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The iron and steelmaking industries play a significant role in the manufacturing sector but result in significant greenhouse gas emissions. Biochar has recently gained attention as a potential substitute for coal in metallurgical processes due to its carbon capture potential. This review explores the potential of biochar as a sustainable substitute for coal in steelmaking industries. Notable research works have shown that substituting biochar in amounts ranging from as low as 5 % to as high as 50 % can be feasible and beneficial in processes such as coke making, iron sintering, blast furnaces, and electric furnaces. The information presented in this review can be applied to create sustainable and competitive alternatives to fossil fuels to help decarbonize metallurgical industries. [ABSTRACT FROM AUTHOR]

For today's high‐quality steel production, good control of slag composition is essential in secondary steelmaking. However, the conventional slag analysis practice, involving sampling, sample preparation, and analysis, is very time‐consuming. This work is the first step toward an investigation of infrared (IR)‐based systems and can be used for online slag composition monitoring using the principle that different slag compositions have different emissivities in the IR wavelength range. Therefore, this work experimentally determines emissivity values of slags as a function of composition at steelmaking temperature, since available data for slags are very limited in the literature. The emissivities of three different slag compositions belonging to the Al2O3–CaO–SiO2–MgO system are investigated at 1773 K. The investigated emissivities are in the range of 0.75–0.87, with the best repeatability seen in the slag which is fully liquid at 1773 K. Variations in emissivities are observed within the other slags due to the presence of solid phases. Although the data clearly indicate a difference of emissivities as a function of slag composition, further experiments must be performed to evaluate the emissivities of other characteristic slags at different temperatures in order to further assess the applicability of IR‐based systems for slag composition control. [ABSTRACT FROM AUTHOR]

The steelmaking process involves a series of operations, including decarburization, desulfurization, dephosphorization, and deoxygenation. Therefore, this process is controlled by complex multifactors such as high and inhomogeneous reactor temperatures, simultaneous multiphase chemical reactions, and mutual coupling between phases in terms of mass, momentum, and heat transfer. Accurate prediction and control of the steelmaking process has always been a difficult problem in the ironmaking and steelmaking industries and a popular topic in metallurgy. The reaction thermodynamics and kinetics are theoretical bases for controlling the steelmaking process. Recent advances in reaction thermodynamics and kinetics and their application in numerical simulation are summarized in the present study. In terms of reaction thermodynamics, the main models for calculating the activity of liquid steel are the Wagner interaction parameter formalism (WIPF), the unified interaction parameter formalism, and the associate model. At present, the WIPF model is still the most widely used model for calculating liquid-steel activity, but with the development of new steel grades, the universality of the WIPF model has been challenged. An urgent need exists to develop a new model for liquid-steel activity calculations and to supplement it with new data. The main models for calculating slag activity are molecular theory, ionic theory, the regular ionic solution model, the modified quasi-chemical model (MQM), and ion and molecular coexistence theory (IMCT). The MQM and IMCT models are the most widely used for calculating slag activity. Reaction kinetic models such as the multicomponent coupled reaction model, the effective equilibrium reaction zone model, and the unreacted nucleus model can accurately predict changes in the molten steel, slag, and nonmetallic inclusions during the steelmaking process. However, the mass transfer coefficients in these kinetic models are mostly determined using empirical equations, which cannot accurately characterize the kinetics in different reactors. To address this problem, the three-dimensional distribution of the molten steel composition and its time evolution during the iron desulfurization process in Kambara reactor and the steel decarburization process in Ruhrstah-Hereaeus reactor were revealed by coupling the reaction kinetics with three-dimensional numerical simulations. However, no mature three-dimensional numerical simulations are available for multiphase and multidimensional reactions with the integrated consideration of molten steel, slag, inclusions, refractory materials, and alloys, which requires further in-depth study.

Based on the concept of green low-carbon and high-efficiency smelting, Fe-C alloys were prepared using industrial pure iron and high-purity graphite powder, and thermal experiments were carried out using a high-tem perature tube furnace, and it was found that with the increase of blowing time, the carbon content in the molten pool was reduced subsequently; The reaction rates of CO2 with (C) were 11,03 %, 12,94 %, 16,51 %, and 18,75 % at blowing flow rates of 10, 20, 30, and 40/ml·min-1, respectively; The decarburization rate increased from 0,0172 %/min to 0,0289 %/min, and the decarburization reaction rate is subsequently increased 1,68 times.

