industrial drums.[J]. Chemical Engineering Science, 2002, 57:1685~1693.
[89] Sakamoto, Noboru. Iron ore granulation model supposing the granulation probability estimated from both properties of the ores and their size distributions[J]. ISIJ International, 2002, 42 (8):834~843.
[90] Sosman, R. B., H. E. Merwin. Preliminary report on the system lime: ferric oxide[J]. Journal of Washington acadamic science, 1916, 6:532-536.
[91] Phillips, B., A. Muan. Phase equilibria in the system CaO-iron oxide in air and at 1 atm. O2 pressure[J]. Journal of the American Ceramic Socirty, 1958, 41 (11):445-454.
[92] Phillips, B. , A. Muan. Phase equilibria in the system CaO-iron oxide-SiO2 in air [J]. Journal of the American Ceramic Socirty, 1959, 42 (9):413-423.
[93] Egundebi, G. O., J. A. Whiteman. Ironmaking and Steelmaking, 1989, 16 (6):379-385.
[94] Hida, Y., M. Sasaki, K. Sato, M. Kagawa, N. Soma. Nippon steel technical report, 1987, 35:59-67.
[95] Hsieh, Li-heng, J. A. Whiteman. Effect of oxygen potential on mineral formation in lime-fluxed iron ore sinter[J]. ISIJ International, 1989, 29 (8):625-634.
[96] Heieh, L. H., J. A. Whiteman. Sintering conditions for simulating the formation of mineral phases in industrial iron ore sinter[J]. ISIJ International, 1989, 29 (1):24-32.
[97] Ishikawa, Y , Y Shimomura, M Sasaki, Y Hida, H Toda. Improvement of sinter qualitybased on the mineralogical properties of ores [C]. Atlanta, GA: The 42th Ironmaking Proceedings, 1983:17-29.
[98] Kasai, Eiki, Yoshio Waseda, Maximiano V. Ramos. Thermal analyses of the sintering reactions of iron ores[J]. Tetsu to Hagane, 1997, 83 (9):539-544.
[99] Guo, Xingmin, Yunmao Zhang, Mei Zhang, Huaigong Guo, Haiting Shang, Qiangzhi Li. Study of TG-DSC method on sintering performance of iron ores imported in Laiwu steel[J]. Iron and Steel, 2004, 39 (8):34-37.
[100] Morcos, Riham Michelle, Alexandra Navrotsky. Iron ore sintering characterization by calorimetry and thermal analysis[J]. Journal of thermal analysis and calorimetry, 2009, 96 (2):353-361.
[101]吴胜利,刘宇,杜建新,米坤,林鸿.铁矿粉与CaO同化能力的试验研究[J].北京科技大学学报, 2002, 26 (3):258-261.
[102]吴胜利,许海法,汪国俊,李肇毅,田筠清,陈辉.现代高炉合理使用天然块矿的基础研究[J].北京科技大学学报, 2007, 29 (03):320-324.
[103]吴胜利,汪国俊,姜伟忠,孙金铎,许海法.高炉内天然块矿与烧结矿高温交互反应研究[J].钢铁, 2007, 42 (3):10-13.
[104] Loo, C. E., G. C. Penny, D Witchard. Effective sintering of iron ore blends containing porous ores at low moisture contents[J]. Transaction of Institution mineral and metallurgy (Section C: Mineral processing and extractive metallurgy), 1996, 105:C22-C36.
[105] Loo, C. E., K. T. Wan, V. R. Howes. Mechanical properties of natural and synthetic mineral phases in sinters having varying reduction degradation indices[J]. Ironmaking and Steelmaking, 1988, 15 (6):279-285.
[106] Sasaki, M., Y. Hida. Consideration on the properties of sinter from the point of view of sintering reactions[J]. Tetsu-to-Hagane, 1982, 68 (6):563-571.
[107]金明芳,李光森,姜鑫,沈峰满.铁酸钙熔体对赤铁矿的渗透行为及对烧结强度的影响[J].东北大学学报(自然科学版), 2008, 29 (08):1135-1138.
[108]李光森,金明芳,储满生,李小刚,沈峰满.含氟烧结矿硅酸盐粘结相自身的强度[J].钢铁研究学报, 2008, 20 (1):10-28.
[109]李光森,窦力威,尚策,沈峰满.铁酸钙粘结相自身强度的研究[J].东北大学学报(自然科学版), 2009, 30 (02):225-228.
[110]应自伟,许力贤,姜茂发,沈岩松.铁酸钙的形态对烧结矿抗断裂性能的影响[J].钢铁研究学报, 2006, 18 (02):55-58.
[111] Matsuno, F., T. harada. Changes in mineral phases during the sintering of Fe203-CaO-Si02 system[J]. Transactions of the Iron and Steel Institute of Japan, 1979, 21 (5):318-325.
[112]郭兴敏,张俊,李春增,朱利,赵金龙.准化学平衡条件下MgO对多元铁酸钙生成的影响[J].钢铁, 2009, 44 (04):16-19.
[113]甘勤,何群.影响钒钛烧结矿铁酸钙生成因素的研究[J].烧结球团, 2008, 33 (02):9-14.
[114]刘振林.铁矿石烧结的铁酸钙生成特性研究[J].山东冶金, 2003, 25 (06):46-49.
[115]范晓慧,孟君,陈许玲,庄剑鸣,李英,袁礼顺.铁矿烧结中铁酸钙形成的影响因素[J].中南大学学报(自然科学版), 2008, 39 (6):1125-1131.
[116]李广森,金明芳,姜鑫,沈峰满.烧结矿粘接相的熔化特性[J].东北大学学报(自然科学版), 2008, 29 (5):697-700.
[117] Ying, Z. W., M. F. Jiang, L. X. Xu. Effects of mineral composition and microstructure on crack resistance of sintered ore[J]. Journal of Iron and Steel Research International, 2006, 13 (4):9-12.
[118] Nandy, B., M. K. Chaudhury, J. Paul, D. Bhattacharjee. Sintering Characteristics of Indian Chrome Ore Fines[J]. Metallurgical and Materials Transactions B-Process Metallurgy and Materials Processing Science, 2009, 40 (5):662-675.
[119] Matsumura, M., M. Hoshi, T. Kawaguchi. Improvement of sinter softening property and reducibility by controlling chemical compositions[J]. ISIJ International, 2005, 45 (4):594-602.
[120] Tsukihashi, F., H. Kimura. The effect of MgO and Al-2(3)O (additions on the liquidus of the CaO-SiO)(-FeO)(2)(x) system at various partial pressure of oxygen[J]. Metallurgical and Materials Processing: Principles and Technologies, 2003, 1:851-860.
[121] Kongoli, F., I. McBow, A. Yazawa. Phase relations during sintering of iron ore and fluxing effect of minor components[J]. Metallurgical and Materials Processing: Principles and Technologies, 2003, 1:917-925.
