Prospects for green steelmaking technology with low carbon emissions in China

The steel industry is a major source of CO_(2) emissions,and thus,the mitigation of carbon emissions is the most pressing challenge in this sector.In this paper,international environmental governance in the steel industry is reviewed,and the current state of development of low-carbon technologies is discussed.Additionally,low-carbon pathways for the steel industry at the current time are proposed,emphasizing prevention and treatment strategies.Furthermore,the prospects of low-carbon technologies are explored from the perspective of transitioning the energy structure to a“carbon-electricity-hydrogen”relationship.Overall,steel enterprises should adopt hydrogen-rich metallurgical technologies that are compatible with current needs and process flows in the short term,based on the carbon substitution with hydrogen(prevention)and the CCU(CO_(2) capture and utilization)concepts(treatment).Additionally,the capture and utilization of CO_(2) for steelmaking,which can assist in achieving short-term emission reduction targets but is not a long-term solution,is discussed.In conclusion,in the long term,the carbon metallurgical process should be gradually supplanted by a hydrogen-electric synergistic approach,thus transforming the energy structure of existing steelmaking processes and attaining near-zero carbon emission steelmaking technology.

Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.

State of the art in applications of machine learning in steelmaking process modeling

With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.

Nucleation and growth control for iron-and phosphorus-rich phases from a modified steelmaking waste slag

Recovering the iron(Fe)and phosphorus(P)contained in steelmaking slags not only reduces the environmental burden caused by the accumulated slag,but also is the way to develop a circular economy and achieve sustainable development in the steel industry.We had pre-viously found the possibility of recovering Fe and P resources,i.e.,magnetite(Fe_(3)O_(4)) and calcium phosphate(Ca_(10)P_(6)O_(25)),contained in steel-making slags by adjusting oxygen partial pressure and adding modifier B_(2)O_(3).As a fundamental study for efficiently recovering Fe and P from steelmaking slag,in this study,the crystallization behavior of the CaO-SiO_(2)-FeO-P_(2)O_(5)-B_(2)O_(3) melt has been observed in situ,using a confocal scanning laser microscope(CLSM).The kinetics of nucleation and growth of Fe-and P-rich phases have been calculated using a classical crys-tallization kinetic theory.During cooling,a Fe_(3)O_(4) phase with faceted morphology was observed as the 1st precipitated phase in the isothermal interval of 1300-1150℃,while Ca_(10)P_(6)O_(25),with rod-shaped morphology,was found to be the 2nd phase to precipitate in the interval of 1150-1000℃.The crystallization abilities of Fe_(3)O_(4) and Ca_(10)P_(6)O_(25) phases in the CaO-SiO_(2)-FeO-P_(2)O_(5)-B_(2)O_(3) melt were quantified with the in-dex of(T_(U)−T_(I))/T_(I)(where T_(I) represents the peak temperature of the nucleation rate and TU stands for that of growth rate),and the crystalliza-tion ability of Fe_(3)O_(4) was found to be larger than that of Ca_(10)P_(6)O_(25) phase.The range of crystallization temperature for Fe_(3)O_(4) and Ca_(10)P_(6)O_(25) phases was optimized subsequently.The Fe_(3)O_(4) and Ca_(10)P_(6)O_(25) phases are the potential sources for ferrous feedstock and phosphate fertilizer,respectively.

Decarbonization options of the iron and steelmaking industry based on a three-dimensional analysis

Decarbonization is a critical issue for peaking CO_(2) emissions of energy-intensive industries,such as the iron and steel industry.The decarbonization options of China’s ironmaking and steelmaking sector were discussed based on a systematic three-dimensional low-carbon analysis from the aspects of resource utilization(Y),energy utilization(Q),and energy cleanliness which is evaluated by a process general emission factor(PGEF)on all the related processes,including the current blast furnace(BF)-basic oxygen furnace(BOF)integrated process and the specific sub-processes,as well as the electric arc furnace(EAF)process,typical direct reduction(DR)process,and smelting reduction(SR)process.The study indicates that the three-dimensional aspects,particularly the energy structure,should be comprehensively considered to quantitatively evaluate the decarbonization road map based on novel technologies or processes.Promoting scrap utilization(improvement of Y)and the substitution of carbon-based energy(improvement of PGEF)in particular is critical.In terms of process scale,promoting the development of the scrap-based EAF or DR-EAF process is highly encouraged because of their lower PGEF.The three-dimensional method is expected to extend to other processes or industries,such as the cement production and thermal electricity generation industries.

