A new anodic material of ternary Pb-0.8%Ag-(0-5.0%)Bi alloy for zinc electrowinning was obtained by doping Bi.The anodic oxygen evolution potential,corrosion rate,surface products after polarization,and microstructure...A new anodic material of ternary Pb-0.8%Ag-(0-5.0%)Bi alloy for zinc electrowinning was obtained by doping Bi.The anodic oxygen evolution potential,corrosion rate,surface products after polarization,and microstructures before and after polarization were studied and compared with those of Pb-0.8%Ag anode used in industry.The results show the anodic overpotential decreases with the increase of Bi content in the alloys.When the content of Bi is 1.0%(mass fraction),the anodic overpotential is 40-50 mV lower than that of Pb-0.8%Ag anode.While the corrosion rate decreases and then increases with the increase of Bi content.The Pb-0.8%Ag-0.1%Bi anode has the lowest corrosion rate(0.090 6 mg/(h·cm2).Doping Bi influences the structure of the anodic layer,but does not change the phase.The Pb-0.8%Ag-1.0%Bi anode layer is of a more fine-grained structure compared with Pb-0.8%Ag anode.展开更多
针对黄河源区水文情势复杂多变、径流模拟精度不足的问题,旨在构建融合潜在蒸散发(PET)预测的径流模拟方法,提升高寒地区径流模拟的可靠性。本研究采用随机森林(RF)、多层感知机(MLP)和极限学习机(ELM)3种机器学习方法,引入长短期记忆网...针对黄河源区水文情势复杂多变、径流模拟精度不足的问题,旨在构建融合潜在蒸散发(PET)预测的径流模拟方法,提升高寒地区径流模拟的可靠性。本研究采用随机森林(RF)、多层感知机(MLP)和极限学习机(ELM)3种机器学习方法,引入长短期记忆网络(LSTM)和PatchTST(Patch Time Series Transformer)深度学习方法,融合PET预测值进行径流模拟,评估不同气象因子组合下PET的模拟性能。研究结果表明:最高气温是PET模拟的最关键驱动因子,最高气温、相对湿度与风速组合情景下的PET模拟精度最高;在深度学习模型中,PatchTST模型在预测未来1个月潜在蒸散发时表现次于LSTM模型,但在多步长预测中表现更优;融合潜在蒸散发预测数据后,模型性能显著提升;以唐乃亥站PatchTST模型为例,纳什效率系数从0.706增至0.896(改进幅度为26.9%),平均绝对百分比误差从23.502降至18.305(降幅为22.1%),均方根误差从276.7降至160.8(降幅为41.9%),表明PET数据有效捕捉了蒸散发对径流损失的动态影响。研究成果可为高寒、缺资料地区的水文预报工作提供更精准的解决方案。展开更多
基金Project(2007SK2009)supported by the Science and Technology Research Project of Hunan Province,China
文摘A new anodic material of ternary Pb-0.8%Ag-(0-5.0%)Bi alloy for zinc electrowinning was obtained by doping Bi.The anodic oxygen evolution potential,corrosion rate,surface products after polarization,and microstructures before and after polarization were studied and compared with those of Pb-0.8%Ag anode used in industry.The results show the anodic overpotential decreases with the increase of Bi content in the alloys.When the content of Bi is 1.0%(mass fraction),the anodic overpotential is 40-50 mV lower than that of Pb-0.8%Ag anode.While the corrosion rate decreases and then increases with the increase of Bi content.The Pb-0.8%Ag-0.1%Bi anode has the lowest corrosion rate(0.090 6 mg/(h·cm2).Doping Bi influences the structure of the anodic layer,but does not change the phase.The Pb-0.8%Ag-1.0%Bi anode layer is of a more fine-grained structure compared with Pb-0.8%Ag anode.
文摘针对黄河源区水文情势复杂多变、径流模拟精度不足的问题,旨在构建融合潜在蒸散发(PET)预测的径流模拟方法,提升高寒地区径流模拟的可靠性。本研究采用随机森林(RF)、多层感知机(MLP)和极限学习机(ELM)3种机器学习方法,引入长短期记忆网络(LSTM)和PatchTST(Patch Time Series Transformer)深度学习方法,融合PET预测值进行径流模拟,评估不同气象因子组合下PET的模拟性能。研究结果表明:最高气温是PET模拟的最关键驱动因子,最高气温、相对湿度与风速组合情景下的PET模拟精度最高;在深度学习模型中,PatchTST模型在预测未来1个月潜在蒸散发时表现次于LSTM模型,但在多步长预测中表现更优;融合潜在蒸散发预测数据后,模型性能显著提升;以唐乃亥站PatchTST模型为例,纳什效率系数从0.706增至0.896(改进幅度为26.9%),平均绝对百分比误差从23.502降至18.305(降幅为22.1%),均方根误差从276.7降至160.8(降幅为41.9%),表明PET数据有效捕捉了蒸散发对径流损失的动态影响。研究成果可为高寒、缺资料地区的水文预报工作提供更精准的解决方案。
