Search Results (68)

Metallurgical batching—governing raw material proportioning across sintering, blast furnace ironmaking, converter steelmaking, and non-ferrous smelting—critically determines product quality, energy consumption, and production cost throughout the full process chain. Its inherent complexity, characterized by strong nonlinear physicochemical coupling, measurement delays of up to 1.5 h, and multi-source raw material disturbances, renders conventional linear programming and empirical methods inadequate for dynamic, multi-objective industrial environments. This review systematically examines 98 representative studies (2020–2026) on intelligent algorithms applied to metallurgical batching optimization. A two-dimensional analysis framework of the fusion algorithm function and metallurgical scene is established. All kinds of methods are divided into three categories: prediction-oriented, optimization-oriented and decision-oriented, covering four typical scenes of sintering burdening, blast furnace ironmaking, converter steelmaking and non-ferrous metal smelting. Traditional machine learning models achieve sintering burn-through point prediction with R² ≈ 0.85 and offer superior interpretability via SHAP analysis. Deep learning architectures deliver blast furnace silicon content prediction with RMSE ≈ 0.04%, while multi-objective evolutionary algorithms provide mature Pareto optimization for batching cost and carbon objectives. Reinforcement learning holds long-term potential for closed-loop adaptive control but remains constrained by Sim-to-Real safety barriers. Converter steelmaking and non-ferrous smelting are identified as underexplored domains. Three priority directions are proposed: domain-adaptive predictive modeling for cross-plant generalization, real-time re-optimization embedding mechanism constraints, and safe reinforcement learning transfer via high-fidelity digital twins. Full article

Converter steelmaking remains the dominant route for global steel production, and the double-slag process is an important refining method that merits further study. In this work, a MATLAB-based mathematical model was developed for the double-slag process under a fixed dephosphorization rate, focusing on slag control during the low-temperature dephosphorization stage (1360–1400 °C) and the subsequent decarburization stage. The model was used to guide industrial trials and analyze the effect of the deslagging ratio (Rds) on slag control and process behavior. The results show that: (1) under a given Rds, the double-slag process can theoretically approach stable slag control and slag volume with increasing decarburization slag recycling cycles; (2) at a fixed dephosphorization rate, changes in Rds affect both the total amount of slag-forming materials and their distribution between refining stages; (3) although the double-slag process reduces slag-forming material consumption compared with the single-slag process and conventional low-slag practice, it does not necessarily guarantee low-slag smelting; and (4) an optimal Rds exists under specific conditions, indicating that a higher deslagging ratio is not always beneficial and must be balanced with effective phosphorus removal. Industrial trials showed that the compliance rate of key slag parameters exceeded 60%, the dephosphorization rate during the dephosphorization stage was above 60%, and the overall dephosphorization rate exceeded 90% on average. The recycling of decarburization slag also showed complex effects on phosphorus removal in subsequent heats, indicating that its influence should be evaluated over multiple cycles rather than from isolated heats. Therefore, ideal stability predicted by the model cannot be fully achieved in industrial practice, and controlled recycling combined with timely slag renewal is required for process optimization. Full article

Accurate endpoint control in basic oxygen furnace (BOF) steelmaking is essential for reducing production costs and improving steel quality. To overcome the limited mechanism support and poor transparency of purely data-driven models, this study proposes a physics-aware and interpretable framework for cumulative decarburization prediction based on real industrial data. Historical multi-heat data from the same converter were integrated, and an averaged full-spectrum cross-correlation method was used to estimate and correct the transport delay of off-gas signals, thereby constructing a heat-wise large-sample dataset. Key elemental features with clear physical significance were then extracted from high-dimensional flame spectra by incorporating their underlying radiation mechanisms. On this basis, a Stacking-based ensemble model was developed for cumulative decarburization prediction, and SHAP was introduced to interpret the model decision logic. Results show that the proposed framework outperforms conventional single models and purely data-driven dimensionality reduction methods. SHAP analysis further indicates that model decisions are mainly dominated by four core elemental spectral features, namely Fe, C, O, and Mn. Overall, the proposed method combines predictive performance, physical constraints, and interpretability, and provides a new solution for auxiliary soft sensing and decision support in BOF endpoint control. Full article

