Steel Production Process Research Articles

A systematic review of decarbonization pathway and modeling conception in iron and steel industry at micro-, meso-, and macro-levels.

To meet the carbon-neutral goal in 2050, an accelerated transition of decarbonization is needed in the iron and steel industry. Lots of decarbonization path models are recently being used to analyze the technology pathways, industrial structure adjustments, and short- or long-term policies for greenhouse gas emission reduction. Thus, systematic insight is required for better making sense of the status quo and differences in low-carbon transition pathways at different levels. This paper reviews the carbon emission reduction of steel production routes and summarizes the decarbonization models from micro-, meso-, and macro-level perspectives, respectively. First, we systematically analyze the capacity and potential of both available and emerging technologies in the iron and steel production process. Additionally, we conduct a theoretical analysis of the bottom-up models currently used as effective tools to assess decarbonization technology pathways. Subsequently, pathways and models in terms of industrial synergy are elaborated. Policy guidance and market support are explored to overcome the challenges of collaborative carbon reduction faced by global steel producers. The characteristics of top-down models for supporting carbon reduction are also discussed. Finally, gaps in the literature and future research agendas are identified. Advancing this research could enrich discussions on steel industry decarbonization and provide clearer assessments of modeling approaches. The results indicate that existing energy-saving technologies in the iron and steel industry have limited carbon reduction capacity. Significant reduction requires coordinated efforts with upstream and downstream industries, particularly in hydrogen and power sectors, along with financial support and favorable policies.

Going beyond carbon: Influence of structural parameters on the environmental impacts of typical building structures

The construction sector has a large impact on the environment, be it climate change, biodiversity loss or resources depletion. Part of those impacts comes from construction materials, and more specifically, concrete and steel building structures. Early-stage design tools including environmental damage assessment are therefore required to achieve sustainable construction practices. This article presents a parametric approach aiming at studying the influence of early-stage structural parameters on the environmental impacts of a structure. The developed methodology combines parametric structural design with a multicriteria cradle-to-grave Life Cycle Assessment, applied to typical housing structures such as grid based beam column structures with a central core for lateral stability. Results show that span, number of levels and materials greatly influence the environmental impacts of considered structures. Climate change scores per floor area range from 80 kgCO2/m2 for short span timber structure to 215 kgCO2/m2 for long span steel buildings with few levels. Beams and slabs have the largest contribution in such impacts. Changing the production processes of materials is a way to reduce greenhouse gas emissions. However, decarbonizing steel production processes or reducing the cement content of concrete participate in burdenshifting with increased scores in several impact categories, such as carcinogenic toxicity or ozone depletion.

Evaluation of steel metallurgical dust management: combining treatment methods and resource attributes

The escalating challenges of resource scarcity and environmental pressures have drawn significant attention to heavy metal-containing dust generated during steel production. While previous research on heavy metals in steel manufacturing has primarily focused on emission inventories, health and ecological risks, and recycling technologies, the assessment of steel metallurgical dust management has been largely overlooked. To address this research gap, this study employed statistical entropy analysis to evaluate the effectiveness of dust treatment in the steel production process, aiming to identify potential issues in dust management. Furthermore, an evaluation framework was established to systematically assess the resource attributes of steel metallurgical dust, thereby supporting evidence-based decision-making in dust management. Results indicate that Cd, Pb, As, Zn, Cr, and Hg tend to accumulate in dust during steel production. Given its high resource attribute, converter dust should be prioritized for recovery. Further analysis suggests that the rotary kiln process plays a transitional role in dust recovery. This study provides a scientific foundation for dust management in the steel industry and offers guidance for the sustainable development of dust utilization.

