Search Results (46)

With the gradual depletion of high-quality iron ore resources, global steel enterprises have shifted their focus to low-grade, high-impurity iron ores. Using low-grade iron ore to produce pellets for blast furnaces is crucial for companies to control production costs and diversify raw material sources. However, producing qualified pellets from limonite and other low-grade iron ores remains highly challenging. This study investigates the mechanism by which basicity affects the consolidation and reduction behavior of high-Al₂O₃ limonite pellets from a thermodynamic perspective. As the binary basicity of the pellets increased from 0.01 under natural conditions to 1.2, the compressive strength of the roasted pellets increased from 1100 N/P to 5200 N/P. The enhancement in basicity led to an increase in the amount of low-melting-point calcium ferrite in the binding phase, which increased the liquid phase in the pellets, thereby strengthening the consolidation. CaO infiltrated into large-sized iron particles and reacted with Al and Si elements, segregating the contiguous large-sized iron particles and encapsulating them with liquid-phase calcium ferrite. Calcium oxide reacts with the Al and Si elements in large hematite particles, segmenting them and forming liquid calcium ferrite that encapsulates the particles. Additionally, this study used thermodynamic analysis to characterize the influence of CaO on aluminum elements in high-aluminum iron ore pellets. Adding CaO boosted the liquid phase’s ability to incorporate aluminum, lessening the inhibition by high-melting-point aluminum elements of hematite recrystallization. During the reduction process, pellets with high basicity exhibited superior reduction performance. Full article

The efficient simultaneous removal of NO_x and CO from sintering flue gas under low-temperature conditions (110–180 °C) in iron and steel enterprises remains a significant challenge in the field of environmental catalysis. In this study, we present an innovative strategy to enhance the performance of CuSmTi catalysts through silver modification, yielding a bifunctional system capable of oxygen structure regulation and demonstrating superior activity for the combined NH₃-SCR and CO oxidation reactions under low-temperature, oxygen-rich conditions. The modified AgCuSmTi catalyst achieves complete NO conversion at 150 °C, representing a 50 °C reduction compared to the unmodified CuSmTi catalyst (T_100% = 200 °C). Moreover, the catalyst exhibits over 90% N₂ selectivity across a broad temperature range of 150–300 °C, while achieving full CO oxidation at 175 °C. A series of characterization techniques, including XRD, Raman spectroscopy, N₂ adsorption, XPS, and O₂-TPD, were employed to elucidate the Ag-Cu interaction. These modifications effectively optimize the surface physical structure, modulate the distribution of acid sites, increase the proportion of Lewis acid sites, and enhance the activity of lattice oxygen species. As a result, they effectively promote the adsorption and activation of reactants, as well as electron transfer between active species, thereby significantly enhancing the low-temperature performance of the catalyst. Furthermore, in situ DRIFTS investigations reveal the reaction mechanisms involved in NH₃-SCR and CO oxidation over the Ag-modified CuSmTi catalyst. The NH₃-SCR process predominantly follows the L-H mechanism, with partial contribution from the E-R mechanism, whereas CO oxidation proceeds via the MvK mechanism. This work demonstrates that Ag modification is an effective approach for enhancing the low-temperature performance of CuSmTi-based catalysts, offering a promising technical solution for the simultaneous control of NO_x and CO emissions in industrial flue gases. Full article

Iron ore sintering is a critical process in steelmaking, where the produced sinter is the main raw material for blast furnace ironmaking. The quality and yield of sinter ore directly affect the cost and efficiency of iron and steel production. Accurately predicting the burn-through point (BTP) temperature is of paramount importance for controlling quality and yield. Traditional BTP temperature prediction only utilizes data from bellows, neglecting the information contained in sinter images. This study combines color temperature information extracted from the cross-sectional frame at the discharge end with bellows data. Due to the non-stationarity of the BTP temperature, a hybrid prediction model of the BTP temperature integrating bidirectional long short-term memory and extreme gradient boosting is presented. By combining the advantages of deep learning and tree ensemble learning, a hybrid prediction model of the BTP temperature is established using the color temperature information in the cross-sectional frame at the discharge end and time-series data. Experiments were conducted with the actual running data in an iron and steel enterprise and show that the proposed method has higher accuracy than existing methods, achieving an approximately 4.3% improvement in prediction accuracy. The proposed method can provide an effective reference for decision-making and for the optimization of operating parameters in the sintering process. Full article