Intelligent optimization method for the dynamic scheduling of hot metal ladles of one-ladle technology on ironmaking and steelmaking interface in steel plants

The one-ladle technology requires an efficient ironmaking and steelmaking interface. The scheduling of the hot metal ladle in the steel plant determines the overall operational efficiency of the interface. Considering the strong uncertainties of real-world production environments, this work studies the dynamic scheduling problem of hot metal ladles and develops a data-driven three-layer approach to solve this problem. A dynamic scheduling optimization model of the hot metal ladle operation with a minimum average turnover time as the optimization objective is also constructed. Furthermore, the intelligent perception of industrial scenes and autonomous identification of disturbances, adaptive configuration of dynamic scheduling strategies, and real-time adjustment of schedules can be realized. The upper layer generates a demand-oriented prescheduling scheme for hot metal ladles. The middle layer adaptively adjusts this scheme to obtain an executable schedule according to the actual supply–demand relationship. In the lower layer, three types of dynamic scheduling strategies are designed according to the characteristics of the dynamic disturbance in the model:real-time flexible fine-tuning, local machine adjustment, and global rescheduling. Case test using 24 h production data on a certain day during the system operation of a steel plant shows that the method and system can effectively reduce the fluctuation and operation time of the hot metal ladle and improve the stability of the ironmaking and steelmaking interface production rhythm. The data-driven dynamic scheduling strategy is feasible and effective, and the proposed method can improve the operation efficiency of hot metal ladles.

Properties and Characterization of Two Clays Raw Material from Mountain District (West of Côte d’Ivoire) for Use in Low-Carbon Cements

This work was devoted to the study of the physico-chemical properties of two clay minerals from the Mountain District (West Côte d'Ivoire) referenced ME1 and ME2. These samples were characterized by the experimental techniques, such as X-ray diffraction (XRD), Infrared spectroscopy (IR), Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES), Differential Thermal Analysis and Thermogravimetry (DTA-TG), Brunauer, Emett and Teller method (BET), laser particle size analysis and Scanning Electron Microscope (SEM). The main results of these analyses reveal that the two clay samples mainly contain quartz (52.91% for ME1 and 51.72% for ME2), kaolinite (36.60% for ME1 and 41.6% for ME2) and associated phases, namely goethite and hematite (13.47% for ME1 and 11.00% for ME2). The specific surface values obtained for samples ME1 and ME2 are 34.78 m2/g and 29.18 m2/g respectively. The results obtained show that the samples studied belong to the kaolinite family. After calcination, they could have good pozzolanic activity and therefore be used in the manufacture of low-carbon cements.

Does China's digital economy contribute to low-carbon transition?

The digital economy,as a new emerging economic form,has become an important power for realizing Chinese-style modernization and promoting green development in China.This paper measures the digital economy and low-carbon transition index based on the data of 30 provinces in China from 2013 to 2020 and analyzes the mechanism and path of the digital economy affecting low-carbon transition using the fixed effect panel data model and the threshold effect model.It is found that,(1)The digital economy and low-carbon transition in China are various in different regions,with characteristics of being unbalanced and insufficient.(2)The digital economy significantly promotes low-carbon transition,with the greatest influence in the Central region,followed by the Eastern region and the Western region.Under different dimensions,the development of informatization and digital transactions promote low-carbon transition,but the development of the internet plays an inhibiting role.(3)The higher the degree of urbanization and environmental regulation,the greater the influence of the digital economy on low-carbon transition.

