The novel process of hydrogen-based shaft furnaces(HSFs)has attracted considerable attention because of their significant reduction of CO_(2)emissions.In this study,the interaction of H_(2)and CO with Fe_(tet1)-and Fe...The novel process of hydrogen-based shaft furnaces(HSFs)has attracted considerable attention because of their significant reduction of CO_(2)emissions.In this study,the interaction of H_(2)and CO with Fe_(tet1)-and Fe_(oct2)-terminated Fe_(3)O_(4)(111)surfaces under HSF conditions,including their adsorption and reduction behaviors,was investigated using the density functional theory method.The results indicated that the H_(2)molecule adsorbed onto the Fe_(tet1)-terminated surface with an adsorption energy(AE)of-1.36 eV,whereas the CO molecule preferentially adsorbed on the Fe_(oct2)-terminated surface with an AE of-1.56 eV.Both H_(2)and CO can readily undergo reduction on the Fe_(tet1)-terminated surface(corresponding to energy barriers of 0.83 eV and 2.23 eV,respectively),but kinetically the reaction of H2is more favorable than that of CO.With regard to the thermodynamics at 400-1400 K,the H_(2)was easy to be adsorbed,while the CO would like to react on the Fe_(tet1)-terminated surface.These thermodynamically tendencies were reversed on the Fe_(oct2)-terminated surface.The thermodynamic disadvantage of the reaction of H_(2)on the Fe_(tet1)-terminated surface was offset by an increase in the temperature.Furthermore,the adsorption of H2 and CO on the Fe_(tet1)-terminated surface was competitive,whereas the adsorption of them on the Fe_(oct2)-terminated surface was synergistic.Therefore,iron ores with a higher proportion of Fe_(tet1)-terminated surface can be applied for the HSF process.In conjunction with the increases in the reduction temperature and the ratio of H_(2)in the reducing gas would promote efficient HSF smelting.These observations provide effective guidance for optimizing the practical operation parameters and advancing the development of the HSF process.展开更多
Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate ...Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.展开更多
基金financially supported by the Key Program of National Natural Science Foundation of China(No.U23A20608)the Liaoning Province Science and Technology Plan Joint Program(Key Research and Development Program Project),China(No.2023JH2/101800058)+3 种基金the Science&Technology Plan Project of Hebei Province,China(No.23314601L)the Project of Hydrogen-Based Shaft Furnace Reduction-Electric Furnace Melting And Separation Technology Research and Application for High-Titanium Magnetite Iron Ore(No.HG2023239)the General Program of National Natural Science Foundation of China(No.52274253)the Special Project for Major Scientific and Technological Achievements Transformation in Hebei Province,China(No.23284101Z)。
文摘The novel process of hydrogen-based shaft furnaces(HSFs)has attracted considerable attention because of their significant reduction of CO_(2)emissions.In this study,the interaction of H_(2)and CO with Fe_(tet1)-and Fe_(oct2)-terminated Fe_(3)O_(4)(111)surfaces under HSF conditions,including their adsorption and reduction behaviors,was investigated using the density functional theory method.The results indicated that the H_(2)molecule adsorbed onto the Fe_(tet1)-terminated surface with an adsorption energy(AE)of-1.36 eV,whereas the CO molecule preferentially adsorbed on the Fe_(oct2)-terminated surface with an AE of-1.56 eV.Both H_(2)and CO can readily undergo reduction on the Fe_(tet1)-terminated surface(corresponding to energy barriers of 0.83 eV and 2.23 eV,respectively),but kinetically the reaction of H2is more favorable than that of CO.With regard to the thermodynamics at 400-1400 K,the H_(2)was easy to be adsorbed,while the CO would like to react on the Fe_(tet1)-terminated surface.These thermodynamically tendencies were reversed on the Fe_(oct2)-terminated surface.The thermodynamic disadvantage of the reaction of H_(2)on the Fe_(tet1)-terminated surface was offset by an increase in the temperature.Furthermore,the adsorption of H2 and CO on the Fe_(tet1)-terminated surface was competitive,whereas the adsorption of them on the Fe_(oct2)-terminated surface was synergistic.Therefore,iron ores with a higher proportion of Fe_(tet1)-terminated surface can be applied for the HSF process.In conjunction with the increases in the reduction temperature and the ratio of H_(2)in the reducing gas would promote efficient HSF smelting.These observations provide effective guidance for optimizing the practical operation parameters and advancing the development of the HSF process.
基金financial support provided by the Natural Science Foundation of Hebei Province,China(No.E2024105036)the Tangshan Talent Funding Project,China(Nos.B202302007 and A2021110015)+1 种基金the National Natural Science Foundation of China(No.52264042)the Australian Research Council(No.IH230100010)。
文摘Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.