[122] Wu, Shengli, Hongliang Han, Weizhong Jiang, Jinming Zhu, Gensheng Feng, Zuocheng Zhang. MgO interaction mechanism in sinter[J]. Journal of University of Science and Technology Beijing, 2009, 31 (04):428-432.
[123] Zhao, Yanxia, Guoqing Luo, Zhizhong Hao, Jingbo Bai. Effect of MgO on bonding phase properties of sinter of Baotou Iron and Steel(Group) Co.[J]. Journal of Inner Mongolia University of Science and Technology, 2008, 27 (1):5-9.
[124] Jin, Mingfan, Guangsen Li, Xin Jiang, Fengman Shen. Effect of penetration behavior of calcium ferrite melts into hematite on the strength of sinter[J]. Journal of Northeast University (Natural Science), 2008, 29 (8):1135-1138.
[125] Jin, Mingfan, Guangsen Li, Mansheng Chu, Xin Jiang, Fengman Shen. Influence of the melting characteristic of fine ores on the strength of sinter[J]. China Metallurgy, 2008, 18(6):17-20.
[126] Gan, Qin, Qun He, Yongcai Wen. Study on influence of MgO on mineral composition and metallurgical properties of V-bearing titaniferous magnetite sinter[J]. Iron and Steel, 2008, 43 (08):7-11.
[127] Lu, Qing, Fumin Li, Wenshan Wang, Binsheng Hu. Influence of w(MgO) on sinter strength and sintering process of vanadium-titanium magnetite[J]. Research on Iron and Steel, 2007, 35 (01):5-8.
[128] Jiang, Xin, Gangsheng Wu, Guo Wei, Xiaogang Li, Fengman Shen. Effect of MgO on Sintering Process and Metallurgical Properties of Sinter[J]. Iron and Steel, 2006, 41 (03):8-12.
[129] Jiang, Xin, Gangsheng Wu, Mingfan Jin, Fengman Shen. Effect of MgO Content on Softening-Melting Property of Sinter[J]. Journal of Northeast University (Natural Science), 2006, 27 (12):1358-1362.
[130] Zhou, Mingshun, Liwei Zhai, Yanru Li. Laboratory study on reasonable MgO content in the sinter of Anshan iron and steel group co. [J]. Sintering and Pelletizing, 2005, 30 (06):1-4.
[131] Huang, Zhucheng, Zhiguo Han, Jiewang Zhou, Tao Jiang, Xipeng Yang. Influence of MgO on microstructure of fine magnetite concentrate based sinter[J]. Iron and Steel, 2005, 40 (09):16-20.
[132] Yadav, U. S., B. D. Pandey, B. k. Das, D. N. Jena. Influence of magnesia on sintering characteristics of iron ore[J]. Ironamking Steelmaking, 2002, 29 (2):91-95.
[133]吴胜利,韩宏亮,姜伟忠,朱锦明,冯根生,张作程.烧结矿中MgO作用机理[J].北京科技大学学报, 2009, 31 (4):428-432.
[134] Zhang, Yuzhu, Yan Xu, Xinguo Si, Qingjun Zhang, Xuegang Ma. Research on size distribution of sinters with different MgO content[J]. Iron and Steel, 2007, 42 (09):13-15.
[135]甘勤,何庆莉,邓君.钒钛烧结矿适宜FeO含量的研究[J].云南冶金, 2000, 29 (06):19-23.
[136]张金柱,邓海亮,敖万忠,赵跃萍.烧结混合料中SiO_2和FeO含量对烧结矿强度的影响[J].钢铁, 2008, 43 (05):18-21.
[137]蒋大军,何木光,甘勤,何群.高碱度条件下FeO对烧结矿性能的影响[J].中国冶金, 2008, 18 (11):14-21.
[138]袁慧,周明顺,翟立委,李艳茹,任伟,张立国. FeO对烧结矿矿物组成和显微结构影响的研究[J].中国冶金, 2008, 18 (08):6-9.
[139]张胜.烧结矿FeO对其质量的影响[J].烧结球团, 1990, (5):31-34.
[140]庄剑呜,姚锐.烧结矿FeO含量的确定[J].烧结球团, 1997, 22 (6):4-7.
[141]刘竹林.烧结矿FeO含量的影响因素探讨[J].重庆科技学院学报(自然科学版), 2005, 7 (01):8-13.
[142]卢红军,贾春海,成飞.烧结矿中FeO含量的影响因素分析[J].山东冶金, 2007, 29 (02):45-46.
[143]王明阳.影响烧结矿FeO含量的工艺参数最佳配置研究[J].武汉工业大学学报, 1998, 20 (3):50-52.
[144]张军红,沈峰满,谢安国. G-BP算法在烧结矿FeO指标预测中的应用[J].东北大学学报(自然科学版), 2002, 23 (11):1073-1075.
[145]蒋大军.烧结矿FeO含量预报系统开发与应用[J].钢铁, 2006, 41 (09):13-17.
[146]马洛文.基于机尾红外热图像的烧结矿质量检测[J].炼铁, 2005, 24 (9):107-109.
[147]吴晓峰.基于机尾断面红外特征构建烧结过程温度三维趋势图的方法研究[J].冶金自动化, 2007, (2):14-18.
[148]周芳.在线判断烧结矿烧结点的图像识别法[J].自动化与仪表, 2005, (4):7-12.
[149]谢志江,佟莹,欧阳奇.烧结矿FeO含量模糊聚类算法的研究与应用[J].重庆大学学报, 2006, 29 (10):58-61.
[150]姚志超,张延玲,李士琦,周燕.考虑性能的烧结优化配料模型[J].包头钢铁学院学报, 2002, 21 (3):263-267.
[151]陈义华,王开荣,何仁斌.最优化方法[M].重庆:重庆大学出版社, 2003.
[152]贾彦忠,王书瑞,周振庄,李士琦.烧结配料优化利用研究[J].河北冶金, 1995, (3):15-18.
[153]蒋大军,石军.铁矿石的价值评价与线性规划优化配料[J].烧结球团, 1999, 24 (5):44-48.
[154]贾娟鱼,白晨光,赖宏,邱贵宝.烧结矿和入炉矿配料的优化及实现[J].重庆大学学报, 2002, 25 (10):68-71.
[155]周明顺,刘万山,杜鹤贵,黄晓煜.鞍钢烧结优化配矿研究[J].钢铁, 2004, 39 (6):10-13.
[156]张艳允.邯钢烧结配料优化探讨[J].烧结球团, 2002, 27 (2):8-12.
[157]赵晓煜,王福利,杨英华.基于目标规划的多物料配比优化计算[J].烧结球团, 1996, 21 (3):16-20.
[158]马振华.现代应用数学手册(运筹学与最优化理论卷)[M].北京:清华大学出版社, 1998.
