Abstract：With the solemn commitments of "carbon peaking" and "carbon neutrality", the steel industry, as an energyintensive industry, needs to carry out technological innovation for highcarbon emissions and highenergy consumption ironmaking processes, improve the energyusing structure, and help the smooth implementation of the national lowcarbon development strategy. Firstly, the application status of renewable energy was introduced, as a major resourcebased country, China′s renewable energy such as biomass, solar energy, wind energy, and other reserves are abundant, and have great potential for application in ironmaking processes. Then, the application of renewable energy in lowcarbon ironmaking was analyzed and discussed, and the difficulties of renewable energy in the ironmaking process were analyzed, including low efficiency in energy collection, storage and conversion. Finally, the application prospect of its ironmaking process is prospected, and a promising technical route is proposed, to provide more possibilities and source power for the development of lowcarbon ironmaking technology in China.

Abstract：The hydrogenbased direct reduced iron （HDRI） process is an environmentally friendly and efficient steel production technology with broad application prospects. On the basis of a comprehensive introduction to the hydrogen based direct reduction iron process, the application, advantages and disadvantages, and applicability of the hydrogen based reduction gas composition reforming process in the metallurgical industry were analyzed, mainly including four reforming methods: steam reforming, partial oxidation reforming, self heating reforming, and carbon dioxide reforming. The exploration and practice of China′s hydrogen based direct reduction iron process, as well as the opportunities and challenges it faces were explored. It was pointed out that the technology of coke oven gas self thermal reforming to produce reduction gas would play an important role in China′s hydrogen based direct reduction iron in the future.

Abstract： In order to further increase the amount of coal injection and reduce the coke ratio, and achieve cost decrease and efficiency increase of the enterprise, Shougang Jingtang United Iron and Steel Co., Ltd. carried out research on high oxygenenriched blast smelting technology for blast furnace in combination with its own production situation. Experimental tests and numerical simulation were used to carry out research on the influence of changes in the oxygen content in the blast and oxygencoal lance on the state of the raceway and the burnout rate of pulverized coal. After the oxygen enrichment rate increases from 7% to 11%, the pulverized coal burnout rate of the two coal blending schemes increased from 77.10% to 82.71% and 78.43% to 82.62%, respectively, under the same coal ratio, but the burnout rate decreases with the increase of the coal ratio. After the oxygen flow rate in the oxygencoal lance increases from 3000m3/h to 6000m3/h, the maximum temperature along the direction of the pulverized coal stream increases from 2762K to 2802K, and the burnout rate of pulverized coal increases from 72.31% to 75.12%. Simultaneously, adopting the above two oxygenenrichment methods can realize largescale coal injection and highoxygenenrichment injection, achieve the expected goal of coke reduction, and obtain good economic benefits.

Abstract：The silicon content in molten iron during the ironmaking process in a blast furnace has been an important indicator of the furnace′s thermal state for a long time. However, predicting the silicon content in realtime is extremely difficult due to the dynamic nature of the blast furnace and the complex internal chemical reactions that occur within it. To address this issue, a GABP neural network for predicting the silicon content in molten iron by improving the traditional BP algorithm using genetic algorithms (GA) was proposed. Firstly, feature extraction is performed on 13 parameters (such as air volume, air pressure, etc.) during the blast furnace ironmaking process. A genetic algorithm is used to globally search for the optimal initial weights and thresholds of the BP neural network. Then, the forward propagation algorithm (FP) is used to transmit the selected features in the threelayer neural network and calculate the predicted values. The number of neurons in each layer of the threelayer neural network is 7, 50, and 1, respectively. Finally, an error analysis was conducted between the predicted silicon content in molten iron and that of the actual value, using the principle of Gradient Descent (GD) to continuously update the weights of neural network until the error between the predicted value and the actual value reached the given threshold. Compared to traditional BP neural networks, GA-BP neural networks improve the shortcomings of BP neural networks, such as difficult to determine weights and thresholds, slow learning speed, and easy to fall into local optima. After preprocessing the realtime data collected during the production process of a certain steel plant, it is input into a neural network for training and the accuracy of the model is verified using a test set. In the end, the model achieved an accuracy of 92% on the test set and a stable Mean Square Error (MSE) of 0001, proving the effectiveness of the model. New data outside of 50 datasets for prediction were selected, and the results verified that the model has the ability to guide production practice.

Abstract： Compressive strength is an important indicator for evaluating the quality of pellet ore, and it is also the core control objective of pellet production. However, its detection cycle is long and the control is seriously lagging behind. Therefore, accurate realtime prediction of pellet compressive strength is of great significance for improving and stabilizing pellet quality. A prediction method of pellet compressive strength based on Filter and Wrapper mixed feature parameter selection combined with Bayesian optimization algorithm was proposed. Field production data was used to train and test the model. The prediction results show that: feature selection and the introduction of Bayesian optimization algorithm can significantly improve the prediction accuracy of the model. The gradient Boosting decision tree (GBDT) model based on feature selection and Bayesian optimization has the best fitting effect, with the prediction accuracy of 95.31%, laying a good foundation for the optimization and control of pellet quality.

