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The melting of scrap and hot briquetted iron (HBI) in an AC electric arc furnace (EAF) is simulated by an advanced 3D computational fluid dynamics (CFD) model that captures the arc heating, the scrap/HBI melting process, and the solid collapse mechanisms. The CFD model is used to simulate a scenario where charge layering and EAF power profiles are provided by a real EAF operation. CFD simulation of the EAF operation shows proper prediction of the charge melting when compared with standard industry practice. Namely, the CFD model predicts a 32.5%/67.5% ratio of solid/liquid steel at the beginning of refining, which approaches the 30%/70% ratio used in standard practice. Based on this prediction, the melting rate in the CFD results differs by 8.3% from actual EAF operation. The impact of charge layering on melting is also investigated. CFD results show that distributing charge material into a greater number of layers in the first bucket (10 layers as compared to 4) enhances the melting rate by 12%. However, including dense material at the bottom of the furnace deteriorates melting performance, reducing the impact of the number of layers of the charge. The CFD platform can be used to optimize the use of HBI/scrap in real EAF operations and to determine best recipe practices. [ABSTRACT FROM AUTHOR]

The development of multi‐principal‐element alloys (MPEAs) with unique characteristics such as high work hardening capacity similar to well‐known alloy systems like Hadfield steel X120Mn12 (ASTM A128) is a promising approach. Hence, by exploiting the core effects of MPEAs, the application range of conventional alloy systems can be extended. In the present study, work‐hardening MPEAs based on the equimolar composition CoFeNi are developed. Mn and C are alloyed in the same ratio as for X120Mn12. The production route consists of cast manufacturing by an electric arc furnace and surface functionalization via mechanical finishing using ultrasonic‐assisted milling (USAM) to initiate work hardening. The microstructure evolution, the hardness as well as the resulting oscillating wear resistance are detected. A pronounced lattice strain and grain refinement due to the plastic deformation during the USAM is recorded for the MPEA CoFeNi‐Mn12C1.2. Consequently, hardness increases by ≈380 HV0.025 in combination with a higher oscillating wear resistance compared to the X120Mn12. This shows the promising approach for developing work‐hardening alloys based on novel alloy concepts such as MPEAs. [ABSTRACT FROM AUTHOR]

The article describes modern methods for controlling and optimizing the energy mode of melting in electric arc furnaces and units for out-of-furnace steel processing using optical emission spectroscopy systems. These systems enable the determination of melt and slag temperatures, plasma temperatures in the arc combustion area, and slag composition, as well as the analysis of emission intensity from the melt and slag surfaces. The analysis revealed that depending on the range of steel to be smelted and the peculiarities of smelting technology at a particular electric arc steel-making furnace, control systems for smelting monitoring can be developed according to the obtained indicators of one or a combination of the abovementioned parameters. [ABSTRACT FROM AUTHOR]

The paper presents a novel decision support system designed to improve the efficiency and effectiveness of decision-making for electric arc furnace (EAF) operators. The system integrates two primary tools: the EAF Simulator, which is based on advanced mechanistic models, and the EAF Optimiser, which uses data-driven models trained on historical data. These tools enable the simulation and optimisation of furnace settings in real time and provide operators with important insights. A key objective was to develop a user-friendly interface with the Siemens Insights Hub Cloud Service and Node-RED that enables interactive management and support. The interface allows operators to analyse and compare past and simulated batches by adjusting the input data and parameters, resulting in improved optimisation and reduced costs. In addition, the system focuses on the collection and pre-processing of input data for the simulator and optimiser and uses Message Queuing Telemetry Transport (MQTT)communication between the user interfaces and models to ensure seamless data exchange. The EAF Simulator uses a comprehensive mathematical model to simulate the complex dynamics of heat and mass transfer, while the EAF Optimiser uses a fuzzy logic-based approach to predict optimal energy consumption. The integration with Siemens Edge Streaming Analytics ensures robust data collection and real-time responsiveness. The dual-interface design improves user accessibility and operational flexibility. This system has significant potential to reduce energy consumption by up to 10% and melting times by up to 15%, improving the efficiency and sustainability of the entire process. [ABSTRACT FROM AUTHOR]

This paper studies the integration of direct reduced iron (DRI) and hot briquetted iron (HBI) into the steelmaking process via an electric arc furnace (EAF). Considering a variety of DRI production techniques distinguished by different reactor types, this paper provides a comparative overview of the current state. It delves into significant challenges, such as the susceptibility of DRI to reoxidation and the necessity of thorough handling to maintain its quality. The effectiveness of several reoxidation mitigation strategies, including the application of thin oxide layers, briquetting, various coatings, and nitride formation in ammonia-based reduction processes, is evaluated. Most existing studies have primarily focused on the reoxidation of DRI rather than on HBI, despite the fact that HBI may undergo reoxidation. The importance of DRI/HBI in offering an alternative to the integrated steelmaking route is highlighted, focusing on how it changes the EAF process compared to those for melting scrap. This paper also identifies several research prospects for further DRI/HBI applications in steel production. [ABSTRACT FROM AUTHOR]