[159]雷英杰,张善文. MATLAB遗传算法工具箱及应用[M].西安:西安电子科技大学出版社, 2005.
[160]李智,姚驻斌.免疫遗传算法在烧结矿配料优化中的应用[J].上海金属, 2004, 26 (6):23-29.
[161]李智,姚驻斌,张望兴,贺超武.基于神经网络的混匀配料优化方法[J].钢铁研究,,2000, (4):10-12.
[162]李智,李伟.模拟退火算法在铸造生产配料优化中的应用[J].铸造技术, 2004, (11):816-818.
[163]李智,谢兆鸿,姚驻斌.蚁群算法在原料矿粉混匀优化中的应用[J].矿冶, 2004, (2):75-78.
[164]周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社, 1999.
[165]邓先礼.最优化技术[M].重庆:重庆大学出版社, 1998.
[166]许俊,韩志伟,邹德余.蒙特卡洛优化法在炼焦配煤中的应用[J].燃料与化工, 2001, 32 (1):13-16.
[167]梁中渝,胡林,邓能运,龚文渠.铁矿粉配料烧结经济评价体系的研究[J].烧结球团, 2003, 28 (4):3-7.
[168]郭文军,王福利,李明忠.基于神经网络的烧结矿化学成分超前预报[J].烧结球团, 1997, 22 (5):7-10.
[169]韩庆虹,金永龙,张军红. BP网络在烧结矿质量预测中的初步应用[J].烧结球团, 2005, 30 (1):20-22.
[170]张舒,高为民.人工神经网络在烧结矿指标预测中的应用[J].烧结球团, 2006, 26 (4):6-10.
[171]张军红,谢安国,沈峰满.烧结矿质量预测的BP网络模型[J].鞍山钢铁学院学报, 2001, 24 (6):406-408.
[172]刘怀.用BP网络模型对烧结矿质量预测[J].有色矿山, 1999, (5):41-44.
[173]刘勇,程武山,孙鑫.基于自适应遗传神经网络的烧结矿化学成分超前预报模型[J].烧结球团, 2005, 30 (6):16-19.
[174]范晓慧,曾垂喜,姜涛.铁矿石烧结性能预报模型[J].中南大学学报(自然科学版, 2005, 36 (6):949-954.
[175]王翊,王雪.基于径向基函数神经网络的烧结矿FeO实时预测[J].冶金自动化, 2006, (Supp.):22-25.
[176]谌晓文,邬捷鹏.基于BP神经网络的烧结终点预测模型[J].烧结球团, 2006, 31 (4):21-26.
[177] Lv, Xuewei, Chenguang Bai, Chuanqiang Zhou, Hao Xie. Determination of moisture capacity of iron ore for sintering[C]. Shanghai, China: Proceedings of the 5th international congress on the science and technology of ironmaking, 2009:235-238.
[178] Kondo, Seiichi, Tatsuo Ishikawa, Ikuo Abe. Adsorption Science (In Japanese)[M]. Tokyo: Maruzen Co. Ltd., 1991.
[179] Schramm, L. L. The language of colloid and Interface Science[M]. Washington, DC.: ACS,1993.
[180] Ho, Yuh-shan. Citation review of Lagergren kinetic rate equation on adsorption reactions[J]. Scientometrics, 2004, 59 (1):171-177.
[181] Hornik, K. Cross-validation with active pattern selection for neural-networkclassifiers[J]. IEEE Transactions on Neural Networks, 1998, 9:35-41.
[182] Wu, Shengli, Jianxin Du, Hongbin Ma, Junqing Tian, Haifa Xu. Fluidity of liquid phase in iron ores during sintering[J]. Journal of University of Science and Technology Beijing, 2005, 27 (3):291-293.
[183] Wu, Shengli, Yu Liu, Jianjin Du, Kun Mi, Hong Lin. New concept of iron ore sintering basic characteristics[J]. Journal of University of Science and Technology Beijing, 2002, 24 (3):254-257.
[184]任允芙.钢铁冶金岩相矿相学[M].北京:冶金工业出版社, 1982.
[185]周乐光.工艺矿物学[M].北京:冶金工业出版社, 2007.
[186] Jeulin, D. Quantitative morphology and quality of iron ore sinter[C]. Atlanta: Proceeding of the 42nd ironmaking conference 1983:31-41.
[187] Jeulin, D. Relation between quality and morphology of sinter- texture analysis contribution[J]. Ironmaking Steelmaking, 1983, 10 (4):145-154.
[188] Shibuya, T., H. Yanaka, K. Takemoto. Quantitative determination of sinter structure by image analysis[J]. Transactions ISIJ, 1985, 25:257-260.
[189]冈萨雷斯.数字图像处理[M].北京:电子工业出版社, 2006.
[190]章毓晋.图像处理和分析[M].北京:清华大学出版社, 1999.
[191]梁运行,崔杜武.图像拼接的预处理算法研究[J].西安理工大学学报, 2003, 19 (4):348-351.
[192]周金和,彭福堂.一种有选择的图像灰度化方法[J].计算机工程, 2006, 32 (20):198-200.
[193] Smith, A R. Color gamut transform pairs[J]. Computer Graphics, 1978, 12 (3):12-19.
[194]贾永红.数字图像处理[M].武汉:武汉大学出版社, 2003.
[195] Chang, Jeng-horng, Kuo-Chin Fan, Yang-Lang Chang. Multi-modal gray histogram modeling and decomposition. [J]. Image and Vision Computing, 2002, 20:203~216.
[196]薛景浩,章毓晋,林行刚.低质量图像基于散射图SEM估计的MAP像素聚类方法[J].电子学报, 1999, 27 (7):95-98.
[197] Togan, Vedat, Ayse T. Daloglu. An improved genetic alogrithm with initial population strategy and sel-adaptive menmber grouping[J]. Comput. & Struct., 2008, 86 (11-12):1204-1218.
[2]王悦祥.烧结矿与球团矿生产[M].北京:冶金工业出版社, 2006.
[3]文光远.铁冶金学[M].重庆:重庆大学出版社, 1993.
[4]王筱留.钢铁冶金学(炼铁部分)[M].北京:冶金工业出版社, 2000.
[5] Kapur, P. C. The crushing layering mechanism of granule growth[J]. Chemical Engineering Science, 1971, 26 (7):1093-1099.
[6] Kapur, P. C. . kinetics of granulation by non-random coalescense mechanism[J]. Chemical engineering Sciense, 1972, 27:1863-1869.
[7] Ouchiyama, Norio, Tatsuo Tanaka. mathematical model in the kinetic of granulation[J]. Indian Engineering Chemistry Process Design and Development, 1974, 13 (4):383-388.
[8] Pulvermacher, B., E. Ruckenstein. time evolution of the size spectrum in granulation[J]. Chemical engineering journal, 1975, 9:21-29.
