High-temperature experiments were carried out for the slag systems of“FeO”−SiO_(2)−CaO−Al_(2)O_(3)and“FeO”−SiO_(2)−CaO−MgO at 1200℃and p(O_(2))of 10^(−7)kPa.The equilibrated samples were quenched,and the phase co...High-temperature experiments were carried out for the slag systems of“FeO”−SiO_(2)−CaO−Al_(2)O_(3)and“FeO”−SiO_(2)−CaO−MgO at 1200℃and p(O_(2))of 10^(−7)kPa.The equilibrated samples were quenched,and the phase compositions were measured by electron probe microanalysis(EPMA).A series of pseudo-ternary and pseudo-binary phase diagrams are constructed to demonstrate their applications in copper smelting process and evaluation of the thermodynamic database.Spinel and tridymite are identified to be the major primary phases in the composition range related to the copper smelting slags.It is found that the operating window of the smelting slag is primarily determined by w_(Fe)/w_(SiO_(2))ratio in the slag.Both MgO and Al_(2)O_(3)in the slag reduce the operating window which requires extra fluxing agent to keep the slag to be fully liquid.Complex spinel solid solutions cause inaccurate predictions of the current thermodynamic database.展开更多
氧化亚铁(FeO)含量是衡量烧结矿强度和还原性的重要指标,烧结过程FeO含量的实时准确预测对于提升烧结质量、优化烧结工艺具有重要意义.然而烧结过程热状态参数缺失、过程参数波动频繁给FeO含量的高精度预测带来巨大的挑战,为此,提出一...氧化亚铁(FeO)含量是衡量烧结矿强度和还原性的重要指标,烧结过程FeO含量的实时准确预测对于提升烧结质量、优化烧结工艺具有重要意义.然而烧结过程热状态参数缺失、过程参数波动频繁给FeO含量的高精度预测带来巨大的挑战,为此,提出一种基于知识与变权重回声状态网络融合(Fusion of data-knowledge and adaptive weight echo state network, DK-AWESN)的烧结过程FeO含量预测方法.首先,针对烧结过程热状态参数缺失的问题,建立烧结料层最高温度分布模型,实现基于料层温度分布特征的FeO含量等级划分;其次,针对烧结过程参数波动频繁的问题,提出基于核函数高维映射的多尺度数据配准方法,有效抑制离群点的影响,提升建模数据的质量;最后,针对烧结过程数据驱动模型缺乏机理认知致使模型预测精度不高的问题,将过程数据中提取得到的FeO含量等级知识与AW-ESN (Adaptive weight echo state network)结合,建立DK-AWESN模型,有效提升复杂工况下FeO含量的预测精度.现场工业数据试验表明,所提方法能实时准确地预测烧结过程FeO含量,为烧结过程的智能化调控提供实时有效的FeO含量反馈信息.展开更多
The iron oxide(FeO)content had a significant impact on both the metallurgical properties of sintered ores and the economic indicators of the sintering process.Precisely predicting FeO content possessed substantial pot...The iron oxide(FeO)content had a significant impact on both the metallurgical properties of sintered ores and the economic indicators of the sintering process.Precisely predicting FeO content possessed substantial potential for enhancing the quality of sintered ore and optimizing the sintering process.A multi-model integrated prediction framework for FeO content during the iron ore sintering process was presented.By applying the affinity propagation clustering algorithm,different working conditions were efficiently classified and the support vector machine algorithm was utilized to identify these conditions.Comparison of several models under different working conditions was carried out.The regression prediction model characterized by high precision and robust stability was selected.The model was integrated into the comprehensive multi-model framework.The precision,reliability and credibility of the model were validated through actual production data,yielding an impressive accuracy of 94.57%and a minimal absolute error of 0.13 in FeO content prediction.The real-time prediction of FeO content provided excellent guidance for on-site sinter production.展开更多
文摘High-temperature experiments were carried out for the slag systems of“FeO”−SiO_(2)−CaO−Al_(2)O_(3)and“FeO”−SiO_(2)−CaO−MgO at 1200℃and p(O_(2))of 10^(−7)kPa.The equilibrated samples were quenched,and the phase compositions were measured by electron probe microanalysis(EPMA).A series of pseudo-ternary and pseudo-binary phase diagrams are constructed to demonstrate their applications in copper smelting process and evaluation of the thermodynamic database.Spinel and tridymite are identified to be the major primary phases in the composition range related to the copper smelting slags.It is found that the operating window of the smelting slag is primarily determined by w_(Fe)/w_(SiO_(2))ratio in the slag.Both MgO and Al_(2)O_(3)in the slag reduce the operating window which requires extra fluxing agent to keep the slag to be fully liquid.Complex spinel solid solutions cause inaccurate predictions of the current thermodynamic database.