Developing the prediction model of the end‐point carbon content of the electric arc furnace (EAF) is an effective way to reduce the adjustment frequency of liquid steel composition and shorten the smelting time. Previous data‐driven models lack effective handling of the missing values in EAF production data. This may be the main reason why model accuracy is difficult to improve. This article proposes a novel modeling method based on the CatBoost algorithm with two‐stage optimization. In the preprocessing session, empirical and empirical‐cumulative‐distribution‐based outlier detection (ECOD) methods are utilized to extract input features and reject outliers. The end‐point carbon content prediction model is built based on CatBoost. The generative adversarial imputation nets (GAIN) method is used in the first optimization stage to handle the missing values. In the second optimization stage, recursive feature elimination (RFE) is used to select the final features, and whale optimization algorithm (WOA) is used to optimize the parameters of the CatBoost model. After verification with actual production data, the two‐stage optimized CatBoost model demonstrates excellent performance compared with other methods, with an R2 of 0.903, mean absolute error of 0.021, root mean squared error of 0.043, and 90.34% hit ratio within ±0.05% error range. [ABSTRACT FROM AUTHOR]

In the global metallurgy, there is an accelerated transition to more environmentally friendly steel production technologies with an increased share of secondary raw materials (scrap) and a weakening of the negative impact of the steelmaking process on the environment. As part of the study of technical and technological aspects of the transition from the use of continuously cast slabs from oxygen-converter steel to slabs from electric arc steel, NLMK Dansteel carried out a series of pilot experiments on smelting and thermomechanical rolling of steel products for current industrial orders for the production of hot-rolled plate steel for grounds of offshore wind generators. Using the example of the steel corresponding to category EN 10025-4:2019 S355ML, the quality of continuously cast large slabs produced by the electric arc method at NLMK Verona and the delivery mechanical properties of rolled sheets with a thickness of 68–73 mm produced in the rolling complex 4200 NLMK DanSteel by thermomechanical treatment followed by accelerated cooling have been studied. The total CFP value (carbon footprint) of manufactured sheet metal is at the level of 0.73–0.75 ton CO/ton. [ABSTRACT FROM AUTHOR]

The electric arc furnace (EAF) is considered the second most important process for the production of crude steel and is usually used for the melting of scrap. With the current emphasis on defossilization, its share in global steelmaking is likely to further increase. Due to the large production quantities, minor improvements to the EAF process can still accumulate into a significant reduction in overall energy and resource consumption. A major aspect in the efficient operation of the EAF is achieving beneficial slag properties, as the slag influences the composition of the steel and can reduce energy losses as well as the maintenance cost. In order to investigate the EAF operation, a dynamic process model is applied. Within the model, the chemical reactions of the metal–slag system are calculated based on the activities of the involved species. In this regard, multiple models for the calculation of the chemical activities have been implemented. However, depending on the chosen model, the computation of the slag activities can be computationally demanding. For this reason, the application of a neural network for the calculation of the chemical activities within the slag is investigated. The performance of the neural network is then compared to the results of the previously applied models by using the commercial software FactSage as a reference. The validation shows that the surrogate model achieves great accuracy while keeping the computation demand low. [ABSTRACT FROM AUTHOR]

Mitigating large active power fluctuations in electric arc furnaces at the start and end of the melting process is critical as it threatens the frequency stability of low power electrical systems. The use of a low-capacity battery energy storage system can help to mitigate the power fluctuations. This paper aims to present the results obtained on the application of different synchronization strategies with a low energy capacity Battery Energy Storage System under two classifications (i) in ramp mode and (ii) smoothing filters. Using MATLAB/SIMULINK (academically licensed 41037228) the different synchronization strategies were modeled, and their advantages and disadvantages were determined. It is concluded that synchronization through a low-pass filter is the most favorable variant for reducing active power variability. [ABSTRACT FROM AUTHOR]

The simulation results of the electrical operating modes of the DSP-100I7 electric arc furnace with different designs of the secondary current lead—triangulated and coplanar versions with a conductive conduit—are presented. The dependences of the skewness of arc discharge power in these furnaces under symmetrical and asymmetrical current modes are obtained. The significant influence of the asymmetrical mode on the arc power skewness in these furnaces is shown. [ABSTRACT FROM AUTHOR]

Thermocline thermal energy storage systems play a crucial role in enhancing energy efficiency in energy-intensive industries. Among available technologies, air-based packed bed systems are promising due to their ability to utilize cost-effective materials. Recently, one of the most intriguing filler materials under study is steel slag, a byproduct of the steel industry. Steel slag offers affordability, ample availability without conflicting usage, stability at temperatures up to 1000 °C, compatibility with heat transfer fluids, and non-toxicity. Previous research demonstrated favorable thermophysical and mechanical properties. Nonetheless, a frequently overlooked aspect is the endurance of the slag particles, when exposed to both mechanical and thermal stresses across numerous charging and discharging cycles. Throughout the thermal cyclic process, the slag within the tank experiences substantial loads at elevated temperatures, undergoing thermal expansion and contraction. This phenomenon can result in the deterioration of individual particles and potential damage to the tank structure. However, assessing the extended performance of these systems is challenging due to the considerable time required for thermal cycles at a relevant scale. To address this issue, this paper introduces a specially designed fast testing apparatus, providing the corresponding testing results of a real-scale system over 15 years of operation. [ABSTRACT FROM AUTHOR]