[9] Cross, M. Mathmatical model of rotary kilns used in the produiction [J]. Ironmaking and Steelmaking, 1976, 3 (1):129-137.
[10] Cross, M. . Mathematical model of balling-drum circiut of a pelletizing plant[J]. Ironmaking and Steelmaking, 1977, 4 (3):159-169.
[11] Furui, Takeo. Technology for Preparation of Raw Materials to Be Sintered[J]. Nippon steel technical report, 1977, (10):36-46.
[12] Kapur, P C. kinetic of green pellet groth by the layering mechanism[J]. 1977,
[13] Kapur, P. C. . Balling and granulation[M], 1978.
[14] Young, R M. Mathematical model of grate-lilin-clooer process used for induration of iron ore pellets[J]. Ironmaking and Steelmaking, 1979, 6 (1):1-13.
[15] Ouchiyama, Norlo, Tatsuo Tanaka. estimation of the average number of contact between randomly mixed solid particle[J]. Indian Engineering Chemistry Fundemental, 1980, 19:338-340.
[16] Errigo, V., M. Pinti, C. Pignatelli. 1981. A New Method for Evaluating Particulate Material Granulation. In 3rd International Symposium on Agglomeration, 23-24.
[17] Ouchiyama, Norlo, Tatsuo Tanaka. kinetic analysis of continous pan granulation[J]. Indian Engineering Chemistry Process Design and Development, 1981, 20:340-348.
[18] Roller, P. W., B. A. Firth. Granulation of Iron Ore Sinter Feeds[J]. BHP technical bulletin,1981, 25 (1):79-84.
[19] Suzuki, M., K. Makino, M. Yamada. A study on the coordination number in a system of randonly packed uniform-sized spherical particle[J]. International chemical engineering, 1981, 21 (3):482-488.
[20] Roller, R. W. Granulation of Iron Ores[J]. BHP technical bulletin, 1982, 26 (1):44-45.
[21] Suzuki, Michitaka, Toshio Oshima. Estimation of the co-ordination number in a multi-component mixture of sphere[J]. Powder technology, 1983, 42:841-852.
[22] Suziki, M, T. Oshima. Co-ordination number of a multi-component randomly packed bed of sphere with size distribution[J]. Powder technology, 1985, 44:213-218.
[23] Kuriyama, K., S. Sato, T. Kawaguchi. Development of Iron Ore and Limestone Pre-Granulation Method for Sintering Process. IV. Properties of Iron Ore and Limestone Pre-Granulated Sinter[J]. Transactions ISIJ, 1986, 26 (8):B262-B264.
[24] Hounslow, M J. A discretized population balance for nucleation growth and aggregation[J]. AIChE Journal, 1988, 34 (11):1821-1832.
[25] Litster, J. D., A G Waters. Influence of the material properties of iron ore sinter feed on granulation effectiveness[J]. Powder technology, 1988, 55:141-151.
[26] Sahoo, K. C., D. K. Lahiri, V. Sundermurthy, S. Barpanda. Effect of Ultrafines on Sinter Quality[C].3rd International Symposium on Beneficiation and Agglomeration, 1991:421-433.
[27] Barnaba, P., A. Scarton, G. Quaranta. Control of chemical and physical characteristics of sintering mix to improve productivity and sinter quality[C]. Sixth International Symposium on Agglomeration, 1993:441-449.
[28] Kapur, P.C. An auto-layering model for the granulation of iron ore fines[J]. International of mineral processing, 1993, 39 (3-4):239-250.
[29] Kasai, E., Y. Omori. Subjects on the Evaluation of the Process and Products in the Sintering of Iron Ore[J]. Tetsu to Hagane, 1993, 79 (11):1217-1223.
[30] Migita, M., S. Mochizuki, T. Deno, K. Ando, J. Kitayama, Y. Hida. Realization of self-densification sintering process on Oita No. 1 sintering machine[C]. Sixth International Symposium on Agglomeration, 1993:279-284.
[31] Adetayo, A A, B J Ennis. Unifying approach to modeling granule coalecense mechanism[J]. AIChE Journal, 1997, 43 (4):927-934.
[32] Haga, T., A. Ohshio, Y. Hida, H. Fukuda, N. Ogata. Improvement of the melting reaction in sintering process by the fine part selective granulation of clayish iron ores[J]. Tetsu to Hagane, 1997, 83 (4):233-238.
[33] Venkataramana, R., S. S. Gupta, P. C. Kapur, N. Ramachandran. Modeling of iron ore sinterfeed granulation[J]. Tata Search, 1997:66-72.
[34] Chatterjee, Amit. Role of particle size in mineral processing at Tata Steel[J]. International of mineral processing, 1998, 53:1-14.
[35] Dawson, P. R. Recent Developments in Iron Ore Sintering. IV. The Sintering Process[J]. Ironmaking and Steelmaking, 1993, 20 (2):150-159.
[36] Dawson, P. R. Recent Developments in Iron Ore Sintering. I. Introduction[J]. Ironmaking and Steelmaking, 1993, 20 (2):135-136.
[37] Dawson, P. R. Recent Developments in Iron Ore Sintering. III. Granulation and Strand Feeding[J]. Ironmaking and Steelmaking, 1993, 20 (2):144-149.
[38] Dawson, P. R. Recent Development in Iron Ore Sintering. II. Research Studies on Sintering and Sinter Quality[J]. Ironmaking and Steelmaking, 1993, 20 (2):137-143.
[39] Direct reduced iron[M]. Warrendale, PA: The Iron & Steel Society 1999.
[40] Zhou, Y. S., H. Xu, Y. P. Zhang, H. T. Feng. Review of Energy Saving and Emission Reduction in Iron Making Process[J]. Journal of Iron and Steel Research International, 2009, 16:628-632.
[41] Zhang, Q., L. Guo, X. D. Chen. Analysis of Improving COREX-3000 Competence[J]. Journal of Iron and Steel Research International, 2009, 16:1225-1227.
[42] Wu, S. L., J. Xu, Q. Zhou, S. D. Yang, L. H. Zhang. Characteristics of Gas Flow Distribution in Reduction Shaft Furnace of COREX (R)[J]. Journal of Iron and Steel Research International, 2009, 16:1250-1253.
[43] Goodman, N., R Dry. Hismelt plant ramp-up[J]. Journal of Iron and Steel Research International, 2009, 16:1228-1233.
[44] Chang-Oh, K. The FINEX Process Emerging at New Steel Era[J]. Journal of Iron and Steel Research International, 2009, 16:87-92.
[45] Luo, Guoping, Lishun Yuan, Yanxia Zhao, Jingbo Bai, Yici Wang, Zhizhong Hao. Influence of MgO on mineral composition and microstructure of sinter of Baotou Iron and Steel(Group) Co.[J]. Journal of Inner Mongolia University of Science and Technology, 2008, 27 (01):1-4.