Abstract： Biomass feedstock, known for its carbonneutral properties, can significantly reduce CO2 emissions in steel production when used in blast furnace (BF) smelting. The effect of pyrolytic and hydrothermal carbonization parameters on the BF injection performance of biochar was studied. Both carbonization methods effectively eliminate volatiles from biomass, enhancing biochar quality. However, pyrolysis carbonization causes ash enrichment, while hydrothermal carbonization can remove ash simultaneously, resulting in lower yields of pyrolysis carbonization compared to hydrothermal carbonization. The ignition point of biomass pyrolysis carbon and hydrothermal carbon is relatively low, among which hydrothermal carbon has strong explosiveness and pyrolysis carbon has no explosiveness. The combustion performance and grindability of biomass pyrolysis carbon and hydrothermal carbon are better than those of blast furnace injection bituminous coal. The analysis of harmful elements shows that the alkali metal content of hydrothermal carbon is much lower than that of pyrolysis carbon. For forest biomass residues, both carbonization methods can be used for blast furnace injection production, but the injection amount of pyrolysis carbon needs to be controlled; for straw biomass residues, hydrothermal carbonization is preferred to reduce the negative impact of alkali metals on blast furnace smelting.

Abstract： There are significant velocity gradients, temperature gradients, and sulfur concentration gradients in the molten steel at the solidification front of the slab continuous casting mold, which have a significant impact on the capture of bubbles and inclusions in the solidified shell. The velocity, temperature, and sulfur content distribution of molten steel at narrow solidification front at different heights in slab continuous casting mold using ANSYS Fluent was studied. Three crosssections parallel to the Xdirection located at the center of the crystallizer at a distance of 0.1, 0.3, and 0.6 meters from the free liquid surface of the mold represent the upper, impact, and lower regions of the narrow solidification front of the mold. The velocity gradient, temperature gradient, and sulfur content gradient of the steel in the Xdirection at a distance of 3mm from the solidification front on each line were calculated and analyzed. The results show that the velocity gradient of molten steel in the lower region is the highest (14.5s-1), slightly higher than that in the upper region (14.1s-1), while that in the impact region is the smallest (0.4s-1); the temperature gradient of the molten steel in the lower region is the highest (902.3K/m), which is about 2.0 times than that of the upper region and 2.4 times than that of the impact region. The sulfur content gradient in the upper region is the highest (0.42%/m), followed by the lower region (0.04%/m), and the sulfur content gradient in the impact region is the smallest (0.02%/m). The distribution of sulfur content in the molten steel has two forms of variation with increasing distance from the solidification front in the three regions: first increase and then decrease to the initial content value, and first decrease, then increase, and finally decrease to the initial content value.

Abstract： After adding different contents of Al elements (2.46wt.%, 3.05wt.%, 4.24wt.%) and readjusting the composition on the basis of 310S heatresistant steel to obtain the heatresistant steel cast sheet, it was hot rolled at 1200℃ with the deformation amount of 60%, and solid solution processed at 1150℃ for 30min to obtain the heatresistant steel hot rolled sheet. The microstructure and microstructure of the hot rolled sheets with different Al contents were analyzed, and their room temperature tensile properties and 800℃ tensile properties were tested. The results show that: with the increase of Al content, the grain size decreases gradually, more Al elements in the matrix solid solution, fine grain strengthening, solid solution strengthening, M7C3 carbide and fine diffuse NiAl phase precipitation strengthening of the joint effect of the heatresistant steel room temperature hardness, strength increases, while the elongation decreases; 800℃ with the increase of Al content, the strength increases and then decreases, and the elongation is about 16%.

Abstract：The influence of longterm thermal exposure up to 3000h at 800℃ on the microstructure evolution and creep behavior of the IN792LC single crystal superalloy was investigated. The results reveal the uniform dispersion of the spheroidal growth of the γ′ phase within the γ matrix after prolonged thermal exposure. The coarsening behavior of the γ′ phase conforming to the LifshitzSlyozovWagner (LSW) model. The discontinuous carbides were precipitated around the eutectic after thermal exposure, but no topological closepacked (TCP) phase was generated. The creep tests were carried out at 760℃/662MPa after longterm thermal exposure, the creep life of superalloy decreased due to the tissue degradation while the facture elongation increased as the thermal exposure time increased. Specifically, the creep fracture life dropped by 81.1% after 3000h of thermal exposure. The reduction in creep life was mainly attributed to the coarsening of the γ′ phase, degradation of the carbide, and increased creep porosity. Additionally, the decomposed MC carbide transformed into the M23C6 carbide, which collapsed and gathered into micropores promote crack expansion. Furthermore, the dislocation network during creep was disrupted under thermal exposure and the dislocations cut through the γ′ phase movement, which further reducing the creep fracture life. The results provide a reference for the service life prediction of IN792LC single crystal superalloy at service temperature.