The iron and steel industry significantly contributes to global energy use and greenhouse gas emissions. The rising deployment of volatile renewables and the resultant need for flexibility, coupled with specific challenges in electric steelmaking (e.g., operation optimization, optimized power purchasing, effective grid capacity monitoring), require accurate energy consumption and demand forecasts for electric steel mills to align with the energy transition. This study investigates diverse approaches to forecast the energy consumption and demand of an electric arc furnace—one of the largest consumers on the grid—considering various forecast horizons and objectives with limited knowledge on process parameters. The results are evaluated for accuracy, robustness, and costs. Two grid connection capacity monitoring approaches—a one-step and a multi-step Long Short-Term Memory neural network—are assessed for intra-hour energy demand forecasts. The one-step approach effectively models energy demand, while the multi-step approach encounters challenges in representing different operational phases of the furnace. By employing a combined statistic–stochastic model integrating a Seasonal Auto-Regressive Moving Average model and Markov chains, the study extends the forecast horizon for optimized day-ahead electricity procurement. However, the accuracy decreases as the forecast horizon lengthens. Nevertheless, the day-ahead forecast provides substantial benefits, including reduced energy balancing needs and potential cost savings. [ABSTRACT FROM AUTHOR]

Considering the pivotal role of ferroalloys in the steel industry and the escalating global emphasis on sustainability (e.g., zero emissions and carbon neutrality), the demand for ferroalloys is anticipated to increase. However, the electric arc furnace (EAF) of ferroalloy plants generates substantial amounts of nitrogen oxides (NOx) because of the high-temperature combustion processes. Despite the substantial contributions of many studies on NOx prediction from various industrial facilities, there is a lack of studies considering the environmental condition of the EAF in ferroalloy plants. Therefore, this study presents a deep learning model for predicting NOx emissions from ferroalloy plants and further can provide guidelines for predicting NOx in industrial sites equipped with electric furnaces. In this study, we collected various historical data from the manufacturing execution system of electric furnaces and exhaust gas systems to develop a prediction model. Additionally, an interpretable artificial intelligence method was employed to track the effects of each variable on the NOx emissions. The proposed prediction model can provide decision support to reduce NOx emissions. Furthermore, the interpretation of the model contributes to a better understanding of the factors influencing NOx emissions and the development of effective strategies for emission reduction in ferroalloys EAF plants. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive review of electric arc furnace (EAF) slag recycling in the United States, examining its classification and the associated challenges and opportunities of its industrial use. The study affirms EAF slag's nonhazardous status. The main challenges identified in EAF slag applications include substantial variations in composition and volume instability during/after hydration. Analysis of the U.S. recycling practices reveals that EAF slag is predominantly reused, with minimal landfill disposal. However, its prevalent use as a low value‐added aggregate in construction applications underscores the industry's ongoing challenge to get additional value from EAF slag recycling. Despite these challenges, the study highlights a great potential for increased value extraction from EAF slag recycling. Beyond conventional applications as a clinker material for the cement industry, the review explores modern technologies for steelmaking slag recycling, revealing options for recovering valuable metals such as Cr, V, Mo, and Fe through methods such as leaching, reduction, and oxidation. [ABSTRACT FROM AUTHOR]

In steel recycling, the optimization of Electric Arc Furnaces (EAFs) is of central importance in order to increase efficiency and reduce costs. This study focuses on the optimization of electric arcs, which make a significant contribution to the energy consumption of EAFs. A three-phase equivalent circuit integrated with the Cassie–Mayr arc model is used to capture the nonlinear and dynamic characteristics of arcs, including arc breakage and ignition process. A particle swarm optimization technique is applied to real EAF data containing current and voltage measurements to estimate the parameters of the Cassie–Mayr model. Based on the Cassie–Mayr arc parameters, a novel Arc Quality Index (AQI) is introduced in the study, which can be used to evaluate arc quality based on deviations from optimal conditions. The AQI provides a qualitative assessment of arc quality, analogous to indices such as arc coverage and arc stability. The study concludes that the AQI serves as an effective operational tool for EAF operators to optimize production and increase the efficiency and sustainability of steel production. The results underline the importance of understanding electric arc dynamics for the development of EAF technology. [ABSTRACT FROM AUTHOR]

The melting of scrap and hot briquetted iron (HBI) in an AC electric arc furnace (EAF) is simulated by an advanced 3D computational fluid dynamics (CFD) model that captures the arc heating, the scrap/HBI melting process, and the solid collapse mechanisms. The CFD model is used to simulate a scenario where charge layering and EAF power profiles are provided by a real EAF operation. CFD simulation of the EAF operation shows proper prediction of the charge melting when compared with standard industry practice. Namely, the CFD model predicts a 32.5%/67.5% ratio of solid/liquid steel at the beginning of refining, which approaches the 30%/70% ratio used in standard practice. Based on this prediction, the melting rate in the CFD results differs by 8.3% from actual EAF operation. The impact of charge layering on melting is also investigated. CFD results show that distributing charge material into a greater number of layers in the first bucket (10 layers as compared to 4) enhances the melting rate by 12%. However, including dense material at the bottom of the furnace deteriorates melting performance, reducing the impact of the number of layers of the charge. The CFD platform can be used to optimize the use of HBI/scrap in real EAF operations and to determine best recipe practices.