Prediction interval soft sensor for dissolved oxygen content estimation in an electric arc furnace

In this study, a novel soft sensor modeling approach using Takagi–Sugeno (TS) fuzzy models and Prediction Intervals (PIs) is presented to quantify uncertainties in Electric Arc Furnace (EAF) steel production processes, namely to estimate the dissolved oxygen content in the steel bath. In real EAF operation, dissolved oxygen content is measured only a few times in the refining stage; therefore, the approach addresses the challenge of predicting unobserved output under conditions of irregular and scarce output measurements, using two distinct methods: Instant TS (I-TS) and Input Integration TS (II-TS). In the I-TS method, the model is computed for each individual indirect measurement, while the II-TS approach integrates these indirect measurements. The inclusion of PIs in TS models allows the derivation of the narrowest band containing a prescribed percentage of data, despite the presence of heteroscedastic noise. These PIs provide valuable insight into potential variability and allow decision-makers to evaluate worst-case scenarios. When evaluated against real EAF data, these methods were shown to effectively overcome the obstacles posed by scarce output measurements. Despite its simplicity, the I-TS model performed better in terms of interpretability and robustness to the operational reality of the EAF process. The II-TS model, on the other hand, showed excellent performance on all metrics but exhibited theoretical inconsistencies when deviating from typical operations. In addition, the proposed method successfully estimates carbon content in the steel bath using the established dissolved oxygen/carbon equilibrium, eliminating the need for direct carbon measurements. This shows the potential of the proposed methods to increase productivity and efficiency in the EAF steel industry.

The Application of Converter Sludge and Slag to Produce Ecological Cement Mortars.

The paper presents an analysis of the effective use of a mixture of steel sludge (S1) and slag (S2) from the converter process of steel production for the production of cement mortars. Metallurgical waste used in the research, which is currently deposited in waste landfills and heaps near plants, posing a threat to groundwater (possibility of leaching metal ions present in the waste), was used as a substitute for natural sand in the range of 0-20% by weight of cement (each). The obtained test results and their numerical analysis made it possible to determine the conditions for replacing part of the sand in cement mortars with a mixture of sludge and slag from a basic oxygen furnace (BOF) and to determine the effects of such modification. For the numerical analysis, a full quadratic Response Surface Model (RSM) was utilized for two controlled factors. This model was subsequently optimized through backward stepwise regression, ensuring the inclusion of only statistically significant components and verifying the consistency of residual distribution with the normal distribution (tested via Ryan-Joiner's test, p > 0.1). The designated material models are helpful in designing ecological cement mortars using difficult-to-recycle waste (i.e., sludge and converter slag), which is important for a circular economy. Mortars modified with a mixture of metallurgical waste (up to 20% each) are characterized by a slightly lower consistency, compressive and flexural strength, and water absorption. However, they show a lower decrease in mechanical strength after the freezing-thawing process (frost resistance) compared to control mortars. Mortars modified with metallurgical waste do not have a negative impact on the environment in terms of leaching heavy metal ions. The use of a mixture of sludge and steel slag in the amount of 40% (slag/sludge in a 20/20 ratio) allows you to save 200 kg of sand when producing 1 m3 of cement mortar (cost reduction by approx. EUR 5.1/Mg) and will also reduce the costs of the environmental fee for depositing waste.

Robust optimization algorithm for integrated crane assignment and scheduling in slab yard with uncertain arrival time

A slab yard served as a key component of the iron and steel plant. Effective slab yard crane assignment and scheduling directly affect the overall efficiency of the steel production process. This work addresses an uncertain slab yard crane assignment and scheduling problem (SYCAS) with the fluctuating arrival time. A stochastic programming model is established to obtain a robust solution that minimises the completion time of slab groups under uncertainty. Due to its NP-hardness, we developed an improved column and cut generation method (IC&CG), which divides the problem into a relaxed master problem (RMP) and a slave problem (SP). A new initialisation strategy for obtaining high-quality solutions and an acceleration strategy for discarding irrelevant scenarios are proposed. We use small-size instances to compare the performance of IC&CG with CPLEX and a generalised column and cut generation method in both deterministic and uncertain environments to validate the effectiveness of the method. For large-size instances, a lower bound (LB) of the optimal objective function is proposed to show the effectiveness of IC&CG. Furthermore, we prove the robustness of the method by sensitive analysis.