To solve the problems of low accuracy, slow detection speed, and difficulty in deploying model parameters in surface defect detection of continuous casting production process, a lightweight surface defect detection algorithm YOLOv7-TSCR that integrates heavy parameterization and attention mechanism was proposed. Firstly, based on the Mish and SiLU activation functions and the SimAM attention mechanism, an improved high-efficiency layer aggregation module ELAN-S was constructed to effectively enhance the extraction of multi-scale defect features. Secondly, the C2f_RG module was designed to improve the feature fusion network, reducing the number of parameters while obtaining richer gradient flow information and enhancing feature fusion capabilities. Finally, based on the collected defect images from actual production, a dataset of casting defects was constructed and validated. The results show that YOLOv7-TSCR has significantly improved detection performance compared to other network models；With a reduced number of model parameters, the accuracy reaches 93.5%, the average accuracy increasesby 2.8%, and the detection speed reaches 120 FPS; The generalization comparison experiment on the NEU-DET public dataset proves that the algorithm has strong generalization. On the basis of ensuring high detection accuracy, the improved algorithm has a fast detection speed and a small number of parameters, which provides a technical reference for the efficient detection of surface defects in casting billets.

The objective of conducted research on the hot metal desulfurization process was to determine the key process parameters that impact the ultimate outcome of desulfurization. As a result, the noticeable outcome of implementing these measures should be the improvement of quality control. In order to determine these parameters, used artificial intelligence methods like as neural networks (ANN). On the basis of the production data collected from the actual metallurgical aggregate for hot metal desulfurization, neural networks were built that used quantitative data (mass of hot metal, mass of used reagents, etc.) and qualitative data (chemical analysis of hot metal). The parameters of the desulfurization process were divided into state parameters and control parameters. From the point of view of the technology of conducting the desulfurization process and building an on-line model, only control parameters can be changed during desulfurization. To describe the problem of predicting change in the sulfur content during the hot metal desulfurization process is sufficient an MLP type neural network with a single hidden layer. Adopting a more complex network structure would probably lead to a loss of the ability to generalise the problem. The research was carried out in STATISTICA Automated Neural Networks SANN.

Copyright of Journal of Hebei University of Science & Technology is the property of Hebei University of Science & Technology, Journal of Hebei University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

The metallurgical sector is one of the most emission- and energy-intensive industries. The possibility of using fossil carbon substitutes has been investigated to reduce the environmental impact of the steelmaking sector. Among others, biochar emerged as a promising fossil coal/coke substitute. We conducted a literature review on biochar use in the metallurgical sector and its potential environmental benefits. The possibility for biochar as a coal/coke substitute is influenced by the source of biochar production and the process within which it can be used. In general, it has been observed that substitution of biochar ranging from a minimum of 5% to a maximum of 50% (mostly around 20–25%) is possible without affecting, or in some cases improving, the process, in coke making, iron sintering, blast furnaces and electric furnaces application. In some studies, the potential CO2 reduction due to biochar use was estimated, ranging from 5% to about 50%. Despite there still being an area of further investigation, biochar appeared as a promising resource with a variety of uses in the metallurgical sector, contributing to the lowering of the environmental impact of the sector. [ABSTRACT FROM AUTHOR]

According to a 2024 World Economic Forum survey, climate change is the primary concern for the future. To address these challenges, adaptation measures and actions to reduce or avoid greenhouse gas emissions are necessary. Emissions from the waste management sector rank as the seventh-largest contributor to global emissions. Efforts are underway to decarbonize steel processes and promote waste reuse. "Fluff", generated during ferrous scrap processing, poses reuse challenges due to its characteristics. This study evaluates the production of wood–plastic composites using polypropylene and "Fluff" to address environmental concerns and reduce greenhouse gas emissions. Methodologically, waste characterization preceded the extrusion of waste and polypropylene blends at varying compositions. The resulting materials were pelletized, molded, and characterized. CO2 emission reductions from waste diversion from landfills were assessed under two scenarios. In Scenario 1, emissions totaled 19,054 tons/year, while in Scenario 2, with gas capture and utilization, emissions reached 10,617 tons/year for 20,000 tons of waste annually. Composite characterization revealed favorable properties, suggesting viability for wood–plastic production. This approach supports industrial decarbonization and circular economy initiatives in the steel sector. [ABSTRACT FROM AUTHOR]

