昆钢烧结矿低温还原粉化性能分析

本文主要介绍了昆钢烧结矿的热性能，并对影响烧结矿低温还原粉化性能的因素作了分析。     

改善攀钢钒钛烧结矿低温还原粉化性研究

本文研究了ＦｅＯ，ＭｇＯ及氯化物稀溶液对攀钢钒钛烧结矿低温还原粉化（ＲＤＩ）的影响，探讨了氯化物稀溶液处理法改善钒钛烧结矿ＲＤＩ的机理及氯化物对高炉腐蚀的问题。     

烧结矿低温还原粉化的研究

本文阐明了烧结工艺条件、烧结矿微观组织结构与烧结矿低温还原粉化的内在联系和规律。对烧结矿低温还原粉化行为及氧化亚铁的影响,提出了一些新观点。这些观点对指导我国造块工业生产具有重要意义。     

控制烧结矿低温还原粉化的试验研究

介绍了湘潭钢采用工业废湘与一定浓度的ＣａＣｌ２溶液配制喷洒在烧结矿的表面上，通过控制喷洒液的浓度以及吨烧结矿的喷洒量，实现了烧结矿ＲＤＩ值平均降低５６．７％，使高炉产量提高１０．５０％，降低焦比３５．５％ｋｇ／ｔ。     

昆钢烧结矿低温还原粉化控制技术试验分析

本文分析了我司试验和生产应用“烧结矿低温学原粉化控制技术”的情况。通过调试摸索和工业试验表明：此技术工艺简单，操作方便，效果明显，有用后，烧结矿低温还原粉化率可降低约５０％，高炉料柱透气性改善，煤气利用率提高，高炉实际节焦６．８５ｋｇ／ｔ，增产６．０２％。     

改善低硅烧结矿低温还原粉化性能的研究

针对低硅烧结矿强度低、低温还原粉化性能差的特点,对某钢铁厂烧结矿矿相进行了分析,发现其磁铁矿和铁酸钙含量较少,而强度极差的硅酸盐玻璃质和还原性较好的赤铁矿含量较高。为改善低硅烧结矿低温还原粉化性能,研究了烧结工艺(燃料配比、碱度)和烧结矿成分(Al2O3/SiO2、MgO含量)等因素对烧结矿矿相和低温还原粉化性能的影响,在该厂的原料条件下,得出最佳的燃料配比和碱度分别为5.0%和2.3;Al2O3/SiO2为0.31,MgO的质量分数为2.02%。     

铁矿石烧结矿低温还原粉化理研究

介绍了无填料环氧树脂浇注干式变压器计算机辅助优化设计软件的特点，给出了数学模型、程序结构和框图，并用计算实例证实了该软件的实用性。     

烧结矿碱度的灰色神经网络预测模型及仿真

本文提出了用灰色神经网络对烧结矿化学成分进行预测,并在此基础上构造了灰色神经网络模型,该模型有效地融合了灰色理论可弱化数据序列波动性和神经网络特有的适应非线性信息处理的能力,研究结果证明,本模型能在小样本、贫信息的条件下对烧结矿碱度做出比较准确的预测,此种模型具有预测精度高、所需样本少、计算简便等优点,取得了比较满意的结果。和BP神经网络算法相比,灰色神经网络算法有很大的应用前景和推广价值。     

Effect of Hydrogen Addition on Low Temperature Metallurgical Property of Sinter

 Abstract： Hydrogen-enrich iron making process is certainly to be an effective method to reduce greenhouse gases emission. However, the effect of hydrogen addition on the low temperature metallurgical property of sinter is still unclear. A detailed investigation was performed on the above topic. The results are as follows. When CO is partially replaced by H2, the RDI＜315 (RDI＜28) of sinter decreases with the increase of the H2 content; when the content of H2 increases, the CO, CO2 and N2 decrease proportionally, in this case, RDI＜315 (RDI＜28) of sinter increases with the increase of H2 content; the value of RDI＜315 (RDI＜28) basically depends on the reduction index (Ri).The experimental data of RDI＜28 based on Japanese industrial standard (JIS) are a little higher than the data of RDI＜315 based on Chinese industrial standard (CIS) in the same condition. In addition, for part of CO is replaced by H2: RDI＜28=338394+11585 RDI＜315; for other gases, except H2, are decreased proportionally: RDI＜28=1739678+042922 RDI＜315.     

改善鞍钢烧结矿冶金性能的试验研究

以烧结矿碱度和w(FeO)为出发点,进行了鞍钢烧结矿冶金性能优化试验研究。结果表明:烧结矿碱度增加,低温还原粉化性和还原性得到改善;但碱度过高时,对低温还原粉化和还原性的影响不明显,鞍钢烧结矿适宜碱度为2.05~2.14。w(FeO)提高,烧结矿低温还原粉化性能得到明显改善,但烧结矿还原性变差,综合考虑烧结生产和烧结矿冶金性能,烧结矿适宜w(FeO)应控制在8%左右。     

Low Temperature Reduction Degradation Characteristics of Sinter, Pellet and Lump Ore

 The reduction degradation characteristics of typical sinter, pellet and lump ore were tested with the reducing gas conditions simulating two kinds of iron-making processes. The results show that, in the same condition of gas composition and temperature, the reduction degradation degree (RDI＜3.15 mm) of sinter is high, RDI＜3.15 mm of lump ore is low and RDI＜3.15 mm of pellet is in the middle level. With two kinds of gas composition simulating different iron-making processes, the reduction degradation indices (RDI) of three kinds of iron ores all present the tendency of “inverted V-shape” in the temperature range from 450 to 650 ℃, and the RDI reach the maximum value at 550 ℃. The reduction degradation degrees of iron ores are extended when mixing the gas with hydrogen to increase the reduction potential, and the influence extent is discrepant for different iron ores. Colligating the increase amplitude of grains in small size fraction, the influence of reducing gas on lump ore is the greatest, the influence on sinter is the second, and the sensitivity of pellet on the reducing gas properties change is relatively small. As for the degradation form, lump ore and sinter both present the degradation of cracking, and the distribution of small grains generated from the cracking is in the range from 0.5 to 6.3 mm uniformly. The lump ore presents surface cracking, while sinter presents integral cracking. The pellet presents the degradation of surface stripping, and the proportion of grains smaller than 0.5 mm is the highest, which is up to 90% in the grains smaller than 3.15 mm.     

包钢低硅烧结矿的冶金性能

 在通过红外微型烧结试验，确定温度、碱度、MgO含量及SiO2含量对包钢低硅烧结矿黏结相强度影响强弱顺序以及黏结相强度最优、较好、较差及最差的包钢低硅烧结工艺条件基础上，采用烧结杯对上述4种烧结矿的黏结相强度进行验证，并对其冶金性能进行研究。结果表明，低硅烧结的最佳工艺条件为：SiO2含量(质量分数，下同)4.0%、碱度2.5、MgO含量1.6%、配碳量3.8%。在此工艺条件下获得的包钢低硅烧结矿具有优良的冷态强度(转鼓强度83.1%)，软熔滴落性能(tS为1307.3℃，tD-tS为98℃)优于包钢烧结矿，还原性(RI为83.65%)较好。尽管低温还原粉化性与目前包钢烧结矿相当，但综合评价，包钢低硅烧结矿可以满足高炉炼铁的需求。     

基于LM算法BP神经网络的高炉-转炉界面铁水温度预报模型

通过研究高炉-转炉界面铁水运输过程温度的主要影响因素,确定了影响高炉-转炉界面铁水运输过程温度的参数,建立了基于Levenberg-Marquardt (LM)算法BP神经网络的高炉-转炉界面铁水温度及铁水过程温降的预报模型.用沙钢100包铁水数据进行模型训练,50包铁水数据进行现场预报,结果表明:在高炉-转炉界面“一包到底”模式下,当绝对误差| X |≤20℃时,铁水温度命中率为94％,铁水温降命中率为78％;当绝对误差|X|≤40℃时,铁水温度命中率为100％,铁水温降命中率为92％,该预报模型能够满足现场实际生产需求,对炼钢生产有很好的指导意义.     

碱度对鞍钢烧结矿产、质量的影响

由于鞍钢球团矿产能增大,球团矿的入炉比例将增加,这就要求提高烧结矿的碱度以满足高炉生产的要求。为了考察继续提高碱度对烧结矿产质量的影响,进行了将烧结矿的碱度从2.0逐步增加到2.8的试验研究。结果表明在此碱度范围内,随着烧结矿碱度的提高,利用系数呈逐渐提高趋势,转鼓强度有明显的提高趋势,烧结矿中铁酸钙含量逐渐增加,烧结矿的冶金性能改善,这些都更加有利于高炉冶炼。     

不同类型铁矿石的低温还原粉化特征

以炼铁厂常用烧结矿、球团矿以及块矿为研究对象,考察了不同含铁原料在高炉炼铁工艺和COREX非高 炉炼铁工艺2种煤气条件下的低温还原粉化行为。研究结果表明:①铁矿石的结构组成决定了其粉化形式,块矿 和烧结矿的粉化形式均表现为“裂开式”,粉化产生的细小粒级颗粒较均匀地分布于-6.3 mm范围;不同于烧结矿 和块矿,球团矿则属于“剥落式”粉化,粉化产物粒径主要集中在-0.5 mm范围;②在还原气氛和温度相同的情况 下,烧结矿粉化程度最高,球团矿次之,块矿最低;③还原温度对3种含铁炉料的还原粉化程度均具有重要影响,当 还原煤气条件相同时,3种含铁炉料的低温还原粉化指数均在550℃时达到最大值;④COREX炼铁工艺的煤气条 件加剧试验用铁矿石的低温还原粉化程度,但对不同类型铁矿石的影响有所差异,综合粉化产生的细小颗粒的增 幅可知,块矿受其影响程度较大,其次是球团矿,而烧结矿对煤气性质改变的敏感性最小。     

Multi-Objective Optimization and Analysis Model of Sintering Process Based on BP Neural Network

A multi-objective optimization and analysis model of the sintering process based on BP neural network is presented. Genetic algorithms are combined to simplify the BP neural network, which can reduce the learning time and increase the forecasting accuracy of the network model. This model has been experimented in the sintering process, and the production cost, the energy consumption, the quality （revolving intensity）, and the output are considered at the same time. Moreover, the relation between some factors and the multi-objectives has been analyzed, and the results are consistent with the process. Different objectives are emphasized at different practical periods, and this can provide a theoretical basis for the manager.     

基于BP神经网络的冷轧带钢力学性能预测

利用BP神经网络方法预测SPCC冷轧带钢产品力学性能并以现场正交试验数据为基础,对比预报结果和试验结果可以知道,该模型具有较高的精度,适用于现场生产。