Search Results (38)

Steel is an important product in many engineering sectors; however, steelmaking remains one of the largest ${\mathrm{CO}}_2$ emitters. Therefore, new governmental policies drive the steelmaking industry toward a cleaner and more sustainable operation such as the gas-based direct reduction–electric arc furnace process. To become carbon neutral, utilizing more scrap is one of the feasible solutions to achieve this goal. Addressing knowledge gaps regarding scrap heterogeneity (size, shape, and composition) is essential to evaluate the effects of increased scrap ratios in basic oxygen furnace (BOF) operations. This review systematically examines heat and mass transfer correlations relevant to scrap melting in BOF steelmaking, with a focus on low Prandtl number fluids (thick thermal boundary layer) and dense particulate systems. Notably, a majority of these correlations are designed for fluids with high Prandtl numbers. Even for the ones tailored for low Prandtl, they lack the introduction of the porosity effect which alters the melting behavior in such high temperature systems. The review is divided into two parts. First, it surveys heat transfer correlations for single elements (rods, spheres, and prisms) under natural and forced convection, emphasizing their role in predicting melting rates and estimating maximum shell size. Second, it introduces three numerical modeling approaches, highlighting that the computational fluid dynamics–discrete element method (CFD–DEM) offers flexibility in modeling diverse scrap geometries and contact interactions while being computationally less demanding than particle-resolved direct numerical simulation (PR-DNS). Nevertheless, the review identifies a critical gap: no current CFD–DEM framework simultaneously captures shell formation (particle growth) and non-isotropic scrap melting (particle shrinkage), underscoring the need for improved multiphase models to enhance BOF operation. Full article

Amid escalating global climate crises and the urgent imperative to meet the Paris Agreement’s carbon neutrality targets, the steel industry—a leading contributor to global greenhouse gas emissions—confronts unprecedented challenges in driving sustainable industrial transformation through innovative low-carbon steelmaking technologies. This paper examines decarbonization technologies across three stages (source, process, and end-of-pipe) for two dominant steel production routes: the long process (BF-BOF) and the short process (EAF). For the BF-BOF route, carbon reduction at the source stage is achieved through high-proportion pellet charging in the blast furnace and high scrap ratio utilization; at the process stage, carbon control is optimized via bottom-blowing O₂-CO₂-CaO composite injection in the converter; and at the end-of-pipe stage, CO₂ recycling and carbon capture are employed to achieve deep decarbonization. In contrast, the EAF route establishes a low-carbon production system by relying on green and efficient electric arc furnaces and hydrogen-based shaft furnaces. At the source stage, energy consumption is reduced through the use of green electricity and advanced equipment; during the process stage, precision smelting is realized through intelligent control systems; and at the end-of-pipe stage, a closed-loop is achieved by combining cascade waste heat recovery and steel slag resource utilization. Across both process routes, hydrogen-based direct reduction and green power-driven EAF technology demonstrate significant emission reduction potential, providing key technical support for the low-carbon transformation of the steel industry. Comparative analysis of industrial applications reveals varying emission reduction efficiencies, economic viability, and implementation challenges across different technical pathways. The study concludes that deep decarbonization of the steel industry requires coordinated policy incentives, technological innovation, and industrial chain collaboration. Accelerating large-scale adoption of low-carbon metallurgical technologies through these synergistic efforts will drive the global steel sector toward sustainable development goals. This study provides a systematic evaluation of current low-carbon steelmaking technologies and outlines practical implementation strategies, contributing to the industry’s decarbonization efforts. Full article

It is essential to evaluate the prospective development trends of coke dry quenching (CDQ) waste heat power generation, to reduce the comprehensive cost of the steelmaking system. Based on TIMES energy system optimization model, this study develops a model of China’s iron and steel production. Three scenarios are established, predictions and comparisons are conducted regarding the iron and steel production structure, total CDQ quantity, CO₂ and pollutant emissions under these scenarios. The findings indicate that: (1) The advancement of hydrogen metallurgy and EAF scrap smelting facilitates a reduction in the quantity of BF-BOF steelmaking and total CDQ consumption. (2) The decreasing demand for CDQ shows that the shift to clean production alters process pathways and compels the energy system from scale-driven to flexibility-focused. (3) The marginal value of the CDQ system is contingent upon the targeted policy support for multi-energy co-generation systems and their deep integration with hydrogen infrastructure. Accordingly, the utilization of CDQ waste heat power generation should be considered as a transitional strategy, it will be imperative to implement a reduction in capacity. Full article