In aggressive environments under a load, steels are subject to chloride corrosion cracking as cracks on the metal surface. This work proposes compositions of steel with additives from groups 5–8, d-transition rare earth metals (REMs) Ni, V, W, and Re, intended to function under constant tensile stresses in an aggressive corrosive environment. Technologies have been developed for the mass industrial production of steel in an electric arc furnace (EAF) with the batch introduction of Ni, V, W, and Re and electromagnetic mixing. For smaller volumes of serial production, electron beam remelting (EBR) with thermal and radiation-chemical effects is used. Studies have shown that the composition of alloying elements in steel during melting in an EAF and electroarc doping is identical and corresponds to that stated; with EBR, the O, P, S, Si, Al, and Ti contents decreased. In the EAF, the use of an ultrahigh purity stainless steel alloy source material does not mix impurity elements P, S, Sn, and Pb with the melt. The mass of losses of alloying elements from the total losses in the EAF is not more than 0.1%–0.15%. Re alloying leads to a stable increase in the conditional yield strength σ0.2 and the tensile strength σv from 20°C to 1200°C, and the strain-to-fracture increased to 62%. Tests for the resistance of steel with additives of d-transition refractory REMs Ni, V, W, and Re against chloride corrosion cracking under the influence of tensile stresses showed that the time to fracture increased by 30%–40%. [ABSTRACT FROM AUTHOR]

This work presents the properties of the glass phase of fusion-cast refractories with 28 – 94% ZrO2. The effect of glass phase composition on the main performance properties of refractories, namely, corrosion resistance and tendency to release defects, is discussed. It is revealed that a decrease in the amount of glass phase in fusion-cast baddeleyite-corundum refractories with a simultaneous increase in the amount of ZrO2 results in an increase in corrosion resistance and complication of the annealing conditions of the products. The glass phase properties that determine the tendency of refractories to release defects depend mainly on the SiO2 to Na2O ratio, the purity of raw materials, and the method of charge melting. Oxidative melting with treatment of the melt with an oxygen–gas mixture is a prerequisite for the formation of a refractory glass phase in refractories and manufacturing products with a low tendency to the formation of defects. [ABSTRACT FROM AUTHOR]

Steelmaking is one of the most energy-intensive industries, so improving control efficiency helps to reduce the energy used to produce a tonne of steel. Mutual influences between the phases of an electric arc furnace in available electrode movement control systems cause unproductive electrode movements as a reaction to the redistribution of currents among the phases of a three-phase power supply system due to changes in arc length in one of the phases. The nonlinearity of the characteristics of an electric arc furnace significantly complicates the ability to provide autonomous electrode movement control. The approach proposed in this paper, based on the formation of a matrix of mutual influences with variable coefficients, significantly improves the per-phase autonomy of the electrode movement control system. Nonlinear dependences of the mutual influence coefficients as a function of the current increment in the phase in which the disturbance occurred are obtained. Thus, it is possible to practically eliminate unproductive electrode movements in existing control systems by avoiding the traditional use of a dead zone, which reduces the control quality in the zone of small disturbances. The complex of experiments performed using the mathematical model demonstrate that the mutual influence improves the dynamic properties of the electrode movement system in certain operating modes. [ABSTRACT FROM AUTHOR]

Voltage flickers and harmonics are power quality (PQ) problems in the electric system during a variation and arc furnace (AF) adaptability. AF creations must determine a harmonic and flicker. This article evaluates complex AF systems. This article presents a newly established time domain (TD) static become to on an AF's V-I attributes (VIA). Static arc configurations are useful for harmonic analyses, but dynamic methods are needed for PQ studies, especially voltage flicker analysis. The MATLABbased dynamic AF configuration is simulated for four different configurations. A response with configurations 1 and 4 varies from the real AF outcomes. The simulation results and numerical finding shows that configurations 2 and 3 are much more appropriate and produce better results for minimum 3rd harmonics for arc current, arc voltage, and point of common coupling (PCC) voltage. The novelty of this configuration is that the energy transferred to the load by the AF during the cycle of operation has been identified, making the developed scheme more reliable and dependent on the load's operational conditions. After that, effective applications of these configurations and other configurations' accuracy should be clarified. [ABSTRACT FROM AUTHOR]

The present work aims to answer three essential research questions (RQs) that have previously not been explicitly dealt with in the field of applied machine learning (ML) in steel process engineering. RQ1: How many training data points are needed to create a model with near-upper-bound predictive performance on test data? RQ2: What is the near-upper-bound predictive performance on test data? RQ3: For how long can a model be used before its predictive performance starts to decrease? A methodology to answer these RQs is proposed. The methodology uses a developed sampling algorithm that samples numerous unique training and test datasets. Each sample was used to create one ML model. The predictive performance of the resulting ML models was analyzed using common statistical tools. The proposed methodology was applied to four disparate datasets from the steel industry in order to externally validate the experimental results. It was shown that the proposed methodology can be used to answer each of the three RQs. Furthermore, a few findings that contradict established ML knowledge were also found during the application of the proposed methodology. [ABSTRACT FROM AUTHOR]