Accurately predicting molten steel carbon content plays a crucial role in improving productivity and energy efficiency during the Basic Oxygen Furnace (BOF) steelmaking process. However, current data-driven methods primarily focus on endpoint carbon content prediction, while lacking sufficient investigation into real-time curve forecasting during the blowing process, which hinders real-time closed-loop BOF control. In this article, a novel Transformer-based framework is presented for real-time carbon content prediction. The contributions include three main aspects. First, the prediction paradigm is reconstructed by converting the regression task into a sequence classification task, which demonstrates superior robustness and accuracy compared to traditional regression methods. Second, the focus is shifted from traditional endpoint-only forecasting to long-term prediction by introducing a Transformer-based model for continuous, real-time prediction of carbon content. Last, spatial–temporal feature representation is enhanced by integrating an optical flow channel with the original RGB channels, and the resulting four-channel input tensor effectively captures the dynamic characteristics of the converter mouth flame. Experimental results on an independent test dataset demonstrate favorable performance of the proposed framework in predicting carbon content trajectories. The model achieves high accuracy, reaching 84% during the critical decarburization endpoint phase where carbon content decreases from 0.0829 to 0.0440, and delivers predictions with approximately 75% of errors within ±0.05. Such performance demonstrates the practical potential for supporting intelligent BOF steelmaking. Full article

Scrap steel is known to influence the fluid flow characteristics of the melt pool in converter steelmaking. However, few studies have considered the effects of the scrap steel charging structure. In this study, a physical model of a 1:8.8 steel–scrap–gas three-phase flow converter was established to investigate the effects of scrap steel state, distribution, material type and shape on the fluid flow characteristics of the converter melt pool. The velocity distribution within the molten pool was measured using particle image velocimetry, while mixing time under various operating conditions was determined using the stimulus–response method. Considering the melting behaviour of scrap steel and the gas utilisation rate comprehensively, the results indicate that when scrap steel is arranged in a uniform position at the bottom of the converter—comprising 90% medium scrap in rectangular scrap and 10% heavy scrap in thin-plate form—and the gas flow rate is 750 m³/h, the overall dynamic conditions of the melt pool are optimal. At this time, the mixing time is 68.2 s (a reduction of up to 45.4%), average velocity is 0.117 m/s (a maximum increase of 207.9%) and turbulent energy dissipation rate is 0.0266 m²/s³ (a maximum increase of 141.8%). Finally, a relationship was established between stirring power and mixing time at different scrap steel charging structures, providing a methodological reference and data support for optimising the charging structure of scrap steel and efficiently using scrap steel in converters. Full article

The amount of scrap steel and selection of blowing process parameters are known to influence the fluid flow characteristics of the melt pool in converter steelmaking. However, few studies have considered the effects of scrap steel and blowing process parameters together. In this study, a physical model of a converter is established to investigate the influences of the amount of scrap steel and bottom-blowing process parameters on the fluid flow characteristics of the melt pool. Particle image velocimetry is used to measure the velocity distribution in the melt pool, and the stimulus–response method is used to measure the mixing time of the melt pool under different operating conditions. The results show that increasing the scrap steel ratio worsens the dynamic conditions of the melt pool. The best of the explored combinations is achieved at a scrap steel ratio of 20% and with six nozzles. The mixing time decreases as the gas flow rate increases, but the rate of decrease also decreases. Based on the results, the mixing time can be predicted from the gas flow rate and the number of nozzles. A relationship between the stirring power and mixing time of a converter using the bottom-blowing process is established. Full article