To solve the issue of oxygen consumption forecasting, the researchers suggested a twin support vector machine for regression (LAOA-TSVR) prediction model based on an improved arithmetic optimization algorithm. The model has beneficial generalization, high prediction accuracy, and the ability to jump out of the local optimum and other characteristics. The group used the method of mechanism analysis to determine the main influencing factors of oxygen consumption. To confirm the model's prediction effect, it is compared to the Back Propagation, Radial Basis Function, and Twin Support Vector Regression prediction models. The LAOA-TSVR oxygen consumption forecasting prediction model was then tested on actual steel mill production. The test phase consisted of 200 production cycles, and the results revealed that the LAOA-TSVR model had an 85,1 % hit rate for oxygen consumption within 5 m3/t. The model can suit the actual needs of predicting oxygen consumption in steel. [ABSTRACT FROM AUTHOR]

The objective of conducted research on the hot metal desulfurization process was to determine the key process parameters that impact the ultimate outcome of desulfurization. As a result, the noticeable outcome of implementing these measures should be the improvement of quality control. In order to determine these parameters, used artificial intelligence methods like as neural networks (ANN). On the basis of the production data collected from the actual metallurgical aggregate for hot metal desulfurization, neural networks were built that used quantitative data (mass of hot metal, mass of used reagents, etc.) and qualitative data (chemical analysis of hot metal). The parameters of the desulfurization process were divided into state parameters and control parameters. From the point of view of the technology of conducting the desulfurization process and building an on-line model, only control parameters can be changed during desulfurization. To describe the problem of predicting change in the sulfur content during the hot metal desulfurization process is sufficient an MLP type neural network with a single hidden layer. Adopting a more complex network structure would probably lead to a loss of the ability to generalise the problem. The research was carried out in STATISTICA Automated Neural Networks SANN. [ABSTRACT FROM AUTHOR]

The aim of the presented research was to optimize a pig iron desulfurization process through data-driven machine learning methods. Utilizing historical data, chemical analysis of pig iron and slag, and the thermodynamics of the process including simulations of the chemical reactions between individual phases, a neural network was trained for the predictive modeling of desulfurization efficiency. The accuracy of the model was enhanced by integrating Physics-Informed Neural Networks (PINNs), which incorporate chemical reaction principles. The results show better performance of PINNs in comparison to the Feedforward Neural Network (FNN) in the generalization of the desulfurization process, bringing better reliability to the model. [ABSTRACT FROM AUTHOR]

The basic oxygen steelmaking process is based on the CaO-FeO-SiO2 ternary slag system, characterized by a high melting point and low lime dissolution rate, often becoming one of the key factors limiting the efficiency of the converter. The bulk solid waste red mud, produced by the Bayer alumina process and rich in Fe2O3/Al2O3/Na2O, significantly reduces the melting point of the steelmaking slag system and enhances the efficiency of lime dissolution. This study utilized red mud as the main raw material to prepare a fluoride-free flux. An in situ online observation system was used to measure the melting point of the flux and the dissolution rate of lime in the flux. The results indicate that the melting point of the red mud-based flux is below 1200 °C, and under the same conditions, the lime dissolution rate is 10 to 15 times higher than when this flux is not used. Experiments in a 10 kg induction furnace show that using this flux, the dephosphorization rate under conditions without oxygen blowing is close to 40%, far higher than the rate achieved using CaF2. Under oxygen-blowing conditions, the dephosphorization rate using the red mud-based flux is comparable to that of CaF2, and significantly higher than without any flux, especially under high [C] content conditions. The data show that the red mud-based flux has the potential to be widely used as a fluoride-free flux in the steelmaking process. [ABSTRACT FROM AUTHOR]

Aiming at the temperature measurement in the refining process, a temperature measurement robot that can go deep into the furnace for temperature measurement is designed. Based on the D-H parameter modeling method, the kinematics of the manipulator is modeled, and the forward and inverse kinematics of the manipulator are solved. The workspace of the manipulator is simulated by Monte Carlo method on MATLAB, and the joint trajectory of the manipulator is planned by using Robotics Toolbox. The angular displacement, angular velocity and angular acceleration curves of each joint are obtained through simulation. The simulation results show that the manipulator runs smoothly and continuously, meeting the requirements of kinematics.