Analysis of the scrap pre-heating inside an EOF steel refining furnace

In the present work a full-scale mathematical model of the gaseous flow and the thermal exchange of gases with the scrap were proposed in the exhaust system of an Energy Optimizing Furnace (EOF). The analysis of the flow and heat transfer of the gases in the exhaust system is fundamental to predict the thermal use in the pre-heating of the scrap and the best scrap allocation profile in the system to predict the continuity of the current industrial process. The objective of this work is to improve the overall energy efficiency of the EOF process, thereby reducing the direct and indirect emission of gases that contribute to climate change. Industrial tests were carried out to obtain the real values of the variables such as gas and scrap temperature. The results of the variables were used in the mathematical model. The results of the mathematical modelling demonstrated that the heat transfer between the gases and the metal scrap depends mainly on the previous preparation in the scrap yard and on the control of the size and compaction of the scrap. In this work it was possible to predict the variables of gas flow and scrap pre-heating during the steel production process in the EOF using mathematical simulation and validating with the results collected in the industrial furnace.

Low‐carbon economic schedule of the H2DRI‐EAF steel plant integrated with a power‐to‐hydrogen system driven by blue hydrogen and green hydrogen

AbstractHydrogen direct reduction iron coupled with electronic arc furnace (H2DRI‐EAF) technology, as an important technology for decarbonisation in the iron and steel industry, has the advantages of high electrification and low carbon emissions. However, the large demand for hydrogen in this technology relies significantly on the production of electrolytic hydrogen, leading to a substantial increase in power consumption in the steel production process. Moreover, the use of an unclean power source in electrolytic hydrogen production leads to increases in indirect carbon emissions, reducing the low‐carbon attributes of the technology. This study investigates the integrated flexible operation mode of a steel plant. An illustrating method is utilised for modelling the entire steel production process and power to hydrogen (PtH2) process in detail for the H2DRI‐EAF steel plant, which includes natural gas, photovoltaic, wind power self‐provided power plants, and carbon capture and storage (CCS) systems. A mixed integer linear programming (MILP) model is developed for the comprehensive scheduling of the steel mill. The results of the case studies indicate that by reliably integrating the production of renewable energy and natural gas power plants, the PtH2 system can fully consume the renewable energy output while ensuring the smooth progress of steel production and maximising the reduction of carbon emissions from hydrogen production and the total cost of steel production.

Prediction and analysis of mechanical properties of hot-rolled strip steel based on an interpretable machine learning

Establishing a low-cost, high-precision predictive model for mechanical properties is crucial for enhancing the properties of steel. Due to the complexity of the steel production process, traditional mechanistic models struggle to efficiently and accurately describe the relationships among alloy elements, processes, and properties. In response to this, the paper introduces a high-precision framework for predicting and optimizing mechanical properties through feature engineering and machine learning models. Firstly, an effective dimension reduction of features is achieved through a weighted fusion heterogeneous feature selection strategy, thereby identifying the optimal model input features. Subsequently, an improved seagull optimization algorithm is utilized to optimize the hyperparameters of Extreme Gradient Boosting, further enhancing the predictive accuracy of the model. Moreover, based on the Shapley Additive Explanation method, a quantitative analysis of the model's predictive outcomes is conducted, elucidating the impacts of alloy elements and processes on mechanical properties, consistent with established principles of physical metallurgy. The proposed framework not only enables precise prediction of mechanical properties in steel but also provides theoretical guidance and technical support for process optimization design and the development of new steel grades.

Data-Driven Batch Process Monitoring for Continuous Annealing of Cold-Rolled Strip Steel

The continuous annealing process (CAP) is a crucial process of steel production, which has a significant impact on the uniformity and stability of mechanical properties. A novel batch monitoring process based on kernel dissimilarity (KDISSIM) and Kmeans++ is proposed in this paper, focusing on problems such as unequal sample lengths between batches and nonlinearity between variables. First, KDISSIM is used to describe the dissimilarity between batches. Secondly, Kmeans++ is employed to improve the accuracy of clustering tasks based on historical batches. The largest cluster is considered to be at a relatively stable control level, and these batches are further used as training data. Then, the center batch and boundary batch of the training set are used as the reference batch and monitoring threshold for the monitoring model, respectively. Finally, the effectiveness of the proposed method is verified via the actual CAP data, providing a feasible solution for CAP batch monitoring.