Magnesia-dolomite refractories have emerged as sustainable alternatives to traditional carbon- or chromium-containing linings in steelmaking and cement industries. Their outstanding thermochemical stability, high refractoriness, and strong basic slag compatibility make them suitable for converters, electric arc furnaces (EAF), and argon–oxygen decarburization (AOD) units. However, their practical application has long been constrained by hydration and thermal shock sensitivity associated with free CaO and open porosity. Recent advances, including optimized raw material purity, fused co-clinker synthesis, nano-additive incorporation (TiO₂, MgAl₂O₄ spinel, FeAl₂O₄), and improved sintering strategies, have significantly enhanced density, mechanical strength, and hydration resistance. Emerging technologies such as co-sintered magnesia–dolomite composites and additive-assisted microstructural tailoring have enabled superior corrosion resistance and extended service life. This review provides a comprehensive analysis of physicochemical mechanisms, processing routes, and industrial performance of magnesia–dolomite refractories, with special emphasis on their contribution to technological innovation, decarbonization, and circular economy strategies in high-temperature industries. Full article

This article presents the results of experimental studies to determine the possibility of processing steelmaking slags into an artificial granulated filler for concrete by the method of forced carbonization and the stabilization of the obtained filler in the concrete matrix over time. The utilization of metallurgical waste and technogenic CO₂ is a global problem. In this work, the method of the granulation of finely ground converter (BOF) and electric steelmaking (EAF) slags was used to obtain artificial granules and their subsequent forced carbonization in the developed laboratory carbonization chamber. Within the framework of this study, the quantitative binding of CO₂ by granules based on BOF and EAF slags was established, which amounted to 5.2 and 7.8% by weight, respectively. It was determined that the mass loss during crushability testing, indirectly characterizing the actual compressive strength of the granule material, depending on the type of slag and grain size, ranges from 13.6 to 42.3%, which is quite sufficient for using this artificial filler in concrete production. Based on the developed batches of fillers, concretes were obtained that have a compressive strength of 30.7 to 37.8 MPa in 28 days of hardening, which generally corresponds to concrete class B25. The preliminary studies and the results obtained indicate the prospects of processing steel slags into artificial granulated fillers by forced carbonization and using this product in concrete production. Full article

Top-bottom combined blowing converter steelmaking involves complex thermodynamic and kinetic processes, and predictive modeling has long been a key focus in steelmaking research. This paper proposes a kinetic process prediction model with on-site applicability. Based on actual production data, machine learning models (BP neural network, random forest, XGBoost) are employed to predict Tapping Steel Oxygen (TSO) content, which is then used as input for the kinetic model. An optimized theoretical decarburization kinetic model is selected and validated against measured Tapping Steel Carbon (TSC) data. The key innovation lies in the integration of converter control parameters into the kinetic model through a data-driven cyclic iteration algorithm. Comparison of prediction accuracy before and after integration shows that the model’s TSC prediction within the range [−0.2, +0.2] improves by 6.26%. This work presents a novel approach for enhancing kinetic models via control parameter integration, offering effective guidance for real-time monitoring and optimization in converter steelmaking. Full article

In this study, the feasibility of converting iron-bearing tailings from urban steel scrap recycling to value-added direct reduced iron (DRI) via magnetic separation followed by hydrogen reduction under microwave irradiation was investigated, with an emphasis on the effect of reduction temperature. The experimental results showed that by magnetic separation, the tailings sample with an iron content of 15.42 wt% could transit to a high-grade magnetic concentrate with an iron content of 60.04 wt% and good microwave absorption capability, as revealed by its short microwave penetration depth (D_p). After hydrogen reduction under microwave irradiation, the main iron-bearing phases, including magnetite, hematite, limonite, and martite, had stepwise deoxidation into metallic iron. As the reduction temperature increased from 750 °C to 1050 °C, the total iron content (TFe), reduction degree and iron metallization degree of the product increased rapidly and then became stable due to difficult reduction of FeO. As the reduction process proceeded, the dispersed iron particles gradually aggregated. At the optimum temperature of 950 °C, the reduction degree and iron metallization degree reached 90.10% and 88.71%, respectively. Meanwhile, the pore size, microporous volume, and specific surface area of the product were 1.943 nm, 1.767 × 10⁻⁵ cm³/g, and 0.3961 m²/g, respectively. The saturation magnetization (M_S) and coercivity (H_C) of the product remained 170.94 emu/g and 46.25 Oe, respectively. The product can act as a potential feedstock for electric arc furnace (EAF) steelmaking. Full article