Effect of ZrC Modified Graphite on Structure and Properties of Low-carbon Al_(2)O_(3)-C Refractories

To address the issues of reduced performance and shortened lifespan during the low-carbonizating process of Al_(2)O_(3)-C refractories,nano-crystalline ZrC modified graphite was prepared using Zr powder and flake graphite as raw materials,with NaCl and NaF mixed salt serving as the medium.The flake graphite was gradually replaced by ZrC modified graphite in the preparation of Al_(2)O_(3)-C refractories,and its impact on the material’s structure and properties was investigated.The results indicate that,compared to samples with only flake graphite,the introduction of 1 mass%to 5 mass%nano-crystalline ZrC modified graphite can significantly enhance the mechanical performance of low-carbon Al_(2)O_(3)-C refractories.When 5 mass%ZrC modified graphite is added,the mechanical properties of the samples are optimal,with the cold modulus of rupture and elastic modulus reaching 22.5 MPa and 65.0 GPa,respectively.

Performance Evaluation of Low-Carbon and Clean Transformation of China’s Coal Economy

In China,the oversupply of coal occurred in 2009,and from that year onwards,China’s coal economy began a low-carbon and clean transformation.Evaluating transformation performance is the research goal of this paper.The data collection for this paper includes data on deep processing of Chinese coal products from 2009 to 2020,as well as data on asset structure evolution and financial performance of 34 listed companies in the Chinese coal mining.Entropy value method is used to calculate the entropy value of low-carbon transformation,and the regression analysis is used to study the performance of cleaner transformation,the conclusion is as follows:(1)From 2009 to 2020,in China’s total energy consumption,coal consumption accounted for 71.6%in 2009 and 56.8%in 2020,the goals set by the state have been achieved.(2)The national goal of reducing the proportion of coal consumption and reducing carbon emissions has forced the transformation of deep processing of coal products.The transformation of coal enterprises towards low-carbon and clean production has achieved remarkable results.(3)From 2009 to 2020,the non coal industry income of 34 listed companies in China’s coal mining industry increased by 8.21%annually.At the same time,the asset structure was adjusted,and nearly 80%of the asset structure evolution showed an orderly development trend.(4)The regression analysis results show that the entropy value of coal deep processing products and the entropy value of asset structure adjustment are significantly related to transformation performance.The paper proposes to summarize the successful experience of China’s coal energy economic transformation,lay a foundation for achieving the carbon peak and carbon neutral goals in the future,further increase the intensity of coal deep processing,increase the proportion of clean energy in total energy consumption,and strive to control asset operation towards the goal of increasing the proportion of non coal industry income.

A Mechanism for Digital Finance to Drive Green and Low-Carbon Development

Given the global focus on green and low-carbon development and the increasing prominence of digital finance,it is particularly important to explore how to leverage digital finance to achieve these environmental goals.This study,through mechanism analysis,deeply examines how China’s digital finance promotes green and low-carbon development and elucidates the positive interaction between digital finance and the green industry.The study found that digital finance,through more flexible and efficient financial functions,alters the cost structure of carbon emissions,and reduces the risks and costs of green investments,thereby creating a cooperative green mechanism benefiting all parties,and guiding social groups toward a green and low-carbon transformation.Additionally,the rapid development of digital finance has strengthened the implementation of environmental protection policies,effectively promoted the expansion of the environmental protection industry,and established the green ethos as a mainstream concept in financial development.This study aims to provide reference perspectives and suggestions,assist policymakers in promoting the green and lowcarbon development of digital finance,and offer insights into the integrated development of digital finance and the green environmental protection industry.

Phosphate enrichment mechanism in CaO–SiO_2–FeO–Fe_2O_3–P_2O_5 steelmaking slags with lower binary basicity

The addition of silica to steelmaking slags to decrease the binary basicity can promote phosphate enrichment in quenched slag samples. In this study, we experimentally investigated phosphate enrichment behavior in CaO-SiO2-FeO-Fe203-P205 slags with a P205 content of 5.00% and the binary basicity B ranging from 1.0 to 2.0, where the （%Fe/O）/（%CaO） mass percentage ratio was maintained at 0.955. The experimental results are explained by the defined enrichment degree c, of solid solution 2CaO·SiO2-3CaO·P205 （C2S-C3P）, where R_C2S-C3P is a component of the developed ion and molecule coexistence theory （IMCT）-Ni model for calculating the mass action concentrations Ni of structural units in the slags on the basis of the IMCT. The asymmetrically inverse V-shaped relation be- tween phosphate enrichment and binary basicity B was observed to be correlated in the slags under applied two-stage cooling conditions. The maximum content of PROs in the C2S-C3P solid solution reached approximately 30.0% when the binary basicity B was controlled at 1.3.