This study introduces an innovative process for stabilizing BOF slag by blowing oxygen into molten slag, addressing challenges associated with conventional methods that require silica injection. Molten BOF slag from a steelmaking workshop at China Steel Corporation is directly modified at the slag modification station, where chemical compositions and crystalline phases are analyzed under varying oxygen injection amounts. In 70 industrial trials (20–25 tons per trial) with the basicity of the BOF slag ranging from 2.2 to 4.5, the reduction in the slag expansion rate increases proportionally with oxygen-blowing amounts. Oxygen blowing facilitates the oxidation of FeO to Fe₂O₃, which reacts with f-CaO to form volumetrically stable C₂AF (brownmillerite, Ca₂Al_xFe_2−xO₅), as confirmed by XRD and SEM-EDX analyses. The treated BOF slag exhibits excellent volumetric stability (expansion < 0.5%), lower pH (10.6–10.8) in comparison to original BOF slag, and compliance with Taiwan’s EPA-leaching regulations. This stabilized slag demonstrates potential for engineering applications, such as pavement bricks, concrete products, and high-value engineered stones. Additionally, the high brownmillerite content highlights its promise for low-carbon cement applications, offering a scalable and cost-effective solution for BOF slag utilization in the steel industry. Full article

This paper will discuss the dynamics of steel production in Poland in light of the forecasts of tendencies under conditions of decarbonization. The research presented will be an attempt, using data from 2006 to 2023, to create econometric models and forecast production volumes until 2028, along with influencing factors. The obtained models were compared by calculating their error metrics. Based on the conducted econometric models, the critical determinants of the decarbonization of the industry have been established. Forecasts of the volume of steel production in Poland are downward in the face of the increasingly clear emphasis on strategic investments in low-emission technologies. This paper consists of two research parts. The first concerns the forecasting of steel production volume, and the second concerns the modeling of the steel production process, taking into account the key determinants of technological processes (EAF and BOF). Forecasts were calculated for each econometric model. This analysis is a contribution to a broader discussion on industrial adaptation and sustainable development in the steel sector. The developed models and forecasts can provide decision-makers and industry stakeholders with important information at the stage of the decision-making process concerned with developing a strategy for the decarbonization of steelmaking processes. In Poland, two technologies of steel production are used: BOF and EAF. In accordance with the assumptions of deep decarbonization, BF-BOF technology must be replaced by DRI-EAF technology. Full article

The BOF steelmaking process involves complex physical and chemical reactions, making precise control challenging when relying solely on human experience. Therefore, understanding the reaction mechanisms and developing simulation models for the BOF process are crucial for enhancing control accuracy and advancing intelligent steelmaking. In this study, the physical and chemical behaviors in various reaction zones were first analyzed under actual production conditions using the multi-zone reaction theory. Then, a comprehensive mechanism model for BOF steelmaking was established, and an integrated simulation of metallurgical reactions during the BOF steelmaking process was performed using FactSage thermodynamic software. Finally, the validity of this comprehensive model was verified through actual production data. The results show that the relative deviation of the cumulative decarburization rate ranges from −0.66% to 1.68%, while the absolute deviation of the calculated carbon content curve compared to the actual curve is less than 0.12%. This research helps clarify the variation patterns of key process parameters in BOF steelmaking, playing a significant role in advancing the intelligence of the BOF steelmaking process. Full article

Greenhouse gas emissions are significant contributors to global warming, and steel enterprises need to find more efficient and environmentally friendly solutions to reduce CO₂ emissions while maintaining high process efficiency and low production costs. Carbon capture and utilization (CCU) is a promising approach which can convert captured CO₂ into valuable chemicals, reducing dependence on fossil fuels and mitigating climate change. This study uses life cycle assessment (LCA) to compare the environmental impacts of BF-BOF steel plants with and without CCU. When evaluating seven scenarios, including three carbon capture and two carbon utilization technologies, against a baseline, the results demonstrate significant environmental benefits from implementing CCU technologies. Although the activated carbon TSA route for CO₂-based methanol production showed good environmental performance, its toxicity risks highlight the advantages of combining TSA with steel slag carbonation as a better non-toxic solution. Full article