The traditional power load forecasting learning method has problems such as overfitting and incomplete learning of time series information when dealing with complex nonlinear data, which affects the accuracy of short–medium term power load forecasting. A joint learning method, LSVM-MKL, was proposed based on the bidirectional promotion of deep kernel learning (DKL) and multiple kernel learning (MKL). The multi-kernel method was combined with the input layer, the highest coding layer, and the highest encoding layer to model the network of the stack autoencoder (SAE) to obtain more comprehensive information. At the same time, the deep kernel was integrated into the optimization training of Gaussian multi-kernel by means of the nonlinear product to form the nonlinear composite kernel. Through a large number of reference datasets and actual industrial data experiments, it was shown that compared with the Elman and LSTM-Seq2Seq methods, the proposed method achieved a higher prediction accuracy of 4.32%, which verified its adaptability to complex time-varying power load forecasting processes and greatly improved the accuracy of power load forecasting. Full article

Fully harnessing the inherent flexible adjustment potential of steel enterprises and fostering their interaction with the power grid is a crucial pathway to advancing green transformation. However, traditional research usually takes reducing energy consumption as the optimization goal, which limits the adjustment response capability, or ignores the storage and conversion constraints of secondary energy sources such as gas, steam, and electricity, making it difficult to fully explore and reasonably utilize the potential of multi-energy coordination. This study considers the production constraints of the surplus energy recovery and utilization system, establishes a collaborative scheduling model for a gas–steam–power system (GSPS) in an iron and steel enterprise, and proposes a demand response strategy that considers internal production constraints. Considering the time-of-use (TOU) tariff, iron and steel enterprises achieve a dynamic optimization adjustment range of electricity demand response through the conversion and storage process of gas, steam, and power. The adjustment capability of the GSPS reaches 26.94% of the initial electricity load, while reducing the total system energy cost by 2.24%. There is vast development potential of iron and steel enterprises participating in electricity demand response for promoting cost reduction and efficiency improvement, as well as enhancing the power grid flexibility. Full article

In energy conservation and low-carbon environmental protection, separating and capturing CO₂ from blast furnace gas is a crucial strategy for the steel industry to achieve its dual carbon goals. This study conducts an experimental study on the phase change absorption of carbon dioxide for the low-energy capture of carbon dioxide in blast furnace gas in iron and steel enterprises. The experiment used 30%wt monoethanolamine (MEA) and 30%wt 1-(2-aminoethyl)piperazine (AEP) as a reference to blend different absorbents, and the CO₂ absorption effect of the absorbents was tested. The results indicated that the MEA system phase change absorbents have the best absorption effect when the mass ratio of additives to water is 5:5, and the AEP system has the best absorption effect at 7:3. The absorption effect of different phase separators is as follows: n-propanol > sulfolane > isopropanol. AEP/n-propanol/H₂O (7:3) has a maximum absorption load of 2.03 molCO₂·mol⁻¹ amine, a relatively low rich phase ratio of 0.46, and low regeneration energy consumption. The load capacity of different absorbents was calculated based on the load experiment results, and it was found that the loading capacity of the MEA system was greater than that of the AEP system, with the maximum load capacity of MEA/n-propanol/H₂O (5:5) being 4.02 mol/L. Different types of absorbents exhibited an increase in rich phase density with the increase in additive quality. The regeneration performance of the absorbent indicated that at a temperature of 393.15 K, the desorption load of n-propanol aqueous solution rich phase in the absorbent was high, and the desorption speed was the fastest. Full article

In this study, the effects of using different scrap ratios in a converter on carbon emissions were analyzed based on life cycle assessment (LCA) theory, and the carbon emissions from the converter were evaluated with the use of coke and biochar as heating agents at high scrap ratios. In this industrial experiment, the CO₂ emissions during the converter smelting process decreased with the increase in the scrap steel ratio. For every 1% increase in the scrap steel ratio, the carbon emissions during the steelmaking process decreased by 14.09 kgCO₂/t steel. Based on statistical data for the actual use of a charcoal heating agent in the converter, the relationship between the utilization coefficient of the heating agent and the scrap ratio was calculated as $\eta =7.698\times {10}^2{x}^{-2.596}.$ When biochar was used as a converter heating agent, the scrap ratio required to achieve the lowest carbon emissions was 36%, and the converter emissions could be reduced by 172 kgCO₂/t·steel relative to the use of coke. The use of biochar as a converter heating agent can contribute to the elimination of 330 million tons of scrap through furnace–converter long-process steelmaking, yielding an annual reduction in CO₂ emissions of 158 million tons. Full article