Mass Balance Modeling for Electric Arc Furnace and Ladle Furnace System in Steelmaking Facility in Turkey

In the electric arc furnace （EAF） steel production processes, scrap steel is principally used as a raw material instead of iron ore. In the steelmaking process with EAF, scrap is first melted in the furnace and then the desired chemical composition of the steel can be obtained in a special furnace such as ladle furnace （LF）. This kind of furnace process is used for the secondary refining of alloy steel. LF furnace offers strong heating fluxes and enables precise temperature control, thereby allowing for the addition of desired amounts of various alloying elements. It also provides outstanding desulfurization at high-temperature treatment by reducing molten steel fluxes and removing deoxidation products. Elemental analysis with mass balance modeling is important to know the precise amount of required alloys for the LF input with respect to scrap composition. In present study, chemical reactions with mass conservation law in EAF and LF were modeled altogether as a whole system and chemical compositions of the final steel alloy output can be obtained precisely according to different scrap compositions, alloying elements ratios, and other input amounts. Besides, it was found that the mass efficiency for iron element in the system is 95.93%. These efficiencies are calculated for all input elements as 8. 45% for C, 30.31% for Si, 46.36% for Mn, 30.64% for P, 41.96% for S, and 69.79% for Cr, etc. These efficiencies provide valuable ideas about the amount of the input materials that are vanished or combusted for 100 kg of each of the input materials in the EAF and LF system.

Thermodynamic calculation on the smelting slag of direct recycling of electric arc furnace stainless steelmaking dust

Thermodynamic calculation on the smelting slag of direct recycling of electric arc furnace stainless steelmaking dust was presented. An induction furnace was used to simulate electric arc furnace smelting to recover the metals from the dust. The elements of iron, chromium and nickel in the ingot and the components of metal oxides in the slag were analyzed. The thermodynamic model for FeO Cr 2O 3 MgO SiO 2 slag was set up and the active concentrations of substances in the slag at 1 550 ℃ were determined by thermodynamic calculation according to the experimental data. The results show that the apparent equilibrium constant and quantitative distribution of chromium between slag and steel are unstable and affected by the mass ratios of pellets to start iron and metal reducing agent to the pellets. In order to get satisfactory chromium recovery from the direct recycling of electric arc furnace stainless steelmaking dust, it is important to ensure the mass ratio of pellets to the steel below 0.20 and the mass ratio of metal reducing agent to pellets over 0.18 in practical smelting runs.

Recycling of ironmaking and steelmaking slags in Japan and China

The mass production of steel is inevitably accompanied by large quantities of slags.The treatment of ironmaking and steelmaking slags is a great challenge in the sustainable development of the steel industry.Japan and China are two major steel producing countries that have placed a large emphasis on developing new technologies to decrease slag emission or promote slag valorization.Slags are almost completely reused or recycled in Japan.However,due to stagnant infrastructural investments,future applications of slags in conventional sectors are expected to be difficult.Exploring new functions or applications of slags has become a research priority in Japan.For example,the utilization of steelmaking slags in offshore seabeds to create marine forests is under development.China is the top steel producer in the world.The utilization ratios of ironmaking and steelmaking slags have risen steadily in recent years,driven largely by technological advances.For example,hot stage processing of slags for materials as well as heat recovery techniques has been widely applied in steel plants with good results.However,increasing the utilization ratio of basic oxygen furnace slags remains a major challenge.Technological innovations in slag recycling are crucial for the steel industries in Japan and China.Here,the current status and developing trends of utilization technologies of slags in both countries are reviewed.