Different mixed cultures of extremely thermoacidophilic microorganisms were used for bioleaching of metalliferous industrial dust waste derived from the basic oxygen furnace (BOF) steelmaking process. Such mixed cultures can extract various metals from multi-metallic BOF-dust waste, improving the metal dissolution and bioleaching performance in frames of metal recycling processes to assist circular economies and waste management. The results of the investigation showed that mixed cultures of thermoacidophilic archaea of the order Sulfolobales (Acidianus spp., Sulfolobus spp., and Metallosphaera sedula) during their growth in laboratory glass bioreactors provided a superior bioleaching system to Acidianus manzaensis alone. Depending on the composition of mixed thermoacidophilic cultures, extraction of various metals from BOF-dust could be achieved. Among the three different types of mixed cultures tested, the mixed culture system of A. manzaensis, A. brierleyi, and S. acidocaldarius was most effective for extraction of major elements (Fe, Ca, Zn, Mn, and Al). The mixed culture of A. manzaensis, A. brierleyi, and M. sedula showed high performance for bioleaching of most of the minor elements (Cu, Ni, Pb, Co, Mo, and Sr). The efficient ability of mixed cultures to colonise the mineral matrix of the metal waste product was observed via scanning electron microscopy, while their metal extraction capacities were analysed by inductively coupled plasma mass spectrometry. These investigations will promote the further design of microbial consortia in order to break down the solid matrix and efficiently extract metals from metalliferous waste materials. Full article

The key to endpoint control in basic oxygen furnace (BOF) steelmaking lies in accurately predicting the endpoint carbon content and temperature. However, BOF steelmaking data are complex and change distribution due to variations in raw material batches, process adjustments, and equipment conditions, leading to concept drift and affecting model performance. In order to resolve these problems, this paper proposes a dynamic soft sensor model based on an adaptive feature matching variational autoencoder (VAE-AFM). Firstly, this paper innovatively proposes an adaptive feature matching (AFM) method. This method utilizes the maximum mean discrepancy to calculate the values of the marginal and conditional distributions. Based on the discrepancy between these two values, a dynamic adjustment algorithm is designed to adaptively assign different weights to the two distributions. This approach dynamically and quantitatively evaluates and adjusts the relative importance of different distributions in the domain adaptation process, thereby enhancing the effectiveness of cross-domain data alignment. Secondly, a variational autoencoder (VAE) is employed to process the data, as the VAE model can capture the complex data structures and latent features in the steelmaking process. Finally, the features extracted by the VAE are processed with the adaptive feature matching method, thereby constructing the VAE-AFM dynamic soft sensor model. Experimental studies on actual BOF steelmaking data validate the efficacy of the offered approach, offering a reliable solution to the challenges of high complexity and concept drift in BOF steelmaking data. Full article

The objective of basic oxygen furnace (BOF) steelmaking is to achieve molten steel with final carbon content, temperature, and phosphorus content meeting the requirements. Accurate prediction of the above properties is crucial for end-point control in BOF steelmaking. Traditional prediction models typically use multi-variable input and single-variable output approaches, neglecting the coupling relationships between different property indicators, making it difficult to predict multiple outputs simultaneously. Consequently, a multi-output prediction model based on the fusion of deep convolution and attention mechanism networks (FDCAN) is proposed. The model inputs include scalar data, such as the properties of raw materials and target molten steel, and time series data, such as lance height, oxygen supply intensity, and bottom air supply intensity during the blowing process. The FDCAN model utilizes a fully connected module to extract nonlinear features from scalar data and a deep convolution module to process time series data, capturing high-dimensional feature representations. The attention mechanism then assigns greater weight to significant features. Finally, multiple multi-layer perceptron modules predict the outputs—final carbon content, temperature, and phosphorus content. This structure allows FDCAN to learn complex relationships within the input data and between input and output variables. The effectiveness of the FDCAN model is validated using actual BOF steelmaking data, achieving hit rates of 95.14% for final carbon content within ±0.015 wt%, 84.72% for final temperature within ±15 °C, and 88.89% for final phosphorus content within ±0.005 wt%. Full article

The replacement of the blast furnace—basic oxygen furnace (BF-BOF) steelmaking route with the direct reduced iron—electric arc furnace (DRI-EAF) route reduces the direct CO₂ emissions from steelmaking by up to 68%; however, the DRI shaft furnace is one of the largest remaining point source emitters in steelmaking. The capital and operating expenses of two potential nearly carbon-neutral DRI process configurations were investigated as a modification to a standard Midrex DRI facility. First, amine-based post-combustion capture with a 95% capture rate was considered as the benchmark, as it is currently commercially available. A second, novel configuration integrated the Midrex process with pressurized chemical looping—direct reduced iron (PCL-DRI) production. The capital expenditures were 71% and 28% higher than the standard Midrex process for a Midrex + amine capture plant, and a PCL-DRI plant, respectively. There was an incremental variable operating cost of USD 103 and USD 44 per tonne of CO₂ for DRI production using amine capture and PCL-DRI, respectively. The amine capture configuration is most sensitive to the cost of steam generation, while PCL-DRI is more sensitive to the cost of electricity and the makeup oxygen carrier. An iron-based natural ore is recommended for PCL-DRI due to the low cost and availability. Based on the lower costs compared to amine-based post-combustion capture, PCL-DRI is an attractive means of eliminating CO₂ emissions from DRI production. Full article