Steel metallurgical dust, characterized by a substantial output, minute particle size, and intricate composition, poses a considerable risk of environmental contamination while simultaneously embodying an exceptionally high potential for recycling. To achieve its resource utilization, chemical analysis, particle size analysis, X-ray diffraction (XRD), scanning electron microscopy (SEM), Mössbauer spectroscopy, and water leaching methods were employed to investigate the chemical compositions, particle size distributions, phase compositions, and microscopic morphologies of blast furnace bag dust, sintering dust, converter fine dust, and electric arc furnace dust from steel plants. The results indicate that the four types of dust have extremely fine particle sizes, with the main distribution range of particle size being less than 100 μm. The main constituent element is Fe (19–56%), and it also contains Zn (1.4–33.5%), Pb, K, C, and other valuable elements. Alkali metals in blast furnace bag dust and sintering machine head dust existed mainly in the form of chloride. The zinc phases in sintering machine head dust and converter fine dust were ZnFe₂O₄, and the zinc phases in blast furnace bag dust were ZnCl₂ and ZnFe₂O₄. Zinc in electric furnace dust was composed of ZnO and ZnFe₂O₄, accounting for 70.31% and 23.12%, respectively. There are significant differences in the types and contents of valuable elements among various dusts, making it difficult to achieve full-scale recovery through a single process. In view of this, a process of “in-plant recycling of harmless dusts—collaborative treatment of harmful dusts” has been proposed. Based on the characteristics of metallurgical dusts, multiple processes are used for collaborative treatment (using hydrometallurgical and pyrometallurgical methods), which can not only directly recover iron resources from dusts within the plant, but also avoid the waste of valuable elements such as Zn, Pb, K, Na, etc. It is hoped that the above work can provide a reference for steel enterprises to achieve full-scale and high value-added treatment of metallurgical dusts. Full article

To evaluate the effectiveness of the low-carbon innovation development of enterprises, this paper proposes a neutral dynamic network cross-efficiency model and introduces the bootstrap sampling method to correct the model. The model categorizes the low-carbon green innovation R&D activities of enterprises into two distinct stages, as follows: the green R&D stage and the results transformation stage. It then assesses the efficiency of each stage and provides an overall efficiency rating. The model has been applied to a sample of listed Chinese iron and steel enterprises (CISES). The results of the study show that the overall efficiency of low-carbon innovation and development of CISES is on the low side, with the highest efficiency achieved in the green R&D stage, which is less than the lowest efficiency attained in the transformation stage, and most of the enterprises are in the stage of high green R&D and low transformation of the results. The ability of marketization of the R&D results still needs to be strengthened. Full article

The iron and steel industry is energy-intensive due to the large volume of steel produced and its high-temperature and high-weight characteristics, sensors such as high-temperature application sensors can be utilized to collect production data and support the process control and optimization. Steelmaking-refining-continuous casting (SRCC) is a bottleneck in the iron and steel production process. SRCC scheduling problems are worldwide problems and NP-hard. The problems are not only important for iron and steel enterprises to enhance production efficiency, but also play a significant role in saving energy and reducing resource consumption. SRCC scheduling problems can be modeled as hybrid flowshop scheduling problems with batch production at the last stage. In this paper, a Discrete Brain Storm Optimization (DBSO) algorithm is proposed to handle SRCC scheduling problems. In the proposed DBSO, population initialization and cluster center replacement are specially designed to enhance the intensification abilities. Moreover, a perturbation operator is devised to enhance its diversification abilities. Furthermore, a new individual generation operator is devised to improve the intensification and diversification abilities simultaneously. Experimental results have demonstrated that the proposed DBSO is an efficient method for solving SRCC scheduling problems. Full article

Polygeneration systems have significant potential for energy conservation and emission reduction and can effectively promote green and low-carbon development in energy-intensive industries, such as the iron and steel industry. However, its application faces the difficulty in technology selection under multiple objectives simultaneously, which is to determine the technology portfolio to achieve the synergy of energy conservation goals and air pollutant emission reduction goals, as well as ensure the economic benefits of the enterprises. This study investigated a case polygeneration system where the iron and steel plant are the core with four polygeneration paths and twenty polygeneration technologies. A multi-objective optimization model is developed to select the optimal technology combination of each polygeneration path under energy conservation, emission reduction, and cost control objectives, which is solved by the non-dominated sorting genetic algorithm-II (NSGA-II). The optimal results can reach significant energy conservation and emission reduction effects while obtaining economic benefits. However, synergistic and conflicting relationships among the objectives exist in both scales of iron and steel plants. The final decision scheme can achieve the mitigations equivalent to 15.9–27.1% and 16.3–42.6% of the energy consumption and air pollutant emissions of the steel enterprises with annual production of 3 Mt/a and 9 Mt/a, respectively. There are thirteen and twelve technologies that are selected as the final decision scheme in the polygeneration system in these two case enterprises. These findings demonstrate the significant roles the polygeneration system plays and provide critical insights and methodology in the technical selection of the polygeneration system. Full article