Effect of Residual Elements during the Hot‐Working Process of Steel Production: A Critical Review

Demand for steel scrap as feedstock for steelmaking increases, allowing the production of steel with lower energy costs and reduced CO2 emissions. In general, steel scrap may contain high levels of undesirable residual elements, which may contribute to processing challenges such as surface hot‐shortness, bulk hot‐shortness, hot‐brittleness, and higher rolling loads during the hot‐working process of steel production. The effect of different residual elements during the hot‐deformation process is a broad topic and involves the understanding of both individual and combined behavior of residual elements for different thermal and mechanical conditions that arise. In this article, the behavior of various residual elements is critically reviewed, individually and synergistically at high temperatures during the hot‐working process (i.e., reheat furnace stage, descaling stage, and deformation stage) of steel production, with a focus on (surface) hot‐shortness, delayed recrystallization, and higher rolling loads caused by the residual elements. Herein, it is aimed to help the steel community to identify challenges and opportunities in efficiently utilizing steel scrap.

Investigation of the Influence of Including or Omitting the Oxide Layer on the Result of Identifying the Local Boundary Condition during Water Spray Cooling

In the case of products made of steel, the presence of an oxide layer, which is formed during the steel production process as a result of high temperature, has a significant impact on the process of heat removal from the surface of the cooled material. For this reason, it is necessary to take into account the presence of the oxide layer in mathematical and numerical models used to simulate the distribution of the temperature field in cooled steel products. These models are based on the boundary conditions identified for given production conditions. This paper presents a comparison of the results of the identification of the boundary condition during water spray cooling of Armco iron with the use of the inverse solution. Numerical calculations were carried out using two models of heat conduction. In the first model, the presence of an oxide layer with different thermophysical properties than the base material (Armco iron) was taken into account. The second model assumed no oxide layer on the cooled Armco iron surface. It was found that the inverse solution obtained in the case of the heat conduction model taking into account the thickness of the oxide layer is correct in time and as a function of temperature. Thus, the boundary condition model obtained as a function of temperature is universal. However, this model requires an additional layer of oxides with different thermophysical properties than the base material to be included in the finite element model (FEM). Based on the conducted uncertainty tests of the inverse solution, it was found that the results of the determined boundary condition in the absence of the oxide layer on the cooled surface are subject to an error higher than 10% in comparison to the maximum reference value of the heat transfer coefficient.

Susceptibility to moisture damage in asphalt mixes with blast furnace dust as aggregate

One of the ongoing challenges in engineering is mitigating the degradation of road infrastructure caused by water in asphalt mixes. This study aims to assess the impact of blast furnace dust, a byproduct of the steel manufacturing process, on moisture-induced damage in asphalt mixes. Three asphalt mixes were developed following the Marshall methodology: one comprising conventional crushed aggregates, while the others substituted 50% and 100% of the conventional fine aggregate with blast furnace dust. Material characterization procedures and chemical analysis of the blast furnace dust were conducted. Once compliance with the specified material requirements was verified and analyzed, water susceptibility was evaluated through an indirect tensile test across various void content levels. Similarly, leveraging the Superpave gyratory compactor, several compaction indices were determined to estimate the compaction behavior of each mix. Based on the findings, the inclusion of blast furnace dust in an asphalt mix proved satisfactory due to its contributions in enhancing tensile strength, consequently leading to a reduction in moisture-induced damage within the asphalt mix, as well as exhibiting improved compaction behavior. Additionally, this utilization contributed to diminishing the environmental impact linked to the steel production process, where substantial quantities of this residue accumulate.