[46] Zhang, Shourong, Han Yin. The trends of ironmaking industry and challenges to chinese blast furnace ironmaking in the 21st centry[J]. Journal of Iron and Steel Research International, 2009, 2009 (16):1-13.
[47]吕学伟,白晨光,邱贵宝,欧阳奇,黄玉明.基于遗传算法的烧结配料综合优化研究[J].钢铁, 2007, 42 (04):12-20.
[48]高炉炼铁能耗与节能分析2008 Available from http://www.chinasie.org.cn/ newmore_tb.asp?id=5713.
[49]吴胜利,陈东峰,赵成显,薛方.基于烧结过程各带迁移规律的烧结终点预报方法[J].北京科技大学学报, 2009, 31 (09):1106-1109.
[50]吴胜利,裴元东,陈辉,彭鹏,杨帆.铁矿粉烧结液相流动性评价[J].北京科技大学学报, 2008, 30 (10):1095-1100.
[51]吴胜利,杜建新,马洪斌,田筠清,许海发.铁矿粉烧结液相流动特性[J].北京科技大学学报, 2005, 27 (03):291-293.
[52]吴胜利,杜建新,马洪斌,张作程,陈辉.铁矿粉烧结粘结相自身强度特性[J].北京科技大学学报, 2005, 27 (02):169-172.
[53]吴胜利,刘宇,杜建新,米坤,林鸿.铁矿石的烧结基础特性之新概念[J].北京科技大学学报, 2002, 24 (03):254-257.
[54] Rumpf. Agglomeration[M]. Pennsylvania: Interscience, 1962.
[55] Schubert, H., W. Herrmanna, H. Rumpfa. Deformation behaviour of agglomerates under tensile stress [J]. Powder technology, 1975, 11 (2):121-131.
[56] Cheng, D. C. H. The effect of secondary flow on the viscosity measurement using the cone-and-plate viscometer [J]. Chemical Engineering Science, 1968, 23 (8):895-899.
[57] Newitt, D. M., J. M. Conway-Jones. A Contribution to the Theory and Practice of Granulation[J]. Transactions of the Institution of Chemical Engineers, 1958, 36:422-440.
[58] Mason, G., W. C. Clark. Liquid bridges between spheres[J]. Chemical Engineering Science, 1965, 20 (10):859-866.
[59] Tigerschoid, M., P. A. Ilmoni. Proc. AIME Blast furnace Coke Oven Raw Materials, 1950:9-18.
[60] Capes, C. E., P. V. Danckwerts. Granule formation by the agglomeration of damp powders. Part I:The mechanism of granule growth[J]. Transactions of the Institution of Chemical Engineers, 1965, 43:T116-T124.
[61] Kapur, P C. Kinetic of green pelletization[J]. Transactions of the American Institute of Mining Engineers, 1964, 229:348-355.
[62] Hulbert, H. M., S. Katz. Some problems in particle technology[J]. Chemical Engineering Science, 1964, 19:555-574.
[63] Randolph, A. D., M. A. Larson. Theory of particulate processes[M]: Acadamic press, 1962.
[64] Sastry, K. V. S., D. W. Fuerstenau. Principles of Agglomerating Particulate Materials by Balling or Granulation [C]. Proceedings of 12th Biennial Conference of the Institute for Briquetting and Agglomeration, 1971:113-124.
[65] Lister, D. H., F. P. Glasser. Phase relations in the system CaO-Al2O3-Iron Oxide[J]. Transactions of the British Ceramic Society, 1967:293-305.
[66] Litster, J., B. Ennis. The science and engineering of granulation process[M]. Netherland: Kluwer academic publishers, 2004.
[67] Waters, A. G., J. D. Litster, S. K. Nicol. A mathematical model for the prediction of granule size distribution for multicomponent sinter feed[J]. ISIJ International, 1989, 29 (4):274-283.
[68] Linkson, P B, J R Glastonbury, G J Duffy. The mechanism of granule growth in wet pelleting[J]. Transaction of institute of chemical Engineering, 1973, 51:251.
[69] Iveson, S. M., S. Holt, S. Biggs. Advancing contact angle of iron ores as a function of their hematite and goethite content: implications for pelletising and sintering[J]. International Journal of Mineral Processing, 2004, 74:1-4.
[70] Iveson, S. M., S. Holt, S Rapmond, C. E. Loo, S. Biggs. The effect of solid-liquid contact angle on the granulation of porous materials: a case study on iron ore granulation[C]. Miami: Advanced Technologies for Particle Processing 1998:127-132.
[71] Iveson, S. M., K. F. Rutherford, S. R. Biggs. Liquid penetration rate into submerged porous particles: theory, experimental validation and implications for iron ore granulation and sintering[J]. Transactions of the Institution of Mining and Metallurgy, Section C, 2001, 110:C133-C143.
[72] Kapur, P. C., S. C. D. Arora, S. V. B. Subbarao. Water-bentonite interaction in balling of iron ores [J]. Chemical Engineering Science, 1973, 28 (8):1535-1540.
[73] Yoshinaga, M., S. Sato, T. Kawaguchi. Fundmental study on granulation of sinter raw material mixture[C]. Sydney, Australia: Australia Japan extractive metallurgy symposium, 1980:145-155.
[74] Khosa, J., J. Manuel. Predicting granulating behaviour of iron ores based on size distribution and composition[J]. ISIJ International, 2007, 47 (7):965-972.
[75]潘建,朱德庆,李建.铁精矿的及原料特性其对成球性能的影响[J].中国科技论文在线. http://www.paper.edu.cn/. 2006,
[76] Maeda, Takayuki, Chieko Fukumoto. Effect of adding moisture and wettability on granulation of iron ore[J]. ISIJ International, 2005, 45 (4):477~484.
[77] Bergstrand, Robin, Jasbir Khosa. The Effect of Marra Mamba Ore Addition on the Granulation Characteristics of Pisolite Based and Hematite Based Sinter Blends[J]. ISIJ International, 2005, 45 (4):492-499.
[78] Kasal, Eiki, J.RANKIN William, F.G. John. The Effect of Raw Mixture Properties on bed permeability during Sintering ANNON[J]. ISIJ International, 1989, 29 (1):33~42.
[79]廖国瑞.烧结原料混匀和制粒技术的研究[J].钢铁研究, 1994, 7 (2):40-44.
[80]黎成家,文剑平.烧结混合机最佳工艺参数选择的研究[J].烧结球团, 1992, 17(4):13-17.
[81]吴力华,陈雪峰.攀钢烧结混合料制粒因素的研究与优化[J].烧结球团, 2006, 31 (1):19-22.
[82]杜炽,何群,石军.强化攀钢烧结混合料制粒的试验研究[J].烧结球团, 1998, 23 (1):36-42.