The use of CO2 for molten steel refining is an important supplement to the application of CO2 resources in the steelmaking process. Based on the latest research progress of CO2 used in molten steel refining, the metallurgical reaction behavior of CO2 gas in refining molten steel was analyzed, focusing on the use of CO2 as a protective gas, stirring gas or reaction gas in normal pressure and vacuum refining processes, and the application status in the process of refining outside the furnace. Under normal pressure conditions, CO2 can prevent nitrogen growth in molten steel, regulate the oxidation of molten steel, and extend the lifespan of bottom blow components; Under vacuum conditions, CO2 can not only play its role under normal pressure, but also deeply decarburize, degas, and remove molten steel. The effect of inclusions is more pronounced. Finally, the problems currently faced in the use of CO2 resources in the refining process were pointed out, and the future research on CO2 resource use in the refining process was suggested from the following two aspects: 1) Optimizing the CO2 injection process; 2) In-depth study of CO2 participation basic theory of steelmaking reactions.

In the present context of global decarbonization, the steelmaking sector plays a key role. It is indeed one of the so-called hard-to-abate sectors. The high emissions from steelmaking depend on the use of the Blast Furnace-Basic Oxygen Furnace (BF-BOF) route, which generates 1.8 tCO2eq per ton of Liquid Steel (LS). The production of direct reduced iron (DRI) to power an electric arc furnace is currently the most adopted solution to achieve the decarbonization goals. This alternative, based on the use of natural gas, offers a decarbonization potential of about 34% compared to the BF-BOF route. The most promising alternative, however, consists of using electrolysis-based hydrogen (H2) to produce DRI. This solution would drastically reduce direct and indirect process emissions but requires a radical energy transition. In the current transition phase, waste-based H2 production routes could be attractive, but their potential need to be evaluated with respect to the steelmaking process. To this concern, the objective of the present work is to assess the decarbonization potential of three waste-based H2 production routes (i.e., gasification, incineration and anaerobic digestion-based) with respect to the electrolysis-based steelmaking route. An environmental analytical model was therefore developed to evaluate the total (i.e., direct, indirect, and avoided) greenhouse gases emissions associated with the production of 1 ton of LS by employing the different routes. A sensitivity analysis was also carried out to understand the benefit provided by each waste-based H2 alternative in the current energy transition phase. The results obtained confirmed the need for radical emission reductions from electricity generation to make electrolysis-based H2 production environmentally favorable and revealed a high decarbonization potential for waste-based routes in the current energy transition phase. [ABSTRACT FROM AUTHOR]

The pressure of the global sustainability problem is growing faster than ever after the social and economic havoc wreaked by the pandemic, as well as the present time nearing to the 2050 net zero carbon goal. In 2021 the steel industry had a turnover of around €125 billion and was directly responsible for providing at least 300000 jobs. In 2022, it was responsible for roughly 5% of CO 2 emissions in the EU and 7% globally, and was responsible for at least 20278 injuries and 90 deaths. Environmental solutions for mitigating negative consequences of the steel industry exist such as hydrogen-based steelmaking, carbon capture, utilisation and storage and electrolysis, whilst safety regulations and personal protective equipment have been used to improve safety. However, little has been done to combat the problem using the most powerful emerging technology of the fourth industrial revolution, artificial intelligence. In particular, computer vision has already shown great competence in a range of applications related to steelmaking, but without seriously considering sustainability, resulting in limited awareness of the potential benefits computer vision can bring to industry. A lack of this awareness leads to missed opportunities for sustainable development. This paper aims to address the gap in research that discusses computer vision capabilities for enhancing the sustainability of steel production by providing a literature review covering recent advances in computer vision, as well as an industry 4.0 approach for integrating computer vision systems with steelworks. Research presented here successfully exposes the untapped potential of computer vision in the steel industry and paves the way for future developments by exhibiting a blueprint for simultaneously elevating sustainability and technological advancement. [ABSTRACT FROM AUTHOR]

In the pursuit of sustainable solutions for carbon dioxide CO2 sequestration and emission reduction in the steel industry, this study presents an innovative integration of steelmaking slag with the modified Solvay process for sodium bicarbonate (NaHCO3) synthesis from saline brines. Utilizing diverse minerals, including electric arc furnace (EAF) slag, olivine, and kimberlite, the study explored their reactivity under varied pH conditions and examined their potential in ammonium regeneration. SEM and WDXRF analyses were utilized to acquire morphological and chemical compositions of the minerals. Advanced techniques such as XRD and ICP-OES were employed to meticulously analyze mineralogical transformations and elemental concentrations. The findings demonstrate that steelmaking slag, owing to its superior reactivity and pH buffering capabilities, outperforms natural minerals. The integration of finer slag particles significantly elevated pH levels, facilitating efficient ammonium regeneration. Geochemical modeling provided valuable insights into mineral stability and reactivity, which aligned with the ICP-OES results. This synergistic approach not only aids in CO2 capture through mineral carbonation but also minimizes waste, showcasing its potential as a sustainable and environmentally responsible solution for CO2 mitigation in the steel industry. [ABSTRACT FROM AUTHOR]