Identification of the factors influencing refractory lining wear and its residual thickness in the basic oxygen furnace (BOF) is a prerequisite for optimizing the steelmaking process. In this study, the factors that contribute significantly to the wear of the refractory lining in the most stressed areas of the banded lining (i.e., the trunnion ring area and slag line area) are identified. Knowledge of the rate at which a given factor acts on refractory wear is closely related to the development of technological procedures aimed at limiting its influence. This research evaluates the technological causes and describes the lining wear mechanism and the thermodynamic parameters of the reactions between the MgO–C metal, slag, and gunning material phases. In researching the topic, real operational data were processed using statistical methods and data analysis, which were supported by thermodynamic modeling of chemical reactions. The results show that the combination of technological factors, mechanical action of the raw materials, blowing and free oxygen in the metal, silicon from the pig iron, and slag viscosity have the greatest influence on the residual thickness of the MgO–C refractory lining in BOFs. Refractory gunning material consumption, its effect on campaign length, and the cost-effectiveness of repair work were also analyzed. Full article

Oxygen is an important energy medium in the steelmaking process. The accurate dynamic prediction of oxygen demand is needed to guarantee molten steel quality, improve the production rhythm, and promote the collaborative optimization of production and energy. In this work, a analysis of the mechanism and of industrial big data was undertaken, and we found that the characteristic factors of Basic Oxygen Furnace (BOF) oxygen consumption were different in different modes, such as duplex dephosphorization, duplex decarbonization, and the traditional mode. Based on this, a dynamic-prediction modeling method for BOF oxygen demand considering mode classification is proposed. According to the characteristics of BOF production organization, a control module based on dynamic adaptions of the production plan was researched to realize the recalculation of the model predictions. A simulation test on industrial data revealed that the average relative error of the model in each BOF mode was less than 5% and the mean absolute error was about 450 m³. Moreover, an accurate 30-minute-in-advance prediction of dynamic oxygen demand was realized. This paper provides the method support and basis for the long-term demand planning of the static balance and the short-term real-time scheduling of the dynamic balance of oxygen. Full article

With the continuous optimization of the steel production process and the increasing emergence of smelting methods, it has become difficult to monitor and control the production process using the traditional steel management model. The regulation of steel smelting processes by means of machine learning has become a hot research topic in recent years. In this study, through the data mining and correlation analysis of the main equipment and processes involved in steel transfer, a network algorithm was optimized to solve the problems of standard back propagation (BP) networks, and a steel temperature forecasting model based on improved back propagation (BP) neural networks was established for basic oxygen furnace (BOF) steelmaking, ladle furnace (LF) refining, and Ruhrstahl–Heraeus (RH) refining. The main factors influencing steel temperature were selected through theoretical analysis and heat balance principles; the production data were analyzed; and the neural network was trained and tested using large amounts of field data to predict the end-point steel temperature of basic oxygen furnace (BOF) steelmaking, ladle furnace (LF) refining, and Ruhrstahl–Heraeus (RH) refining. The prediction model was applied to predict the degree of influence of different operating parameters on steel temperature. A comparison of the prediction results with the production data shows that the prediction system has good prediction accuracy, with a hit rate of over 90% for steel temperature deviations within 20 °C. Compared with the traditional steel temperature management model, the prediction system in this paper has higher management efficiency and a faster response time and is more practical and generalizable in the thermal management of steel. Full article

Basic oxygen furnace slag (BOFS) is a waste material generated during the steelmaking process and has the potential to harm both the environment and living organisms when disposed of in a landfill. However, the cementitious properties of BOFS might help in utilizing this waste as an alternative material in alkali-activated systems. Therefore, in this study, BOFS and blast furnace slag were activated with varying dosages of NaOH, and the fresh, physical, mechanical, and microstructural properties were determined along with statistical analysis to reach the optimal mix design. The test results showed that an increase in BOFS content decreased compressive and flexural strengths, whereas it slightly increased the water absorption and permeable pores of the tested mortar samples. On the contrary, the increase in NaOH molarity resulted in a denser microstructure, reduced water absorption and permeable pores, and improved mechanical properties. Statistically significant relationships were obtained through response surface methodology with optimal mix proportions, namely, (i) 24.61% BOFS and 7.74 M and (ii) 20.00% BOFS and 8.90 M, which maximize the BOFS content with lower molarity and improve the mechanical properties with lower water absorption and porosity, respectively. The proposed methodology maximizes the utilization of waste BOFS in alkali-activated systems and may promote environmental and economic benefits. Full article