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
FeP/FeO was prepared on carbon cloth(CC)via hydrothermal method,heat treatment in air,and phosphorization in argon.FeP/FeO/CC presents a porous and loose morphology which is conducive to the exposure of active sites a...FeP/FeO was prepared on carbon cloth(CC)via hydrothermal method,heat treatment in air,and phosphorization in argon.FeP/FeO/CC presents a porous and loose morphology which is conducive to the exposure of active sites and the transfer of reactants.FeP/FeO/CC requires the low overpotentials of 257 and 117 mV(vs.reversible hydrogen electrode(RHE))to achieve the current density of 10 mA_·cm^(-2)for oxygen evolution reaction(OER)and hydrogen evolution reaction(HER)in alkaline KOH solution,respectively.The small Tafel slope values of 36.1 mV_·dec^(-1)(for OER)and 96.2 mV_·dec^(-1)(for HER)indicate that FeP/FeO/CC exhibits the fast electrocatalytic reactive kinetics for OER and HER.In particular,the reaction kinetics of FeP/FeO/CC accelerated with the progress of HER.The charge-transfer resistance(R_(ct))of FeP/FeO/CC is only 11Ω.Excellent bifunctional electrocatalytic performances of FeP/FeO/CC should be attributed to the porous morphology and the lower charge-transfer resistance.展开更多
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Funded by the Natural Science Foundation of Anhui Higher Education Institution of China(No.2023AH040160)the Natural Science Foundation of Anhui Province(No.1808085QE126)+1 种基金the Hefei Normal University High level Talent Research Startup Fund Project(No.2022rcjj55)the Collaborative Innovation Project of Colleges and Universities in Anhui Province(No.GXXT-2021-091)。
文摘FeP/FeO was prepared on carbon cloth(CC)via hydrothermal method,heat treatment in air,and phosphorization in argon.FeP/FeO/CC presents a porous and loose morphology which is conducive to the exposure of active sites and the transfer of reactants.FeP/FeO/CC requires the low overpotentials of 257 and 117 mV(vs.reversible hydrogen electrode(RHE))to achieve the current density of 10 mA_·cm^(-2)for oxygen evolution reaction(OER)and hydrogen evolution reaction(HER)in alkaline KOH solution,respectively.The small Tafel slope values of 36.1 mV_·dec^(-1)(for OER)and 96.2 mV_·dec^(-1)(for HER)indicate that FeP/FeO/CC exhibits the fast electrocatalytic reactive kinetics for OER and HER.In particular,the reaction kinetics of FeP/FeO/CC accelerated with the progress of HER.The charge-transfer resistance(R_(ct))of FeP/FeO/CC is only 11Ω.Excellent bifunctional electrocatalytic performances of FeP/FeO/CC should be attributed to the porous morphology and the lower charge-transfer resistance.