A transient 3D CFD model is solved to investigate the features of the complex flow in a blast furnace trough. Hot metal, slag, and air are considered as different phases of the flow. The influence of various taphole stream conditions on the hydrodynamics in the trough is examined, such as different slag-hot metal ratios, taphole diameters, and stream velocities. The special case of a dry trough during its first tapping is also addressed. Attention is devoted to the characterization of the wall shear stress, closely related to mechanical erosion, and to the evolution of the interfaces separating the fluid phases. Intricate flow patterns, characterized by large recirculations and return currents of both slag and hot metal, are observed. The simultaneous tapping of slag and hot metal leads to distinct flow features, deviating from the initial stage, when only hot metal is drained, as well as lower shear stress values, up to 31% less with increasing slag content in the taphole stream. Slag and hot metal levels in the trough evolve rapidly to quasi-steady states. Although the influence of the taphole stream velocity and diameter have a modest impact on the free surface dynamics, variations in the taphole slag fraction lead to significant fluctuations in the depth of slag and hot metal pools in the trough. The height of the slag-iron free surface can increase by up to 40% relative to its initial level, while the variation in the slag pool depth approaches 30%. These changes in the interface positions carry potential implications for refractory wear, as they suggest a substantial shift of the slag-hot metal line during each tapping. [ABSTRACT FROM AUTHOR]

Featured Application: A framework to support steel operators in critical decision-making tasks. This paper proposes a human-in-the-loop framework that integrates machine learning models with semantic technologies to aid decision making in the domain of steelmaking. To achieve this, we convert a random forest (RF) into rules in a Semantic Web Rule Language (SWRL) format and represent real-world data as a knowledge graph in a Resource Description Framework (RDF) format, capturing the meta-data as part of the model. A rule engine is deployed that applies logical inference on the knowledge graph, resulting in a semantically enriched classification. This new classification is combined with external domain-expert knowledge to provide improved, knowledge-guided assistance for the human-in-the-loop system. A case study in the steel manufacturing domain is introduced, where this application is used for real-world predictive analytic purposes. [ABSTRACT FROM AUTHOR]

Copyright of Refractories / Naihuo Cailiao is the property of Naihuo Cailia (Refractories) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Advances in New & Renewable Energy is the property of Editorial Office of Advances in New & Renewable Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The direct carbonation of steel slag has emerged as a promising approach for carbon dioxide (CO2) utilization and sequestration, holding potential for advancing sustainable steel production. Despite considerably high expectations for these cleaner upcycling pathways, their maturity level remains relatively low and large-scale direct carbonation of steel slag is largely untested. To facilitate steel slag carbonation on a scale necessary for a zero-carbon future economy, this article provides a comprehensive review of fundamental carbonation mechanisms and critical parameters governing the reaction process, including temperature, pressure, reaction time, liquid-to-solid ratio, and CO2 partial pressure. The study critically examines the unique interactions among these process parameters, which can either limit or enhance the process optimization. The spectrum of scientific challenges associated with this pathway, including reaction rate limitations and the carbonated product valorization, particularly as a binder or aggregate in the construction sector, are identified and addressed. These insights aim to enhance the carbonation potential of steel slag for possible cleaner upcycling implementation pathways, ultimately facilitating the development of more efficient and sustainable carbon capture utilization and sequestration (CCUS) technologies. The proposed improvements are expected to be instrumental in promoting sustainable practices, not only to foster the decarbonization of the steelmaking industry but also in aiding other hard-to-abate sectors, such as the cement and concrete industry, in achieving their own decarbonization goals.

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.