Purpose: Despite its sizeable role in the global economy, the steel industry is also one of the world's largest energy consumers and a significant source of greenhouse gas emissions. Iran is one of the world's top ten steel producers, with over 77% of its steel produced in electric arc furnaces (EAF). As a result, a thorough impact assessment is essential to understand the possible negative environmental impacts. The purpose of this study is to perform a life cycle assessment (LCA) on gas-based direct-reduction-iron-EAF steel production in Iran. Methods: The environmental impacts of steel production were evaluated in this study. The most influential processes and inputs were determined by analyzing their contributions separately. Due to the fact that the inputs of the EAF process can vary based on the context and availability of resources, multiple scenarios were defined, and the results were compared. SimaPro and OpenLCA software, the Ecoinvent database, and the IMPACT2002+evaluation method were used in this study. The data for the life cycle inventory was derived from the average performance of factories over a one-year period, with 1 ton of hot-rolled steel serving as the functional unit. Results: The findings indicated that among the various categories, non-renewable energy, global warming, and respiratory inorganics have the greatest impact, accounting for 86.4% of the total. The most significant environmental impacts of processes are associated with the EAF (35%), direct reduction iron (DRI), and oxide pellet processes (28.9% and 17.1%, respectively), while the most significant environmental impacts of inputs are associated with electricity (33.8%) and gas consumption (25.8%). Sensitivity analysis was also performed to assess the significance of the inputs. Given that the EAF process's primary inputs are scrap iron, two scenarios involving coal-based and gas-based sponge iron were defined, and the impact of each scenario was evaluated. Conclusion: Suggestions for sustainable production were made based on the assessment of the results. The low price of natural gas and abundance of iron ore in Iran make the production of gas-based steel with sponge iron more appealing. Additionally, it was shown that using sponge iron in the coal-based process results in high environmental impacts compared to other scenarios (gas-based systems) which can have a significant impact on global warming. This is particularly important because coal-based processes are widely used in India and China due to abundant coal resources. [ABSTRACT FROM AUTHOR]

The paper presents a novel decision support system designed to improve the efficiency and effectiveness of decision-making for electric arc furnace (EAF) operators. The system integrates two primary tools: the EAF Simulator, which is based on advanced mechanistic models, and the EAF Optimiser, which uses data-driven models trained on historical data. These tools enable the simulation and optimisation of furnace settings in real time and provide operators with important insights. A key objective was to develop a user-friendly interface with the Siemens Insights Hub Cloud Service and Node-RED that enables interactive management and support. The interface allows operators to analyse and compare past and simulated batches by adjusting the input data and parameters, resulting in improved optimisation and reduced costs. In addition, the system focuses on the collection and pre-processing of input data for the simulator and optimiser and uses Message Queuing Telemetry Transport (MQTT)communication between the user interfaces and models to ensure seamless data exchange. The EAF Simulator uses a comprehensive mathematical model to simulate the complex dynamics of heat and mass transfer, while the EAF Optimiser uses a fuzzy logic-based approach to predict optimal energy consumption. The integration with Siemens Edge Streaming Analytics ensures robust data collection and real-time responsiveness. The dual-interface design improves user accessibility and operational flexibility. This system has significant potential to reduce energy consumption by up to 10% and melting times by up to 15%, improving the efficiency and sustainability of the entire process.

This paper studies the integration of direct reduced iron (DRI) and hot briquetted iron (HBI) into the steelmaking process via an electric arc furnace (EAF). Considering a variety of DRI production techniques distinguished by different reactor types, this paper provides a comparative overview of the current state. It delves into significant challenges, such as the susceptibility of DRI to reoxidation and the necessity of thorough handling to maintain its quality. The effectiveness of several reoxidation mitigation strategies, including the application of thin oxide layers, briquetting, various coatings, and nitride formation in ammonia-based reduction processes, is evaluated. Most existing studies have primarily focused on the reoxidation of DRI rather than on HBI, despite the fact that HBI may undergo reoxidation. The importance of DRI/HBI in offering an alternative to the integrated steelmaking route is highlighted, focusing on how it changes the EAF process compared to those for melting scrap. This paper also identifies several research prospects for further DRI/HBI applications in steel production.

This study investigated lignin as a reducing agent instead of fossil carbon for the reduction of zinc oxide and zinc ferrite contained in steelmaking dusts. Three types of dusts from different steelmaking processes were considered: ferrochrome converter (CRC), electric arc furnace stainless steel (EAFSS) and electric arc furnace carbon steel (EAFCS). Zinc is primarily found in zincite phases within CRC dust, while EAFSS and EAFCS dusts contain franklinite and zincite phases as Zn-bearing minerals. The proximate analysis of lignin showed that the fixed carbon content is 28.9%. Thermogravimetric (TG) analysis coupled with differential scanning calorimetry (DSC) and mass spectrometry (MS) was used to study the reduction behavior of different mixtures of lignin and steel dusts under inert and air atmospheres. Simultaneously, the minimum ratio of lignin out of three different proportions required to achieve a complete reduction of franklinite and zincite phases into metallic zinc was identified. The results indicated that a 1.1 stoichiometric amount of lignin is sufficient for the complete reduction of zinc-bearing minerals into metallic zinc. In conclusion, lignin can be used efficiently for processing steelmaking dusts.