LCA-based environmental benefit allocation between steel and cement industries in steel byproduct recycling

Among the steel byproducts, more than 80% of blast furnace slag is recycled in the cement industry. However, the criteria for allocating environmental benefits between the steel and cement industries need to be more transparent. This study aims to allocate the environmental benefits between the steel and cement industries when granulated blast furnace slag (GBFS) is recycled to replace Portland cement in the production of slag cement (SC). Specifically, this research proposes how the recycling of GBFS, a byproduct of the steel production process, can quantify environmental burdens and benefits from both attributional life cycle assessment (ALCA) and consequential life cycle assessment (CLCA) perspectives. It also suggests methods for allocating these environmental benefits between the steel and cement industries. Following the final agreement on the EU Carbon Border Adjustment Mechanism (CBAM), this study emphasizes the growing importance of interconnected allocation of environmental benefits through recycling. Various allocation methods using ALCA and CLCA approaches have been assessed, significantly impacting the analysis results. Furthermore, these findings of the study are expected to provide guidance for potential policy decisions and internal decision-making processes, highlighting the environmental benefits that GBFS recycling offers to both steel and cement industries.

The operating window simulation for injecting shale gas to the blast furnace used to smelt vanadium and titanium-bearing magnetite

ABSTRACT Presently, the steel industry is confronted with the formidable challenge of reducing carbon emissions, with blast furnace ironmaking accounting for over 60% of carbon emissions in the steel production process. This research suggests a technical approach for smelting vanadium and titanium-bearing magnetite (VTM) by injecting shale gas into blast furnaces. It examines the impact of injecting shale gas on the degree of direct reduction and develops a mathematical model for the blast furnace's operating window based on energy-mass balance. The operating window for a blast furnace that was smelting VTM while injecting shale gas was established: The top gas temperature (TGT) is kept within the range of 110∼180 °C, while the raceway adiabatic flame temperature (RAFT) is maintained between 2000∼2200 °C, when shale gas is replaced for coke, the potential injection range of shale gas is 0∼90.4 kg/tHM, and the associated oxygen enrichment rate ranges from 1.3% to 12.9%; When shale gas is replaced for pulverised coal, the potential range for injecting shale gas is 0∼77.3 kg/tHM, which corresponds to an oxygen enrichment rate of 1.3% to 7.3%. The research results can offer a theoretical foundation for VTM smelting through the injection of shale gas into blast furnaces.

Obtaining a Multi-Factor Optimum Blend Using Scrap within the Scope of Sustainable and Environmentally Friendly Steel Production: Application in a Steel-Casting Company

This study tackles the challenge of optimizing scrap blends in steel production to achieve sustainability and environmental consciousness. Focusing on a steel-casting company as a case study, we develop a mathematical model that minimizes cost, emissions, and energy consumption while maximizing scrap utilization. This model considers the specific elemental composition of various scrap piles and pure elements, alongside their associated costs and environmental impacts in the production of GS52 steel in a foundry company. Through the GAMS program and further verification with Microsoft Excel, we demonstrate that the optimal blend significantly reduces raw material costs by prioritizing scrap (99.7%) over pure elements. Moreover, this optimized blend minimizes energy consumption and associated carbon emissions, thus contributing to a more sustainable and environmentally friendly steel production process. This study offers valuable insights and a practical framework for the steel industry to adopt cost-effective and eco-conscious practices, aligning with global efforts towards sustainable manufacturing.

Three-Dimensional Imaging of Non-metallic Inclusions in Steel Using Ionoluminescence

Obtaining information on the morphology, size, distribution, and chemical composition of non-metallic inclusions in steel helps control the steel production process and the quality of the steel products. Two-dimensional analysis is commonly used to acquire this information; however, more accurate data can be obtained through three-dimensional analysis, leading to a better control of the quality of steel products and their production process. Currently, several techniques are proposed for the three-dimensional analysis of non-metallic inclusions; however, they are time consuming. Herein, the author presented a method to rapidly obtain three-dimensional images of non-metallic inclusions in steel using ionoluminescence (IL). A three-dimensional image of MgO·Al2O3 spinel inclusions was constructed based on two-dimensional IL images obtained every 10 min during argon–ion bombardment. The proposed IL imaging can cover an oval-shaped area of 1.17 mm × 0.26 mm on the semi-major and in the semi-minor axes, respectively, at a single measurement. Three-dimension images of MgO·Al2O3 spinel inclusions with sizes more than 20 μm can be obtained within 4 hours. Therefore, the IL imaging proposed here can provide a precise and rapid account of the effects of non-metallic inclusions on steel products and the steel production process.