A transient 3D CFD model is solved to investigate the features of the complex flow in a blast furnace trough. Hot metal, slag, and air are considered as different phases of the flow. The influence of various taphole stream conditions on the hydrodynamics in the trough is examined, such as different slag-hot metal ratios, taphole diameters, and stream velocities. The special case of a dry trough during its first tapping is also addressed. Attention is devoted to the characterization of the wall shear stress, closely related to mechanical erosion, and to the evolution of the interfaces separating the fluid phases. Intricate flow patterns, characterized by large recirculations and return currents of both slag and hot metal, are observed. The simultaneous tapping of slag and hot metal leads to distinct flow features, deviating from the initial stage, when only hot metal is drained, as well as lower shear stress values, up to 31% less with increasing slag content in the taphole stream. Slag and hot metal levels in the trough evolve rapidly to quasi-steady states. Although the influence of the taphole stream velocity and diameter have a modest impact on the free surface dynamics, variations in the taphole slag fraction lead to significant fluctuations in the depth of slag and hot metal pools in the trough. The height of the slag-iron free surface can increase by up to 40% relative to its initial level, while the variation in the slag pool depth approaches 30%. These changes in the interface positions carry potential implications for refractory wear, as they suggest a substantial shift of the slag-hot metal line during each tapping.

CONARC® steel making process is a combination of convertor steel making and electric arc steelmaking to get the benefit of both the process and make it flexible in terms of using raw material feed mix. Raw material feed mix in this furnace on an average is 60% hot metal (HM), 38% cold direct reduced iron (CDRI) and 2% steel scrap. In this furnace operation there are two phases, namely, the oxygen blowing phase and arcing phase followed by tapping of steel into the ladle. During the oxygen blowing phase, the HM carbon content is reduced from 4.5% to 0.3%–0.5%, and further reduced to 0.025–0.03% in the arcing phase depending upon the grade of steel produced. During the arcing stage, CoJet™ lances are used for the oxidation of the bath and reduction of the carbon content to the desired values. The end point %C parameter is very important in CONARC® steel making as it determines the productivity and quality of the steel produced. Based on the analysis, mathematical and machine learning approach was adopted to predict the end point %C during the arcing stage of the furnace. The algorithms which are used and compared are the tree-based models and support vector machines. After comparing the results, the tree based model seems best fit after further optimization to get an accuracy of 83%. The model was validated with plant trials and the accuracy was found to be within ±0.013 %C. [ABSTRACT FROM AUTHOR]

This paper critically evaluates the prevailing World Bank recommendation for city-regions to drive infrastructure-led development through targeted spatial plans. Introducing a novel analytical framework to conceptualize Duisburg's evolution into the primary European gateway for the EU–China transcontinental railway, the paper demonstrates how key actors and institutions operating across multiple scales enabled this previously deindustrializing German city to benefit from the new transcontinental rail connection. It argues that one-off spatial planning is insufficient for actualizing infrastructure-led development: this process involves a dynamic interaction with inherited industrial pathways that recursively stimulates the repurposing and/or the revision of infrastructure-oriented developmental plans. [ABSTRACT FROM AUTHOR]

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.

The basic oxygen steelmaking process is based on the CaO-FeO-SiO2 ternary slag system, characterized by a high melting point and low lime dissolution rate, often becoming one of the key factors limiting the efficiency of the converter. The bulk solid waste red mud, produced by the Bayer alumina process and rich in Fe2O3/Al2O3/Na2O, significantly reduces the melting point of the steelmaking slag system and enhances the efficiency of lime dissolution. This study utilized red mud as the main raw material to prepare a fluoride-free flux. An in situ online observation system was used to measure the melting point of the flux and the dissolution rate of lime in the flux. The results indicate that the melting point of the red mud-based flux is below 1200 °C, and under the same conditions, the lime dissolution rate is 10 to 15 times higher than when this flux is not used. Experiments in a 10 kg induction furnace show that using this flux, the dephosphorization rate under conditions without oxygen blowing is close to 40%, far higher than the rate achieved using CaF2. Under oxygen-blowing conditions, the dephosphorization rate using the red mud-based flux is comparable to that of CaF2, and significantly higher than without any flux, especially under high [C] content conditions. The data show that the red mud-based flux has the potential to be widely used as a fluoride-free flux in the steelmaking process.