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

Hot metal is produced in a blast furnace. Subsequently, the hot metal is loaded from the blast furnace into a torpedo car and transported to the ladle, where the desulfurization process of the hot metal is realized. After desulfurization, the hot metal is poured from the ladle into the oxygen converter. The temperature of the hot metal has an impact on the steelmaking process realized in the oxygen converter. The complex model presented in the article calculates the temperature drop of the hot metal in the torpedo car and the ladle. Predicting the hot metal temperature behavior allows for determining the length of time the hot metal transport requires and thus initiating steelmaking at its required hot metal temperature. This model, based on heat transfer by conduction, convection, radiation, heat accumulation, and chemical reactions, also allows for the monitoring of the hot metal temperature drop in the torpedo car and the ladle, the analysis of the influence of the linings in terms of heat accumulation, the investigation of the desulfurization process in the ladle, and the optimization torpedo and ladle selection in terms of the accumulated heat in the lining for their entry into the hot metal transport process. An absolute and relative error calculation was used to verify the proposed model. Full article

At the steelmaking temperature, carbon has a strong deoxidation ability. Under the vacuum condition, its deoxidation ability can be further improved, and it can become a stronger deoxidation element than aluminum. The product of carbon deoxygenation is CO, which floats up and detaches from the molten steel in the form of bubbles and does not produce oxide inclusions. Under normal pressure, replacing aluminum with carbon to complete partial deoxidation tasks can not only reduce the generation of inclusions and alleviate the pressure of removing inclusions, but also reduce the consumption of aluminum and save deoxidation costs. In this study, the carbon deoxidation process after the converter was investigated. Firstly, the timing of carbon addition was determined through thermodynamic calculations, and it was found that, in oxygen-enriched molten steel, the priority of the reaction of the deoxidation element was [Al] > [Si] > [C] > [Mn]. Through the carbon and oxygen balance calculation, it is known that the carbon deoxidation effect is greatly affected by the carbon content of the molten steel; for low-carbon steel, carbon can be used for pre-deoxygenation, whereas for medium-carbon and high-carbon steel, carbon can complete most of the deoxidation tasks. Finally, with 45 steel as the research object, the carbon deoxidation process was designed and tested in industry. The results showed that, compared with the aluminum deoxidation process, the number of inclusions in the billet casting of the carbon deoxidation process was reduced by 68.8%, and the carbon deoxidation process had fewer large-sized inclusions in the billet casting. In addition, the carbon deoxidation process uses carbon powder instead of the aluminum block for deoxidation during steel tapping from the converter. The deoxidant cost is reduced by CNY 15.47/ton of steel. From a comprehensive point of view, the application of carbon deoxidation after the converter can reduce aluminum consumption and improve the cleanliness of steel, which is an important way for enterprises to reduce costs and increase efficiency. 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

Regulating the process parameters in converter steelmaking is crucial for reducing the phosphorus content in molten steel and enhancing its quality. However, immoderate alteration may result in raised production costs and the occurrence of phosphorus return. This study addresses process parameter optimization challenges in converter steelmaking by proposing an improved multi-objective whale optimization algorithm (IMOWOA) that synergistically integrates metallurgical thermodynamics with data-driven modeling. The methodology constructs a physics-informed objective function linking process parameters to optimization targets, thereby resolving the disconnect between mechanistic and data-driven modeling approaches. The algorithm innovatively combines Sobol quasi-random sequences with grey wolf social hierarchy strategies to prevent premature convergence in high-dimensional search spaces while maintaining Pareto front diversity, supplemented by a reward mechanism to ensure strict adherence to multi-objective constraints. Experimental validation using steel plant production data demonstrates IMOWOA’s efficacy, achieving a 10.8% reduction in endpoint phosphorus content and a 5.79% decrease in production costs per ton of steel. Comparative analyses further confirm its superior feasibility and stability in quality-cost co-optimization, evidenced by a 12.6% improvement in hypervolume (HV) over conventional swarm intelligence benchmarks, establishing a robust framework for industrial metallurgical process optimization. Full article