低锰高铁矿直接还原-熔分制备富锰渣试验研究

摘要：试验以锰品位27.7%，铁品位18.1%的低锰高铁矿为研究对象还原制备富锰渣，生产得到的富锰渣可用于冶炼硅锰合金，以达到高效利用低品位锰矿的目的。根据该矿的成分分析、XRD分析和粒度检测分析结果，采用还原 熔分法对低锰矿进行还原制备富锰渣试验，试验结果表明：单因素试验下各参数对低锰高铁矿的还原-熔分后渣中Mn、Fe元素的含量和Mn元素的回收率均有较大影响，同时结合Box-Behnke原理设计方案，选取温度、碱度以及配碳量3个试验因素，通过响应曲面法研究各因素交互作用下对Mn元素回收率的影响规律，对试验因素进行优化分析，建立相应的多项式模型。模拟优化得到最优的工艺条件为：还原温度1402℃，碱度0.10，配碳量10.04%，Mn元素回收率为97%。在最佳条件下做验证试验得出Mn元素回收率为95.80%，误差1.24%，证明响应曲面法预测模型具有可靠性，同时对低锰高铁矿的应用有重要指导意义。

Experimental study on preparation of manganese rich slag by direct reduction melting separation of low manganese high iron ore

Abstract： In this experiment, low manganese high iron ore with manganese grade of 27.7% and iron grade of 18.1% was used as the research object to reduce and prepare manganese rich slag. The obtained manganese rich slag can be used for smelting silicon manganese alloy to achieve the purpose of efficient utilization of low grade manganese ore. According to the results of composition analysis, XRD analysis and particle size analysis of the ore, the reduction melting separation method was used to prepare manganese rich slag from low manganese ore. The experimental results show that each parameter has a greater impact on the mass fraction of manganese and iron in the reduction melting separation slag of low manganese high iron ore and the recovery rate of manganese under the single factor test. At the same time, combined with the Box Behnke principle design scheme, three experimental factors including temperature, alkalinity and carbon content were selected. The influence of each factor on the recovery rate of manganese was studied by response surface method. The experimental results were analyzed to establish the corresponding polynomial model, and the optimal process conditions were as follows: reduction temperature of 1402℃, alkalinity of 0.10, carbon content of 10.04%, and the recovery rate of manganese was 97%. A verification test was conducted under the optimal conditions; the recovery rate of manganese was 95.80%, and the error was 1.24%, which proved that the response surface method was a reliable and accurate prediction model. At the same time, the results are instructive for the application of low manganese and high iron ore.