The iron and steel industry remains one of the most energy-intensive activities with high CO2 emissions. Generally, the use of fossil coal as chemical energy in an electric arc furnace (EAF) makes up 40–70% of the total direct emissions in this steelmaking process. Therefore, substituting conventional fossil fuels with alternatives is an attractive option for reducing CO2 emissions. In this study, the environmental impacts of EAF-produced steel were comprehensively assessed using pulverized hydrochar as the charged and injected material as a replacement for fossil coal. An environmental analysis was performed based on the LCA methodology according to the framework of ISO 14044. This study evaluated two different outlines: the use of fossil coal and its replacement with hydrochar from the winemaking industry as a carbon source in the EAF steelmaking process. The environmental impacts from the manufacturing of the hydrochar were calculated using different scenarios, including novel industrial ways to use vinasse as a moisture source for the co-hydrothermal carbonization of vine pruning and exhausted grape marc (EGM). The environmental impacts per unit of steel were reported as a function of the ratio between the fixed carbon of the injected material and the material amount itself. The results highlight the sustainability of the hydrothermal carbonization process and the use of the hydrochar in EAF steelmaking. Moreover, the electricity mix used for the EAF process has significant relevance. The main outline of the results might assist decision-makers to determine which technological route is most likely to be effective in reducing future CO2 emissions from the iron and steel industry. [ABSTRACT FROM AUTHOR]

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

Viscosity and surface tension strongly influence the efficiency of slag foam in metallurgical processes. An excellent foaming slag preserves heat and lowers the cost of smelting in an electric furnace. In this study, we investigated the viscosity, surface tension, and foaming efficiency of a 2.5CaO/SiO 2 -xAl 2 O 3 -yFeO-MgO slag. We also investigated the different valence oxygen ions by X-ray photoelectron spectroscopy (XPS). The results showed that with a gradual increase in the content of Al 2 O 3 , the viscosity initially increased and then decreased, and the changes in surface tension followed a similar pattern. The change in viscosity was caused by the increase in the degree of polymerization of the slag, which was determined by the competitive relationship between polymerization and the reduction in the stability of the overall network structure. Adding a small amount of Al 2 O 3 to the slag slightly increased the number of Al–O–Al structures, whereas adding a large amount of the Al 2 O 3 led to the formation of low-strength Al–O–Si structures, which reduced the stability of the network structure, thus reducing the viscosity. Because the surface tension is related to the concentration of non-bridging oxygens (NBOs), when the NBO content increased, the instability of the surface structure caused an increase in energy, thus increasing the surface tension. In addition, the CaO–SiO 2 –5MgO- x Al 2 O 3 - y FeO five-element oxide in this study had the lowest surface tension at the same NBO concentration, which positively contributed to slag foaming. Finally, When the Al 2 O 3 content in the system increased from 5.1 to 15.7 wt%, the foaming efficiency increased from 24.2 to 69.2 (minute‧centimeters), an increase of 286%. [ABSTRACT FROM AUTHOR]

Given the accelerating depletion of iron ore resources, there is growing concern within the steel industry regarding the availability of high-phosphorus iron ore. However, it is important to note that the utilization of high-phosphorus iron ore may result in elevated phosphorus content and notable fluctuations in molten iron, thereby imposing additional challenges on the dephosphorization process in steelmaking. The most urgent issue in the process of converter steelmaking is how to achieve efficient dephosphorization. In this study, the influence of various factors on the logarithm of the phosphorus balance distribution ratio ( lg L p ), the logarithm of the P2O5 activity coefficient ( lg γ P 2 O 5 ), and the logarithm of the phosphorus capacity ( lg C p ) were examined through thermodynamic calculations. The impact of each factor on dephosphorization was analyzed, and the optimal conditions for the dephosphorization stage of the converter were determined. Furthermore, the influence of basicity and FetO content on the form of phosphorus in the slag was analyzed using FactSage 7.2 software, and the precipitation rules of the slag phases were explored. The thermodynamic calculation results indicated that increasing the basicity of the dephosphorization slag was beneficial for dephosphorization, but it should be maintained below 3. The best dephosphorization effect was achieved when the FetO content was around 20%. The reaction temperature during the dephosphorization stage should be kept low, as the dephosphorization efficiency decreased sharply with the increasing temperature. In dephosphorization slag, Ca3(PO4)2 usually formed a solid solution with Ca2SiO4, so the form of phosphorus in the slag was mainly determined by the precipitation form and content of Ca2SiO4. The phases in the dephosphorization slag mainly consisted of a phosphorus-rich phase, an iron-rich phase, and a matrix phase. The results of scanning electron microscopy and X-ray diffraction analyses were consistent with the thermodynamic calculation results. [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]