文摘为了控制低碳铝镇静钢中Al_(2)O_(3)夹杂物,并提升渣系对Al_(2)O_(3)夹杂物吸附能力,采用FactSage 8.1模拟计算CaO-SiO_(2)-Al_(2)O_(3)-5%MgO-5%FeO渣系的等黏度图和等ΔC/η(ΔC=C_(Al_(2)O_(3))^(s)-C_(Al_(2)O_(3))^(b),η为渣的黏度)值线图。根据模拟计算图选取合适的五元精炼渣做Al_(2)O_(3)的吸附试验,试验研究了Al_(2)O_(3)在CaO-SiO_(2)-Al_(2)O_(3)-5%MgO-5%FeO渣系中的溶解速率,讨论了Al_(2)O_(3)棒浸入深度、直径、转速、渣成分以及温度对Al_(2)O_(3)溶解速率的影响,求解了Al_(2)O_(3)在溶解过程中的活化能。最后,采用场发射扫描电子显微镜(Apreo S HiVac)对氧化铝棒与熔渣接触的界面处进行微区线元素的定性分析。研究结果表明,Al_(2)O_(3)在渣中的溶解速率受诸多因素的影响;溶解速率随氧化铝棒的旋转速度、棒直径、浸入深度和温度的增加而增加;溶解速率也会随着CaO含量的增加而增加,Al_(2)O_(3)和SiO_(2)含量的增加而降低。溶解速率高度依赖于熔渣的黏度,渣的黏度对Al_(2)O_(3)的溶解速率呈负相关,Al_(2)O_(3)的溶解速率与浓度驱动力呈正相关。氧化铝棒溶解于渣系前,会先生成中间相CaO·2Al_(2)O_(3)和CaO·6Al_(2)O_(3),中间相溶解在熔渣中,溶解于渣A中的表观活化能为410.9 kJ/mol。结合溶解速率图与等ΔC/η值线图进行对比,验证了Al_(2)O_(3)在渣中的溶解速率受渣物性的影响。
文摘氧化亚铁(FeO)含量是衡量烧结矿强度和还原性的重要指标,烧结过程FeO含量的实时准确预测对于提升烧结质量、优化烧结工艺具有重要意义.然而烧结过程热状态参数缺失、过程参数波动频繁给FeO含量的高精度预测带来巨大的挑战,为此,提出一种基于知识与变权重回声状态网络融合(Fusion of data-knowledge and adaptive weight echo state network, DK-AWESN)的烧结过程FeO含量预测方法.首先,针对烧结过程热状态参数缺失的问题,建立烧结料层最高温度分布模型,实现基于料层温度分布特征的FeO含量等级划分;其次,针对烧结过程参数波动频繁的问题,提出基于核函数高维映射的多尺度数据配准方法,有效抑制离群点的影响,提升建模数据的质量;最后,针对烧结过程数据驱动模型缺乏机理认知致使模型预测精度不高的问题,将过程数据中提取得到的FeO含量等级知识与AW-ESN (Adaptive weight echo state network)结合,建立DK-AWESN模型,有效提升复杂工况下FeO含量的预测精度.现场工业数据试验表明,所提方法能实时准确地预测烧结过程FeO含量,为烧结过程的智能化调控提供实时有效的FeO含量反馈信息.
文摘将α-Fe_(2)O_(3)@C与钛粉和铝粉一同进行高温煅烧,制备了Fe O@C/MAX(FCM)复合材料。通过XRD、SEM、TEM表征了FCM复合材料在不同Ti/C与Al/C物质的量比下的结构、组成及形貌变化,采用电化学动力学分析方法定量计算了FCM复合材料的赝电容占比,推测可能的电荷储存机理。结果表明,随着Ti/C与Al/C物质的量比的增大,FCM复合材料中MAX相(Ti_(2)Al C和Ti_(3)Al C_(2))的含量随之变化,而α-Fe_(2)O_(3)转变为不稳定的Fe O。当n(Ti)∶n(Al)∶n(C)=3∶1∶2时,制得的FCM-312样品在1 m V/s扫描速率下的比电容最大,为125.09 F/g,约为α-Fe_(2)O_(3)@C的4.76倍。FCM复合材料中部分MAX相在电化学过程中发生氧化还原反应,为离子间电子快速输运提供了条件,增加了FCM复合材料的赝电容占比。其中,FCM-312样品在10 m V/s扫描速率下的赝电容占比为22.12%。
基金the National Natural Science Foundation of China(52174325)the Key Research and Development Program of Shaanxi(Grant Nos.2020GY-166 and 2020GY-247)the Shaanxi Provincial Innovation Capacity Support Plan(Grant No.2023-CX-TD-53).
文摘The iron oxide(FeO)content had a significant impact on both the metallurgical properties of sintered ores and the economic indicators of the sintering process.Precisely predicting FeO content possessed substantial potential for enhancing the quality of sintered ore and optimizing the sintering process.A multi-model integrated prediction framework for FeO content during the iron ore sintering process was presented.By applying the affinity propagation clustering algorithm,different working conditions were efficiently classified and the support vector machine algorithm was utilized to identify these conditions.Comparison of several models under different working conditions was carried out.The regression prediction model characterized by high precision and robust stability was selected.The model was integrated into the comprehensive multi-model framework.The precision,reliability and credibility of the model were validated through actual production data,yielding an impressive accuracy of 94.57%and a minimal absolute error of 0.13 in FeO content prediction.The real-time prediction of FeO content provided excellent guidance for on-site sinter production.