Thermo-analytical study on stainless steelmaking dust

Thermo-gravimetric analyzer (TGA) was used to determine the thermal behavior of stainless steelmaking dust and FTIR was used to detect the components of off-gas. The TGA results indicate that three mass loss/gain stages exist in the thermal process. The mass loss of the dust in the first stage results from the evaporation of moisture and the reaction between dissociated carbon and metal oxides in the dust. The evaporation of moisture within the dust happens at 90-350 ℃ and the formation of carbon dioxide happens at 250-470 ℃. The mass gain of the dust in the second stage results from the oxidation of metals in the dust by the oxygen at 470-950 ℃. The third stage is a slow mass loss process, and some metals in the dust are evaporated into the atmosphere in this stage. The evaporation of metals is carried out mainly at 900-1 200 ℃ and the dust is sintered at high temperature over 1 200 ℃.

A review of carbon dioxide disposal technology in the converter steelmaking process

In last decade,the utilization of CO?resources in steelmaking has achieved certain metallurgical effects and the technology is maturing.In this review,we summarized the basic reaction theory of CO2,the CO2 conversion,and the change of energy-consumption when CO2 was introduced in converter steelmaking process.In the CO2-O2 mixed injection(COMI)process,the CO2 conversion ratio can be obtained as high as 80%or more with a control of the CO2 ratio in mixture gas and the flow rate of CO2,and the energy is saving and even the energy consumption can be reduced by 145.65 MJ/t under certain operations.In addition,a complete route of CO2 disposal technology is proposed combining the comparatively mature technologies of CO2 capture,CO2 compression,and liquid CO2 storage to improve the technology of CO2 utilization.The results are expected to form a large-scale,highly efficient,and valuable method to dispose of CO2.

An improved cross entropy algorithm for steelmaking-continuous casting production scheduling with complicated technological routes

In order to increase productivity and reduce energy consumption of steelmaking-continuous casting(SCC) production process, especially with complicated technological routes, the cross entropy(CE) method was adopted to optimize the SCC production scheduling(SCCPS) problem. Based on the CE method, a matrix encoding scheme was proposed and a backward decoding method was used to generate a reasonable schedule. To describe the distribution of the solution space, a probability distribution model was built and used to generate individuals. In addition, the probability updating mechanism of the probability distribution model was proposed which helps to find the optimal individual gradually. Because of the poor stability and premature convergence of the standard cross entropy(SCE) algorithm, the improved cross entropy(ICE) algorithm was proposed with the following improvements: individual generation mechanism combined with heuristic rules, retention mechanism of the optimal individual, local search mechanism and dynamic parameters of the algorithm. Simulation experiments validate that the CE method is effective in solving the SCCPS problem with complicated technological routes and the ICE algorithm proposed has superior performance to the SCE algorithm and the genetic algorithm(GA).

Theory and practice of the optimizing of charging structure in EAF steelmaking

The viewpoint about harmful residual element control, the charging structure and its influence on production index due to the diversification of raw material in EAF steelmaking was expatiated. The residual element control model, the concept of the proportion of iron and steel and the charging structure triangle were putted forward. Based on theoretical calculation and statistical analysis, the influence of charging structure on production index was discussed, and it was found that the utilizing efficiency of energy will reduce as the proportion of iron and steel in EAF steelmaking increases.

An improved artificial bee colony algorithm for steelmaking–refining–continuous casting scheduling problem

Steelmaking–refining–Continuous Casting(SCC) scheduling is a worldwide problem, which is NP-hard. Effective SCC scheduling algorithms can help to enhance productivity, and thus make significant monetary savings. This paper develops an Improved Artificial Bee Colony(IABC) algorithm for the SCC scheduling. In the proposed IABC, charge permutation is employed to represent the solutions. In the population initialization, several solutions with certain quality are produced by a heuristic while others are generated randomly. Two variable neighborhood search neighborhood operators are devised to generate new high-quality solutions for the employed bee and onlooker bee phases, respectively. Meanwhile, in order to enhance the exploitation ability, a control parameter is introduced to conduct the search of onlooker bee phase. Moreover, to enhance the exploration ability,the new generated solutions are accepted with a control acceptance criterion. In the scout bee phase, the solution corresponding to a scout bee is updated by performing three swap operators and three insert operators with equal probability. Computational comparisons against several recent algorithms and a state-of-the-art SCC scheduling algorithm have demonstrated the strength and superiority of the IABC.