[83] Knight, Peter. Challenges in granulation technology[J]. Powder technology, 2004, 140:156-162.
[84] Liu, Xiaoyan, E.Specht. Experimental study of the lower and upper angles of repose of granular materials in rotating drums[J]. Powder technology, 2005, 154:125-131.
[85] Forrest, Sarah, John Bridgwater. Flow patterns in granulating systems[J]. Powder technology, 2003, 130:91-96.
[86] Ding, Y. L., R. Forster. Granular motion in rotating drums: bed turnover time and slumping-rolling transition [J]. Powder technology, 2002, 124:18-27.
[87]李炳岳,高斌.对邢钢改善圆筒混合机制粒途径的探讨[J].河北冶金, 2006, (3):20-23.
[88] Venkatara, R., P. C. Kapur, S. S. Gupta. Modelling of granulation by a two-stage auto-layering mechanism in continuous industrial drums.[J]. Chemical Engineering Science, 2002, 57:1685~1693.
[89] Sakamoto, Noboru. Iron ore granulation model supposing the granulation probability estimated from both properties of the ores and their size distributions[J]. ISIJ International, 2002, 42 (8):834~843.
[90] Sosman, R. B., H. E. Merwin. Preliminary report on the system lime: ferric oxide[J]. Journal of Washington acadamic science, 1916, 6:532-536.
[91] Phillips, B., A. Muan. Phase equilibria in the system CaO-iron oxide in air and at 1 atm. O2 pressure[J]. Journal of the American Ceramic Socirty, 1958, 41 (11):445-454.
[92] Phillips, B. , A. Muan. Phase equilibria in the system CaO-iron oxide-SiO2 in air [J]. Journal of the American Ceramic Socirty, 1959, 42 (9):413-423.
[93] Egundebi, G. O., J. A. Whiteman. Ironmaking and Steelmaking, 1989, 16 (6):379-385.
[94] Hida, Y., M. Sasaki, K. Sato, M. Kagawa, N. Soma. Nippon steel technical report, 1987, 35:59-67.
[95] Hsieh, Li-heng, J. A. Whiteman. Effect of oxygen potential on mineral formation in lime-fluxed iron ore sinter[J]. ISIJ International, 1989, 29 (8):625-634.
[96] Heieh, L. H., J. A. Whiteman. Sintering conditions for simulating the formation of mineral phases in industrial iron ore sinter[J]. ISIJ International, 1989, 29 (1):24-32.
[97] Ishikawa, Y , Y Shimomura, M Sasaki, Y Hida, H Toda. Improvement of sinter qualitybased on the mineralogical properties of ores [C]. Atlanta, GA: The 42th Ironmaking Proceedings, 1983:17-29.
[98] Kasai, Eiki, Yoshio Waseda, Maximiano V. Ramos. Thermal analyses of the sintering reactions of iron ores[J]. Tetsu to Hagane, 1997, 83 (9):539-544.
[99] Guo, Xingmin, Yunmao Zhang, Mei Zhang, Huaigong Guo, Haiting Shang, Qiangzhi Li. Study of TG-DSC method on sintering performance of iron ores imported in Laiwu steel[J]. Iron and Steel, 2004, 39 (8):34-37.
[100] Morcos, Riham Michelle, Alexandra Navrotsky. Iron ore sintering characterization by calorimetry and thermal analysis[J]. Journal of thermal analysis and calorimetry, 2009, 96 (2):353-361.
[101]吴胜利,刘宇,杜建新,米坤,林鸿.铁矿粉与CaO同化能力的试验研究[J].北京科技大学学报, 2002, 26 (3):258-261.
[102]吴胜利,许海法,汪国俊,李肇毅,田筠清,陈辉.现代高炉合理使用天然块矿的基础研究[J].北京科技大学学报, 2007, 29 (03):320-324.
[103]吴胜利,汪国俊,姜伟忠,孙金铎,许海法.高炉内天然块矿与烧结矿高温交互反应研究[J].钢铁, 2007, 42 (3):10-13.
[104] Loo, C. E., G. C. Penny, D Witchard. Effective sintering of iron ore blends containing porous ores at low moisture contents[J]. Transaction of Institution mineral and metallurgy (Section C: Mineral processing and extractive metallurgy), 1996, 105:C22-C36.
[105] Loo, C. E., K. T. Wan, V. R. Howes. Mechanical properties of natural and synthetic mineral phases in sinters having varying reduction degradation indices[J]. Ironmaking and Steelmaking, 1988, 15 (6):279-285.
[106] Sasaki, M., Y. Hida. Consideration on the properties of sinter from the point of view of sintering reactions[J]. Tetsu-to-Hagane, 1982, 68 (6):563-571.
[107]金明芳,李光森,姜鑫,沈峰满.铁酸钙熔体对赤铁矿的渗透行为及对烧结强度的影响[J].东北大学学报(自然科学版), 2008, 29 (08):1135-1138.
[108]李光森,金明芳,储满生,李小刚,沈峰满.含氟烧结矿硅酸盐粘结相自身的强度[J].钢铁研究学报, 2008, 20 (1):10-28.
[109]李光森,窦力威,尚策,沈峰满.铁酸钙粘结相自身强度的研究[J].东北大学学报(自然科学版), 2009, 30 (02):225-228.
[110]应自伟,许力贤,姜茂发,沈岩松.铁酸钙的形态对烧结矿抗断裂性能的影响[J].钢铁研究学报, 2006, 18 (02):55-58.
[111] Matsuno, F., T. harada. Changes in mineral phases during the sintering of Fe203-CaO-Si02 system[J]. Transactions of the Iron and Steel Institute of Japan, 1979, 21 (5):318-325.
[112]郭兴敏,张俊,李春增,朱利,赵金龙.准化学平衡条件下MgO对多元铁酸钙生成的影响[J].钢铁, 2009, 44 (04):16-19.
[113]甘勤,何群.影响钒钛烧结矿铁酸钙生成因素的研究[J].烧结球团, 2008, 33 (02):9-14.
[114]刘振林.铁矿石烧结的铁酸钙生成特性研究[J].山东冶金, 2003, 25 (06):46-49.
[115]范晓慧,孟君,陈许玲,庄剑鸣,李英,袁礼顺.铁矿烧结中铁酸钙形成的影响因素[J].中南大学学报(自然科学版), 2008, 39 (6):1125-1131.
[116]李广森,金明芳,姜鑫,沈峰满.烧结矿粘接相的熔化特性[J].东北大学学报(自然科学版), 2008, 29 (5):697-700.
[117] Ying, Z. W., M. F. Jiang, L. X. Xu. Effects of mineral composition and microstructure on crack resistance of sintered ore[J]. Journal of Iron and Steel Research International, 2006, 13 (4):9-12.