The electric arc furnace (EAF) is considered the second most important process for the production of crude steel and is usually used for the melting of scrap. With the current emphasis on defossilization, its share in global steelmaking is likely to further increase. Due to the large production quantities, minor improvements to the EAF process can still accumulate into a significant reduction in overall energy and resource consumption. A major aspect in the efficient operation of the EAF is achieving beneficial slag properties, as the slag influences the composition of the steel and can reduce energy losses as well as the maintenance cost. In order to investigate the EAF operation, a dynamic process model is applied. Within the model, the chemical reactions of the metal–slag system are calculated based on the activities of the involved species. In this regard, multiple models for the calculation of the chemical activities have been implemented. However, depending on the chosen model, the computation of the slag activities can be computationally demanding. For this reason, the application of a neural network for the calculation of the chemical activities within the slag is investigated. The performance of the neural network is then compared to the results of the previously applied models by using the commercial software FactSage as a reference. The validation shows that the surrogate model achieves great accuracy while keeping the computation demand low.

A considerable portion of the energy consumed in the steel industry is rejected as waste heat from the electric arc furnace. Capturing this energy impacts the efficiency of production significantly by reducing operating costs and increasing the plant’s productivity. It also presents great opportunities to increase the industry’s competitiveness and sustainable operation through a reduction in emissions. This work presents an assessment of steel manufacturing and demonstrates the potential of thermal energy storage systems in recovering heat from the high-temperature exhaust fumes of the electric arc furnace. Our investigation entails mapping the material and energy requirements of one of two-phase of the current steel production method, i.e. natural gas reforming for syngas production, direct reduction of the iron ore, and secondary refining to obtain the steel in the electric arc furnace. Analysis of an obtained electric arc furnace off-gas temperature and flow rate profiles are then used as a basis for the development of a waste heat recovery model. Simulation results from the waste heat recovery module reveal that in a period of 4 days, an output power of 2108 kW per tap-to-tap cycle can be achieved from a continuous charge electric arc furnace. This can be harnessed and used either internally or externally in the steel manufacturing process. This is inevitably coupled with a reduction in CO2 emissions, which works to actively address climate change.

Electric arc furnace has a variable power consumption with time that causes mechanical oscillations in the adjacent small scale synchronous generators, and consequently, decreases their life. In this paper, we first provide a model for electric arc furnace and validate it using actual measured data. In this paper a real distribution system consisting of the small-scale synchronous generator, which feeds industrial loads, including electric arc furnaces, is analyzed and evaluated based on the simulation of the time domain. Using analytical studies and simulation results, the appropriate value for controller parameters of the prime mover is determined in such a way that the mechanical oscillations of the small-scale synchronous generator created by the electric arc furnace are reduced. In addition, the response of the transient mode of the small-scale synchronous generator is also considered in the proper setting of the parameters. Finally, reliability and effectiveness of the proposed parameters are evaluated using real measurement data. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of biochar, either as an individual component or in combination with high-temperature coke, on the slag foaming behavior. High-temperature coke serves as a reference. Three scenarios were considered to study the slag foaming behavior, each characterized by different slag chemical compositions. The results indicate that biochar can promote steady foaming for specific slags when the basicity (CaO/SiO2) falls within a range of 1.2 to 3.4. Experimental findings also reveal that stable foaming can be achieved when a mixture containing biochar and coke with a ratio of 1:1 is employed, with a minimum slag basicity of 1.0 and FeO content of 25 wt.%. The foaming range obtained using different FeO contents (15 wt.% to 40 wt.%) in the mixture surpasses the range observed with the individual application of coke or biochar. The X-ray diffraction (XRD) analysis showed that unrelated to the carbon source applied, the general pattern was that the phases larnite (Ca2SiO4) or dicalcium silicate were detected for slag foams with high basicity. Monticellite (CaMgSiO4) and magnesium iron oxide (Fe2MgO4) were predominant in slag foam samples, with the highest MgO content. The presence of monticellite and merwinite (Ca3MgSi2O8) occurred in samples with the lowest basicity. Eventually, the application of the mixture of coke and biochar showed the potential to obtain stable foaming across a wide range of slag compositions. [ABSTRACT FROM AUTHOR]

In electric arc furnace (EAF) steelmaking, oxygen jets play a crucial role in controlling stirring ability, chemical reactions, and energy consumption. During the EAF lifetime, refractory wear leads to a decrease in the molten steel level and an increase in the nozzle-to-steel distance, thereby negatively affecting the overall energy efficiency of the process. The objective of this study is to optimize the energy efficiency of the EAF refining process by adjusting the nozzle flow conditions and conducting an analysis of jet performance using computational fluid dynamics (CFD) simulation. Three types of injection jets were considered: the conventional jet, the CH4 coherent jet, and the CH4 + O2 coherent jet. The findings reveal that the shrouded flame of the coherent jet enhances jet performance by maintaining the maximum velocity, extending the potential core length, and increasing the penetration depth in the molten steel bath. To maintain the jet performance in response to an increased nozzle-to-steel distance resulting from refractory wear, transitions from the conventional jet to the CH4 coherent jet and the CH4 + O2 coherent jet are recommended once the nozzle-to-steel distance increases from its initial level of 1000 mm to 1500 mm and 2000 mm, respectively. [ABSTRACT FROM AUTHOR]