Abstract： The hot forming behavior of 654SMO super austenitic stainless steel was researched. The Arrhenius model and PSO-BP neural network model were used to predict the hot deformation behavior of 654SMO super austenitic stainless steel, and the results were compared to select the optimal model. In addition, the deformation stress was obtained through experiments at temperatures ranging from 1000℃ to 1200℃ and strain rates ranging from 01 to 10s-1. The Arrhenius model considering strain correction and the PSO-BP neural network model were used to train the experimental data. The predicted results were quantified and compared by calculating the mean square correlation coefficient (R2), mean square error (RMSE) and average relative error (AARE). Finally, hot working diagrams with strain of 0.3 and 0.6 were created based on the current experimental data and the predicted data of the PSOBP model. The prediction results show that the constructed PSO-BP neural network has a higher accuracy and applicability than the Arrhenius model and can provide theoretical clues for the hot working process of 654SMO.

Abstract： The influence of hydrogen on the ultralow cycle fatigue performance and mechanical behavior of 30CrMo steel was investigated in this study. The experimental steel was subjected to electrochemical hydrogen charging in a 0.5 mol/L H2SO4 solution for 1 and 4 h, respectively, subsequently, ultralow cycle fatigue tests and tensile tests were carried out under simulated earthquake loadcontrolled axial by displacement to obtain cyclic stressstrain responses, hysteresis behavior, and strainlife relationships. Combining with scanning electron microscope (SEM), fracture surface morphology was observed to analyze the ultralow cycle fatigue fracture mechanism. The results show that the tensile strength of the experimental steel increases from 681MPa to 689 and 698MPa after 1 and 4h hydrogen filling; the fracture elongation has decreased from 33% to 29% and 19%, and the reduction in ductility becomes more severe with higher hydrogen concentration. Electrochemical hydrogen charging significantly weakens the fatigue resistance of the experimental steel. The fatigue life of the test material decreased by 33.8% to 40.7% and 65.3% to 69.8% after precharging with hydrogen for 1 and 4h, respectively. Hydrogen charging had no significant impact on the cyclic response characteristics of the experimental steel in relation to Masing behavior. Prolonged hydrogen charging reduced the crack propagation zone area and accelerated the crack propagation rate of the 30CrMo steel.

Abstract： In order to improve the strength of lowcarbon highalloy steels, the influence of deformation of thermomechanical treatment on microstructure evolution and mechanical properties was studied. The results show that the microstructures of the steel after processing by thermomechanical treatment with deformation in the range of 20%-60%, cryogenic treatment and tempering contain the complex phases with tempered martensite, retained austenite and a few carbides. Compared with the conventional quenching and tempering sample, the strength of steel decreases with the elongation increase due to the thicker tempered martensite and a higher fraction of retained austenite after the thermomechanical treatment with the deformation of 20%. With deformation increasing from 20% to 60%, the dislocation density increases, the width of tempered martensite lath and the fraction of retained austenite decreases simultaneously, which leads to an improvement in the strength and a decrease in ductility. When the deformation reaches to 60%, the yield strength is higher by about 13% than that of the undeformed quenchingtempering specimen, and the yield strength, ultimate tensile strength and elongation of the steel are 1547MPa, 1810MPa and 17%, respectively, achieving a good strengthductility matching.

Abstract： The effects of the relative contents of Cu and Ni on the microstructure and second phase precipitation behavior of copperbearing aged steel with the same Cu/Ni ratio were investigated. SEM, TEM, EBSD, XRD and other analytical characterization methods were used to analyze the microstructure and mechanical properties of low carbon microalloyed high strength steel with different Cu and Ni contents under quenching and tempering treatment. The results show that the strength and toughness of the test steel are improved with the increase of Cu and Ni content (tensile strength increased by 3.3%, impact toughness increased by 10%). In the microstructure, some granular bainite is transformed into tempered sorbite (the proportion of tempered sorbite increased by 10%), the content of largeangle grain boundaries increases, and the substructure and dislocation density improve significantly. It is found that the austenite phase transition temperature decreases with the increase of Ni content by ThermoCalc thermodynamic calculation. The addition of Ni element refines the tempered sorbite lath (from 332nm to 264nm) and increases the lath bundles in the structure. A large number of dislocation entanglement in the slat boundary provides more nucleation sites for the second phase precipitation, reduces the form energy of Cu precipitation, promotes and refines Cu precipitation, and then improves the strength and toughness of microalloyed high strength steels.