[118] Nandy, B., M. K. Chaudhury, J. Paul, D. Bhattacharjee. Sintering Characteristics of Indian Chrome Ore Fines[J]. Metallurgical and Materials Transactions B-Process Metallurgy and Materials Processing Science, 2009, 40 (5):662-675.
[119] Matsumura, M., M. Hoshi, T. Kawaguchi. Improvement of sinter softening property and reducibility by controlling chemical compositions[J]. ISIJ International, 2005, 45 (4):594-602.
[120] Tsukihashi, F., H. Kimura. The effect of MgO and Al-2(3)O (additions on the liquidus of the CaO-SiO)(-FeO)(2)(x) system at various partial pressure of oxygen[J]. Metallurgical and Materials Processing: Principles and Technologies, 2003, 1:851-860.
[121] Kongoli, F., I. McBow, A. Yazawa. Phase relations during sintering of iron ore and fluxing effect of minor components[J]. Metallurgical and Materials Processing: Principles and Technologies, 2003, 1:917-925.
[122] Wu, Shengli, Hongliang Han, Weizhong Jiang, Jinming Zhu, Gensheng Feng, Zuocheng Zhang. MgO interaction mechanism in sinter[J]. Journal of University of Science and Technology Beijing, 2009, 31 (04):428-432.
[123] Zhao, Yanxia, Guoqing Luo, Zhizhong Hao, Jingbo Bai. Effect of MgO on bonding phase properties of sinter of Baotou Iron and Steel(Group) Co.[J]. Journal of Inner Mongolia University of Science and Technology, 2008, 27 (1):5-9.
[124] Jin, Mingfan, Guangsen Li, Xin Jiang, Fengman Shen. Effect of penetration behavior of calcium ferrite melts into hematite on the strength of sinter[J]. Journal of Northeast University (Natural Science), 2008, 29 (8):1135-1138.
[125] Jin, Mingfan, Guangsen Li, Mansheng Chu, Xin Jiang, Fengman Shen. Influence of the melting characteristic of fine ores on the strength of sinter[J]. China Metallurgy, 2008, 18(6):17-20.
[126] Gan, Qin, Qun He, Yongcai Wen. Study on influence of MgO on mineral composition and metallurgical properties of V-bearing titaniferous magnetite sinter[J]. Iron and Steel, 2008, 43 (08):7-11.
[127] Lu, Qing, Fumin Li, Wenshan Wang, Binsheng Hu. Influence of w(MgO) on sinter strength and sintering process of vanadium-titanium magnetite[J]. Research on Iron and Steel, 2007, 35 (01):5-8.
[128] Jiang, Xin, Gangsheng Wu, Guo Wei, Xiaogang Li, Fengman Shen. Effect of MgO on Sintering Process and Metallurgical Properties of Sinter[J]. Iron and Steel, 2006, 41 (03):8-12.
[129] Jiang, Xin, Gangsheng Wu, Mingfan Jin, Fengman Shen. Effect of MgO Content on Softening-Melting Property of Sinter[J]. Journal of Northeast University (Natural Science), 2006, 27 (12):1358-1362.
[130] Zhou, Mingshun, Liwei Zhai, Yanru Li. Laboratory study on reasonable MgO content in the sinter of Anshan iron and steel group co. [J]. Sintering and Pelletizing, 2005, 30 (06):1-4.
[131] Huang, Zhucheng, Zhiguo Han, Jiewang Zhou, Tao Jiang, Xipeng Yang. Influence of MgO on microstructure of fine magnetite concentrate based sinter[J]. Iron and Steel, 2005, 40 (09):16-20.
[132] Yadav, U. S., B. D. Pandey, B. k. Das, D. N. Jena. Influence of magnesia on sintering characteristics of iron ore[J]. Ironamking Steelmaking, 2002, 29 (2):91-95.
[133]吴胜利,韩宏亮,姜伟忠,朱锦明,冯根生,张作程.烧结矿中MgO作用机理[J].北京科技大学学报, 2009, 31 (4):428-432.
[134] Zhang, Yuzhu, Yan Xu, Xinguo Si, Qingjun Zhang, Xuegang Ma. Research on size distribution of sinters with different MgO content[J]. Iron and Steel, 2007, 42 (09):13-15.
[135]甘勤,何庆莉,邓君.钒钛烧结矿适宜FeO含量的研究[J].云南冶金, 2000, 29 (06):19-23.
[136]张金柱,邓海亮,敖万忠,赵跃萍.烧结混合料中SiO_2和FeO含量对烧结矿强度的影响[J].钢铁, 2008, 43 (05):18-21.
[137]蒋大军,何木光,甘勤,何群.高碱度条件下FeO对烧结矿性能的影响[J].中国冶金, 2008, 18 (11):14-21.
[138]袁慧,周明顺,翟立委,李艳茹,任伟,张立国. FeO对烧结矿矿物组成和显微结构影响的研究[J].中国冶金, 2008, 18 (08):6-9.
[139]张胜.烧结矿FeO对其质量的影响[J].烧结球团, 1990, (5):31-34.
[140]庄剑呜,姚锐.烧结矿FeO含量的确定[J].烧结球团, 1997, 22 (6):4-7.
[141]刘竹林.烧结矿FeO含量的影响因素探讨[J].重庆科技学院学报(自然科学版), 2005, 7 (01):8-13.
[142]卢红军,贾春海,成飞.烧结矿中FeO含量的影响因素分析[J].山东冶金, 2007, 29 (02):45-46.
[143]王明阳.影响烧结矿FeO含量的工艺参数最佳配置研究[J].武汉工业大学学报, 1998, 20 (3):50-52.
[144]张军红,沈峰满,谢安国. G-BP算法在烧结矿FeO指标预测中的应用[J].东北大学学报(自然科学版), 2002, 23 (11):1073-1075.
[145]蒋大军.烧结矿FeO含量预报系统开发与应用[J].钢铁, 2006, 41 (09):13-17.
[146]马洛文.基于机尾红外热图像的烧结矿质量检测[J].炼铁, 2005, 24 (9):107-109.
[147]吴晓峰.基于机尾断面红外特征构建烧结过程温度三维趋势图的方法研究[J].冶金自动化, 2007, (2):14-18.
[148]周芳.在线判断烧结矿烧结点的图像识别法[J].自动化与仪表, 2005, (4):7-12.
[149]谢志江,佟莹,欧阳奇.烧结矿FeO含量模糊聚类算法的研究与应用[J].重庆大学学报, 2006, 29 (10):58-61.
[150]姚志超,张延玲,李士琦,周燕.考虑性能的烧结优化配料模型[J].包头钢铁学院学报, 2002, 21 (3):263-267.
[151]陈义华,王开荣,何仁斌.最优化方法[M].重庆:重庆大学出版社, 2003.
