摘要
基于XRD-Rietveld全谱拟合法,将样品XRD数据进行精修,得到与物相相关的参数,如物相比例、晶体结构等,是目前较为常用的矿相研究手段,对于研究物相之间的作用关系以及形成过程有重要作用。烧结矿中的粘结相如复合铁酸钙,对烧结矿的强度、还原性等冶金性能影响极大,确定这类物相在烧结矿中的占比,能够一定程度上了解烧结矿性能的优劣。但作为烧结矿性能的表征手段之一,烧结矿的精修研究,大多停留在对于前人研究的物相分析的水平,利用已知物相进行精修,使精修结果有失特征性和专一性。通过对比国内外烧结矿精修过程,以及探究精修物相对精修结果的影响,提出优化精修过程,并将精修过程具体化,以物相的选择和精修顺序为重点,针对烧结矿进行专一性精修,使结果更具真实性。同时在一次精修的基础上继续二次精修,通过一次精修确定基本物相比例,根据比例不同改变精修顺序,使晶相含量与实际相吻合。
Based on the XRD Rietveld full spectrum fitting method,the sample XRD data is refined to obtain parameters related to the phase,such as phase proportions,crystal structure,etc.It is currently a commonly used mineral phase research method and plays an important role in studying the interaction relationship and formation process between phases.The bonding phase in sintering ore,such as composite calcium ferrite,has a significant impact on the strength,reducibility,and other metallurgical properties of sintering ore.Determining the proportion of these phases in sintering ore can help to understand the advantages and disadvantages of sintering ore performance to a certain extent.However,as one of the characterization methods for the performance of sintered ore,the research on the refinement of sintered ore mostly remains at the level of phase analysis studied by previous researchers,using known phases for refinement,resulting in a loss of specificity and specificity in the refinement results.By comparing the refining processes of domestic and foreign sinters and exploring the impact of refined materials on the refining results,it is proposed to optimize the refining process and concretize it.With a focus on the selection of material phases and the sequence of refining,specific refining is carried out for sinters to make the results more realistic.At the same time,on the basis of the first refinement,the second refinement is continued,the proportion of the basic phase is determined through first refinement,and the refinement order is changed according to the different proportions,so that the crystal phase content is consistent with the reality.
作者
冯奔
刘征建
张建良
王耀祖
牛乐乐
FENG Ben;LIU Zhengjian;ZHANG Jianliang;WANG Yaozu;NIU Lele(School of Metallurgical and Ecological Engineering,University of Science and Technology Beijing,Beijing 100083,China;Institute of Artificial Intelligence,University of Science and Technology Beijing,Beijing 100083,China)
出处
《钢铁研究学报》
CAS
CSCD
北大核心
2023年第5期551-559,共9页
Journal of Iron and Steel Research
基金
国家自然科学基金资助项目(52174291)。