Understanding the micro mechanism of foaming behavior is urgently required for regulating metallurgical processes and preparing porous ceramics. In this study, we performed high-temperature simulation experiments on the foaming of CaO–SiO 2 -Fe x O-MgO-based slag induced by a slag/metal reaction. The evolution of the melt structure with changes in the chemical composition of the molten slag was analyzed using Raman spectroscopy. The micro mechanism of slag foaming was interpreted using the melt structure characteristics. As the w (CaO)/ w (SiO 2) and P 2 O 5 content varied, the maximum foaming height exhibited a positive relationship with the degree of polymerization of the experimental slag. In the bulk phase of the liquid film, an increase in the degree of polymerization of the experimental slag hindered the drainage of slag from the liquid film and reduced the solubility of the liquid film for CO gas in the bubble, leading to an increase in the maximum foaming height of the molten slag. Moreover, the increase in the degree of polymerization of the structural units at the liquid film/CO interface improved the elasticity of the liquid film and decreased the permeability of the liquid film for CO in the bubble, resulting in an increase in the maximum foaming height of the molten slag. The new perspective of ''chemical composition—melt structure—slag foaming'' also inspired studies on industrial processes that readily form foam, including glass manufacturing, oil recovery, and froth flotation. [Display omitted] [ABSTRACT FROM AUTHOR]

According to Neil Rollings and Laurent Warlouzet, the historical analysis of the European competition policy has been a priority in the research on institutions but the reaction of companies to these policies has received less attention. This study highlights the importance of analyzing how public policies affect business strategies in innovation. More specifically, how the policy adopted by the High Authority of the ECSC regarding the scrap market influenced the strategies implemented by the steelmakers in the innovation of their production processes. The High Authority banned exports and established maximum prices and a system to equalise internal prices with import prices. This policy was considered a success by both institutions and companies. It decisively influenced the scrap price to be maintained at affordable levels in Europe. But this success in resolving the scrap problem created a larger one as it delayed innovation. This was the scrap paradox suffered by Europe. [ABSTRACT FROM AUTHOR]

The discussion of energy efficiency in the steel ladle dispatching literature is currently limited to indirectly minimizing waiting and heating times. Not explicitly considering the ladle’s thermal balance may lead to sub-optimal solutions and safety concerns regarding the condition of the refractory lining. Hence, this paper studies the energy-efficient ladle dispatching problem with refractory temperature control. A mixed integer linear problem for ladle dispatching that integrates its energy balance is presented. It enables the global solution of the problem using state-of-the-art mixed integer programming solvers. This is achieved by applying piecewise linear models with logarithmic coding to approximate the energy balance. Computational results show that the number of breakpoints employed significantly affects the approximation quality and solution time. However, we show that the error does not affect the feasibility of the problem and yields a negligible difference of 1.4% in the objective function. Hence, this viable approach enables a proper discussion on the energy efficiency of ladle dispatching decisions. For a small but representative production scenario from Tata Steel, IJmuiden, we design and execute an experiment to define a set of operational rules and discuss the potential energy savings. We conclude by presenting the existing compromise between the CO2 emissions from re-heating the ladles and the reduction in the steel temperature losses from the improved thermal management of the ladles. We show that the average steel temperature losses can be reduced up to 3 °C depending on the refractory temperature requirement. This has the potential to unlock further savings for steelmakers.

Due to the advantages of Mg on inclusions and carbides in H13 steel, Mg wire was added into industrial ladle after VD process to replace traditional Ca treatment in this paper, which is of great significance for industrial production of H13 steel. This paper focuses on the effects of Mg treatment and traditional Ca treatment on the inclusions, carbides, microstructure and mechanical properties in H13 steel. The results show that feeding Mg wire during EAF→ LF→ VD → CC steelmaking process can ensure the submerged nozzle not being blocked under the condition of continuous casting 15 heats of steel ladle, which means feeding Ca wire can be replaced with Mg wire from the aspect of steelmaking production. The primary carbides in Mg treatment steel are mainly precipitated with MgO or MgAl2O4 as the cores and small in size, while the primary carbides in Ca treatment steel are almost large-size carbides without core. The samples taken from the intermediate ladle show that the primary carbide types are all V-rich, Mo-rich and V–Mo-rich composite, and the size of primary carbides in Mg treatment steel is much smaller than that in Ca treatment steel. Adding Mg is helpful to improve the equiaxed crystal rate of continuous casting ingot. The annealed hot rolling samples show that Mg treatment could interrupt the M23C6 (M = Cr, Fe) chain carbides distributed along the grain boundary. The impact test shows that the dimple fracture surface become small and deep after adding Mg, which improves impact toughness significantly.