[152]贾彦忠,王书瑞,周振庄,李士琦.烧结配料优化利用研究[J].河北冶金, 1995, (3):15-18.
[153]蒋大军,石军.铁矿石的价值评价与线性规划优化配料[J].烧结球团, 1999, 24 (5):44-48.
[154]贾娟鱼,白晨光,赖宏,邱贵宝.烧结矿和入炉矿配料的优化及实现[J].重庆大学学报, 2002, 25 (10):68-71.
[155]周明顺,刘万山,杜鹤贵,黄晓煜.鞍钢烧结优化配矿研究[J].钢铁, 2004, 39 (6):10-13.
[156]张艳允.邯钢烧结配料优化探讨[J].烧结球团, 2002, 27 (2):8-12.
[157]赵晓煜,王福利,杨英华.基于目标规划的多物料配比优化计算[J].烧结球团, 1996, 21 (3):16-20.
[158]马振华.现代应用数学手册(运筹学与最优化理论卷)[M].北京:清华大学出版社, 1998.
[159]雷英杰,张善文. MATLAB遗传算法工具箱及应用[M].西安:西安电子科技大学出版社, 2005.
[160]李智,姚驻斌.免疫遗传算法在烧结矿配料优化中的应用[J].上海金属, 2004, 26 (6):23-29.
[161]李智,姚驻斌,张望兴,贺超武.基于神经网络的混匀配料优化方法[J].钢铁研究,,2000, (4):10-12.
[162]李智,李伟.模拟退火算法在铸造生产配料优化中的应用[J].铸造技术, 2004, (11):816-818.
[163]李智,谢兆鸿,姚驻斌.蚁群算法在原料矿粉混匀优化中的应用[J].矿冶, 2004, (2):75-78.
[164]周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社, 1999.
[165]邓先礼.最优化技术[M].重庆:重庆大学出版社, 1998.
[166]许俊,韩志伟,邹德余.蒙特卡洛优化法在炼焦配煤中的应用[J].燃料与化工, 2001, 32 (1):13-16.
[167]梁中渝,胡林,邓能运,龚文渠.铁矿粉配料烧结经济评价体系的研究[J].烧结球团, 2003, 28 (4):3-7.
[168]郭文军,王福利,李明忠.基于神经网络的烧结矿化学成分超前预报[J].烧结球团, 1997, 22 (5):7-10.
[169]韩庆虹,金永龙,张军红. BP网络在烧结矿质量预测中的初步应用[J].烧结球团, 2005, 30 (1):20-22.
[170]张舒,高为民.人工神经网络在烧结矿指标预测中的应用[J].烧结球团, 2006, 26 (4):6-10.
[171]张军红,谢安国,沈峰满.烧结矿质量预测的BP网络模型[J].鞍山钢铁学院学报, 2001, 24 (6):406-408.
[172]刘怀.用BP网络模型对烧结矿质量预测[J].有色矿山, 1999, (5):41-44.
[173]刘勇,程武山,孙鑫.基于自适应遗传神经网络的烧结矿化学成分超前预报模型[J].烧结球团, 2005, 30 (6):16-19.
[174]范晓慧,曾垂喜,姜涛.铁矿石烧结性能预报模型[J].中南大学学报(自然科学版, 2005, 36 (6):949-954.
[175]王翊,王雪.基于径向基函数神经网络的烧结矿FeO实时预测[J].冶金自动化, 2006, (Supp.):22-25.
[176]谌晓文,邬捷鹏.基于BP神经网络的烧结终点预测模型[J].烧结球团, 2006, 31 (4):21-26.
[177] Lv, Xuewei, Chenguang Bai, Chuanqiang Zhou, Hao Xie. Determination of moisture capacity of iron ore for sintering[C]. Shanghai, China: Proceedings of the 5th international congress on the science and technology of ironmaking, 2009:235-238.
[178] Kondo, Seiichi, Tatsuo Ishikawa, Ikuo Abe. Adsorption Science (In Japanese)[M]. Tokyo: Maruzen Co. Ltd., 1991.
[179] Schramm, L. L. The language of colloid and Interface Science[M]. Washington, DC.: ACS,1993.
[180] Ho, Yuh-shan. Citation review of Lagergren kinetic rate equation on adsorption reactions[J]. Scientometrics, 2004, 59 (1):171-177.
[181] Hornik, K. Cross-validation with active pattern selection for neural-networkclassifiers[J]. IEEE Transactions on Neural Networks, 1998, 9:35-41.
[182] Wu, Shengli, Jianxin Du, Hongbin Ma, Junqing Tian, Haifa Xu. Fluidity of liquid phase in iron ores during sintering[J]. Journal of University of Science and Technology Beijing, 2005, 27 (3):291-293.
[183] Wu, Shengli, Yu Liu, Jianjin Du, Kun Mi, Hong Lin. New concept of iron ore sintering basic characteristics[J]. Journal of University of Science and Technology Beijing, 2002, 24 (3):254-257.
[184]任允芙.钢铁冶金岩相矿相学[M].北京:冶金工业出版社, 1982.
[185]周乐光.工艺矿物学[M].北京:冶金工业出版社, 2007.
[186] Jeulin, D. Quantitative morphology and quality of iron ore sinter[C]. Atlanta: Proceeding of the 42nd ironmaking conference 1983:31-41.
[187] Jeulin, D. Relation between quality and morphology of sinter- texture analysis contribution[J]. Ironmaking Steelmaking, 1983, 10 (4):145-154.
[188] Shibuya, T., H. Yanaka, K. Takemoto. Quantitative determination of sinter structure by image analysis[J]. Transactions ISIJ, 1985, 25:257-260.
[189]冈萨雷斯.数字图像处理[M].北京:电子工业出版社, 2006.
[190]章毓晋.图像处理和分析[M].北京:清华大学出版社, 1999.
[191]梁运行,崔杜武.图像拼接的预处理算法研究[J].西安理工大学学报, 2003, 19 (4):348-351.
[192]周金和,彭福堂.一种有选择的图像灰度化方法[J].计算机工程, 2006, 32 (20):198-200.
[193] Smith, A R. Color gamut transform pairs[J]. Computer Graphics, 1978, 12 (3):12-19.
[194]贾永红.数字图像处理[M].武汉:武汉大学出版社, 2003.
[195] Chang, Jeng-horng, Kuo-Chin Fan, Yang-Lang Chang. Multi-modal gray histogram modeling and decomposition. [J]. Image and Vision Computing, 2002, 20:203~216.
[196]薛景浩,章毓晋,林行刚.低质量图像基于散射图SEM估计的MAP像素聚类方法[J].电子学报, 1999, 27 (7):95-98.
[197] Togan, Vedat, Ayse T. Daloglu. An improved genetic alogrithm with initial population strategy and sel-adaptive menmber grouping[J]. Comput. & Struct., 2008, 86 (11-12):1204-1218.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |