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本文采用中心组合设计法在实验室中研究了焙烧温度、焙烧时间、氧化铝、二氧化硅和石灰含量及磨矿细度(布莱恩指数)等六因素在相当大的范围内变化(该范围比工厂的实际变化范围要宽)时球团矿性质的变化情况。文中介绍了在等温焙烧条件下球团矿的耐磨指数(A)、冷抗压强度(C)、JIS还原率(R)和孔隙率(P)的测定结果;详细列出了有数值系数判断的经验数学模型以及试验的设计和统计分析。采用图形示出了重要的特性及其变化趋势。根据等温数据绘出的球团矿特性的等值线表明:焙烧温度是起支配作用的因素:随焙烧温度的增加,A和C改善,而R和P减少;随布莱恩指数的升高,A和C得到改善,但R几乎不受影响:焙烧时间和氧化铝含量对球团矿性质没有什么影响。试验研究了焙烧温度和布莱恩指数之间以及石灰和二氧化硅含量之间的重要交互作用。球团矿的质量标准在相当大的程度上限制了能够实际应用的变量组合的数目。数据的子集(即一部分数据——译者注)反映了变量对典型的酸性高炉球团矿和直接还原球团矿性质的影响。球团矿性质随各变量变化的微分响应(因变量随自变量的变化规律——译者注)为球团厂生产的最佳化提供了线索。
In this paper, the central composite design method was used to study in the laboratory the six factors of calcination temperature, calcination time, alumina, silica and lime content and grinding fineness (Blaine index) varied in a wide range Wider than the actual range of changes in the factory) when the nature of the pellet changes. The results of determination of wear resistance index (A), cold compressive strength (C), JIS reduction ratio (R) and porosity (P) of pellets under isothermal calcination are described in this paper. Judgmental mathematical model of experience and experimental design and statistical analysis. Graphically shows the important features and trends. The contour of the pellet properties plotted on the basis of the isothermal data shows that the calcination temperature is the predominant factor: A and C improve and R and P decrease with increasing calcination temperature. With the increase of Blaine index, A and C are improved, but R is almost unaffected: the firing time and the alumina content have no effect on the pellet properties. The experimental study investigated the important interaction between the calcination temperature and the Bryant index and between the lime and silica contents. The quality standards for pellets limit to a considerable extent the number of variable combinations that can be practically used. The subset of data (ie, part of the data) reflects the effect of variables on the properties of a typical acidic blast furnace pellet and direct reduced pellet. The differential response of the properties of the pellets with the variation of each variable (the dependent variable changes with independent variables) provides clues for the optimization of pellet plant production.