Scrap steel serves as the primary alternative raw material to iron ore, exerting a significant impact on production costs for steel enterprises. With the annual growth in scrap resources, concerns regarding traditional manual inspection methods, including issues of fairness and safety, gain increasing prominence. Enhancing scrap inspection processes through digital technology is imperative. In response to these concerns, we developed CNIL-Net, a scrap-quality inspection network model based on object detection, and trained and validated it using images obtained during the scrap inspection process. Initially, we deployed a multi-camera integrated system at a steel plant for acquiring scrap images of diverse types, which were subsequently annotated and employed for constructing an enhanced scrap dataset. Then, we enhanced the YOLOv5 model to improve the detection of small-target scraps in inspection scenarios. This was achieved by adding a small-object detection layer (P2) and streamlining the model through the removal of detection layer P5, resulting in the development of a novel three-layer detection network structure termed the Improved Layer (IL) model. A Coordinate Attention mechanism was incorporated into the network to dynamically learn feature weights from various positions, thereby improving the discernment of scrap features. Substituting the traditional non-maximum suppression algorithm (NMS) with Soft-NMS enhanced detection accuracy in dense and overlapping scrap scenarios, thereby mitigating instances of missed detections. Finally, the model underwent training and validation utilizing the augmented dataset of scraps. Throughout this phase, assessments encompassed metrics like mAP, number of network layers, parameters, and inference duration. Experimental findings illustrate that the developed CNIL-Net scrap-quality inspection network model boosted the average precision across all categories from 88.8% to 96.5%. Compared to manual inspection, it demonstrates notable advantages in accuracy and detection speed, rendering it well suited for real-world deployment and addressing issues in scrap inspection like real-time processing and fairness. Full article

Improving the carbon emission efficiency (CEE) of the iron and steel industry (ISI) is crucial for China to achieve the goal of carbon peak and carbon neutrality. This study employed the undesirable SBM and Dagum Gini coefficient to measure the ISI’s CEE and analyzed the spatial heterogeneity among three regions of China. This study also used the Tobit model to clarify the influencing factors. The conclusions show that (1) the CEE in eastern provinces is the highest, the central ones rank second, while the western ones rank the worst; the promoting effect of Technical Change is greater than that of Efficiency Change. (2) ISI’s CEE shows a positive spatial correlation and an apparent spatial heterogeneity. The CEE gap between the regions contributes most to the CEE difference among provinces. The regional CEE gap within the western region is the largest, with a maximum difference of 0.520 in the Dagum Gini coefficient. Furthermore, the total CEE gap shows a narrowing trend from 2009 to 2020, with the Dagum Gini coefficient decreasing from 0.414 in 2009 to 0.357 in 2020. (3) Industrial structure, enterprise scale, foreign direct investment, and technology level positively correlate with ISI’s CEE; the marginal impacts are 0.6711, 0.1203, 0.0572, and 3.5191, respectively. While energy intensity, environmental regulation, and product structure negatively correlate with it, the marginal impacts are 0.0178, 1.4673, and 0.2452, respectively. Full article

This paper establishes a multi-objective optimization model based on an improved NSGA-II algorithm, aiming to study the carbon reduction technology path of specific enterprises in the steel industry under the background of China’s dual-carbon goal and fill the research gap in the carbon reduction technology path of steel enterprises, which has certain guiding significance for the realization of China’s dual-carbon goal and the low-carbon development of steel enterprises. Firstly, through the analysis of the list of extreme energy efficiency technologies in the steel industry and the main process flow of steel industry production, the multi-objective optimization model is constructed from the two objective dimensions of maximum CO₂ emission reduction and maximum enterprise economic benefit. Then the improved NSGA-II algorithm is used to solve the model. And the empirical analysis of a Hebei iron and steel enterprise, based on the technology application of enterprises before the release of the technology list, the technology path of enterprises to reduce carbon is predicted. The actual application data of the enterprise is used for verification and analysis, and suggestions on the technical path for the future low-carbon development of the enterprise are provided. The experimental results show that: (1) The optimal solution set of Pareto is consistent with the practical application of enterprises, and the constructed model is accurate and efficient, which can be used for the research of carbon reduction technology path. (2) When introducing technology, enterprises can give priority to the solution of common set technology based on their own needs. Full article