Background: Despite its significant impact on the global economy, the iron and steel industry is also the largest consumer of energy and leaves behind a significant environmental footprint. Iran is also one of the top 10 steel producing countries, with more than 77% of its steel being produced in electric arc furnaces (EAFs). Therefore, a proper environmental assessment should be done to minimize the negative effects on the environment. The purpose of this study was to evaluate the life cycle of steel production using EAF method in Iran. This study identifies the major processes and inputs that affect the environmental impact of steel production and proposes effective methods to prevent pollution. Methods: In this study, SimaPro software with ecoinvent database and IMPACT 2002+ evaluation method were used. Life cycle inventory data were obtained from the average performance of factories in one year and the functional unit was considered to be one ton of rolled steel (coil). Findings: Among different categories of environmental impacts, non-renewable energy, global warming, and inhalation of mineral particles had the most significant threat, respectively, so that these three categories included 86.5% of the total environmental impact. Among several processes, the greatest effects were related to EAFs (35%), followed by sponge iron production (28.9%) and pellet production (17.1%) in second and third places, respectively. The highest environmental impacts of input materials included electricity (33.8%) and gas consumption (25.8%), respectively. Conclusion: Despite significant advances in iron and steel industries in recent decades, they still have high energy consumption and carbon dioxide emissions. Conducting a life cycle assessment allows steel producers to identify the most polluting processes in order to make the necessary plans to improve them.

This paper presents new directions in the modeling of electric arc furnaces. This work is devoted to an overview of new approaches based on random differential equations, artificial neural networks, chaos theory, and fractional calculus. The foundation of proposed solutions consists of an instantaneous power balance equation related to the electric arc phenomenon. The emphasis is mostly placed on the conclusions that come from a novel interpretation of the equation coefficients.

Article highlights Receiving radiative heat flux of a water cooling panel (WCP) of an Electric Arc Furnace (EAF) is calculated numerically using the S2S model. Temperature distribution and hot spots is determined on the outer wall of CWP. Heat transfer of WCP is characterized numerically for Al2O3/Water nanofluid at various particle concentrations. Smoothing temperature distribution and hot spot removal are dedicated as the results when using nanofluid.

Thermocline thermal energy storage systems play a crucial role in enhancing energy efficiency in energy-intensive industries. Among available technologies, air-based packed bed systems are promising due to their ability to utilize cost-effective materials. Recently, one of the most intriguing filler materials under study is steel slag, a byproduct of the steel industry. Steel slag offers affordability, ample availability without conflicting usage, stability at temperatures up to 1000 °C, compatibility with heat transfer fluids, and non-toxicity. Previous research demonstrated favorable thermophysical and mechanical properties. Nonetheless, a frequently overlooked aspect is the endurance of the slag particles, when exposed to both mechanical and thermal stresses across numerous charging and discharging cycles. Throughout the thermal cyclic process, the slag within the tank experiences substantial loads at elevated temperatures, undergoing thermal expansion and contraction. This phenomenon can result in the deterioration of individual particles and potential damage to the tank structure. However, assessing the extended performance of these systems is challenging due to the considerable time required for thermal cycles at a relevant scale. To address this issue, this paper introduces a specially designed fast testing apparatus, providing the corresponding testing results of a real-scale system over 15 years of operation.

The iron and steel industry significantly contributes to global energy use and greenhouse gas emissions. The rising deployment of volatile renewables and the resultant need for flexibility, coupled with specific challenges in electric steelmaking (e.g., operation optimization, optimized power purchasing, effective grid capacity monitoring), require accurate energy consumption and demand forecasts for electric steel mills to align with the energy transition. This study investigates diverse approaches to forecast the energy consumption and demand of an electric arc furnace—one of the largest consumers on the grid—considering various forecast horizons and objectives with limited knowledge on process parameters. The results are evaluated for accuracy, robustness, and costs. Two grid connection capacity monitoring approaches—a one-step and a multi-step Long Short-Term Memory neural network—are assessed for intra-hour energy demand forecasts. The one-step approach effectively models energy demand, while the multi-step approach encounters challenges in representing different operational phases of the furnace. By employing a combined statistic–stochastic model integrating a Seasonal Auto-Regressive Moving Average model and Markov chains, the study extends the forecast horizon for optimized day-ahead electricity procurement. However, the accuracy decreases as the forecast horizon lengthens. Nevertheless, the day-ahead forecast provides substantial benefits, including reduced energy balancing needs and potential cost savings.

Considering the pivotal role of ferroalloys in the steel industry and the escalating global emphasis on sustainability (e.g., zero emissions and carbon neutrality), the demand for ferroalloys is anticipated to increase. However, the electric arc furnace (EAF) of ferroalloy plants generates substantial amounts of nitrogen oxides (NOx) because of the high-temperature combustion processes. Despite the substantial contributions of many studies on NOx prediction from various industrial facilities, there is a lack of studies considering the environmental condition of the EAF in ferroalloy plants. Therefore, this study presents a deep learning model for predicting NOx emissions from ferroalloy plants and further can provide guidelines for predicting NOx in industrial sites equipped with electric furnaces. In this study, we collected various historical data from the manufacturing execution system of electric furnaces and exhaust gas systems to develop a prediction model. Additionally, an interpretable artificial intelligence method was employed to track the effects of each variable on the NOx emissions. The proposed prediction model can provide decision support to reduce NOx emissions. Furthermore, the interpretation of the model contributes to a better understanding of the factors influencing NOx emissions and the development of effective strategies for emission reduction in ferroalloys EAF plants.