Long Short-Term Memory Parameter Optimization Based on Improved Sparrow Search Algorithm for Molten Iron Quality Prediction

Blast furnace (BF) ironmaking is a key process in iron and steel production. Because BF ironmaking is a dynamic time series process, it is more appropriate to use a recurrent neural network for modeling. The long short-term memory (LSTM) network is commonly used to model time series data. However, its model performance and generalization ability heavily depend on the parameter configuration. Therefore, it is necessary to study parameter optimization for the LSTM model. The sparrow search algorithm (SSA) holds advantages over traditional optimization algorithms in several aspects, such as no need for prior knowledge, fewer parameters, fast convergence, and high scalability. However, the algorithm still faces some challenges, such as the tendency to become trapped in the local optimum and the imbalance between global search ability and local search ability. Therefore, on the basis of SSA, this study examined the Levy flight strategy, sine search strategy, and step size factor adjustment strategy to improve it. This algorithm, improved by three strategies, is called the improved sparrow search algorithm (ISSA). Then, the ISSA-LSTM model was established. Furthermore, considering the limitations of SSA in dealing with multi-objective problems, the fast non-dominated sorting genetic algorithm (NSGAII) was introduced, and the ISSA-NSGAII model was established. Finally, experimental validation was performed using real blast furnace operation data, which demonstrated the proposed algorithm’s superiority in parameter optimization for the LSTM model and prediction for real industrial data.

A novel leaching process of zinc ferrite and its application in the treatment of zinc leaching residue

Zinc ferrite is a refractory phase generated in the pyrometallurgical process of zinc and steel production. Much energy is invested in the decomposition of zinc ferrite to recycle zinc since zinc ferrite is difficult to leach. In this work, a novel leaching process targeted at decomposition of zinc ferrite was proposed to save energy and improve metal recovery efficiency. The key of this novel leaching process was the use of copper powder as the reductant. Leaching of zinc ferrite in the presence of copper powder was investigated. The extraction of zinc was 100% when molar ratio of copper to zinc ferrite was 1.5 while the extraction of zinc was only 19.3% without copper. Effects of leaching temperature, acid concentration, the ratio of liquid to solid and reaction time were studied. Under the conditions: 60 °C, ≥ 70 g·L1 H2SO4, the ratio of liquid to solid ≥ 25 mL/g and the molar ratio of copper to zinc ferrite ≥ 1.5 at the open system, the synthetic zinc ferrite was dissolved completely within 60 min. Besides, the zinc ferrite-bearing zinc leaching residue from a roast-leach-electrowin plant was analyzed and leached under the optimal conditions. It was found that most of zinc ferrite in the zinc leaching residue was removed, remaining unreacted lead sulphate in the leached residue. The leaching efficiency of zinc in zinc leaching residue reached 94.2%. The results suggest that copper powder enhanced facile and efficient zinc extraction from zinc ferrite without concentrated acid, high temperature, long reaction time and specific investment. The recyclability of copper and its intrinsic commodity value showed the potential application in industrial zinc and iron containing wastes.

Study on the as-cast microstructure and mechanical properties of 35CrMo steel based on electric field control

Herein, the effect of current on the solidification microstructure and properties of 35CrMo structural steel has been studied. The effect of an electric field on the solidification structure of an ingot was investigated by immersing two parallel electrodes into the free surface of molten steel. Using the interaction between the current and melt as well as the Lorentz force generated by its own induced magnetic field, the whole region of the melt was covered with an eddy current. The numerical simulation of the ingot solidification process has been carried out and its influence on the inner flow field during the ingot solidification control process discussed. The results showed that an applied electric field caused turbulence inside the ingot, which drove the molten alloys to rotate and stir, refined the solidification structure, reduced the solidification defects, such as shrinkage cavity and segregation, and increased from 549.9 MPa at the top edge of the ingot and 411.4 MPa at the middle edge to 560.2 and 510.2 MPa, respectively. In addition, the electric field made the hardness and strength of each part of the ingot more uniform and improved the quality of its rigidity for the steel production process.