In steel recycling, the optimization of Electric Arc Furnaces (EAFs) is of central importance in order to increase efficiency and reduce costs. This study focuses on the optimization of electric arcs, which make a significant contribution to the energy consumption of EAFs. A three-phase equivalent circuit integrated with the Cassie–Mayr arc model is used to capture the nonlinear and dynamic characteristics of arcs, including arc breakage and ignition process. A particle swarm optimization technique is applied to real EAF data containing current and voltage measurements to estimate the parameters of the Cassie–Mayr model. Based on the Cassie–Mayr arc parameters, a novel Arc Quality Index (AQI) is introduced in the study, which can be used to evaluate arc quality based on deviations from optimal conditions. The AQI provides a qualitative assessment of arc quality, analogous to indices such as arc coverage and arc stability. The study concludes that the AQI serves as an effective operational tool for EAF operators to optimize production and increase the efficiency and sustainability of steel production. The results underline the importance of understanding electric arc dynamics for the development of EAF technology.

The paper reports on the behaviour and dynamics of direct current electric arc in an industrial electric arc furnace. Electric arcs are intense energy sources involved in many industrial processes. The behaviour of electric arc is simulated in a 2D axisymmetric geometry. A 40 kA current flows between two electrodes with a gap of 0.25 cm. The flow of current creates a very powerful jet up to km/s. Such speeds pose the question of the importance of compressibility and what is the extent of the effects of compressibility on arc behaviour. To assess the effect of compressibility, two different simulations are performed: an incompressible and a compressible simulation. The first simulation considers a temperature-dependent density based on experimental measurement whereas the latter adopts the ideal gas law to calculate the density variation of the plasma. The incompressible results were previously validated and compared to the well-known results predicted by the literature. The numerical results of the two models are reported and compared in terms of flow, thermal fields, and voltage drop. The results show that compressibility affects several aspects of the arc. As expected, the velocity drops when compressibility is present, however, the voltage drop increase significantly. Additionally, compressibility introduces a repetitive pattern of voltage drop over time which also depicted in the arc dynamics. The pattern is divided into three distinctive dynamics: (1) High-frequency low amplitude instabilities, followed by (2) low low-frequency high amplitude instabilities region, and finally, a relatively (3) stable interval of voltage with a bell-shaped arc.

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In this work, a practical approach for a decision support system for the electric arc furnace (EAF) is presented, with real-time heat state monitoring and control set-point optimization, which has been developed within the EU-funded project REVaMP and applied at the EAF of Sidenor in Basauri, Spain. The system consists of a dynamic process model based on energy and mass balances, including thermodynamic calculations for the most important metallurgical reactions, with particular focus on the modelling of the dephosphorisation reaction, as this is a critical parameter for production of high-quality steel grades along the EAF process route. A statistical scrap characterization tool is used to estimate the scrap properties, which are critical for reliable process performance and accurate online process control. The underlying process models and control functions were validated on the basis of historical production and measurement data of a large number of heats produced at the Sidenor plant. The online implementation of the model facilitates the accurate monitoring of the process behaviour and can be applied for exact process end-point control regarding melt temperature as well as oxygen, carbon and phosphorus content. Embedded within a model predictive control concept, the model can provide useful advice to the operator to adjust the relevant set-points for energy and resource-efficient process control. [ABSTRACT FROM AUTHOR]

The influence of different carbon sources, including anthracite, calcined petroleum coke, three samples of high-temperature coke, biochar, and a mixture of 50 wt.% biochar and 50 wt.% coke, on slag foaming behavior was studied. The slag's composition was set to FeO-CaO-Al2O3-MgO-SiO2, and the temperature for slag foaming was 1600 °C. The effect of the carbon sources was evaluated using foaming characteristics (foam height, foam volume, relative foaming height, and gas fraction), X-ray diffraction (XRD), chemical analysis of the slag foams, Mossbauer spectroscopy, observation by scanning electron microscope (SEM), and energy-dispersive spectroscopy (EDS) mapping. Different foaming phenomena were found among conventional sources, biochar as a single source, and the mixture of coke and biochar. Biochar showed the most inferior foaming characteristics compared to the other studied carbon sources. Nevertheless, the slag foaming process was improved and showed slag foaming characteristics similar to results obtained using conventional carbon sources when the mixture of 50 wt.% coke and 50 wt.% biochar was used. The XRD analysis revealed a difference between the top and bottom of the slag foams. In almost all cases, a maghemite crystalline phase was detected at the top of the slag foams, indicating oxidation; metallic iron was found at the bottom. Furthermore, a difference in the slag foam (mixture of coke and biochar) was found in the presence of such crystalline phases as magnesium iron oxide (Fe2MgO4) and magnetite (Mg0.4Fe2.96O4). Notwithstanding the carbon source applied, a layer between the foam slag and the crucible wall was found in many samples. Based on the SEM/EDS and XRD results, it was assumed this layer consists of gehlenite (Ca2(Al(AlSi)O7) and two spinels: magnesium aluminate (MgAl2O4) and magnesium iron oxide (Fe2MgO4). [ABSTRACT FROM AUTHOR]