Steel Enterprises Research Articles

Design and Site Selection of Reverse Logistics Network for Iron and Steel Enterprises from the Perspective of Green Supply Chain

The iron and steel industry is not only one of the most important components of China's national economy, but also a typical resource-and-energy-intensive industry. Under the background of "Carbon Peaking and Carbon Neutrality Goals", it is of great significance for the iron and steel industry to improve the green closed-loop supply chain and develop circular economy. From the perspective of green supply chain, this paper puts forward the three-level network structure of waste steel reverse logistics based on the present situation of waste steel recycling in China, and uses k-means clustering algorithm and accurate center of gravity method to select locations for the primary and secondary nodes. The purpose of this paper is to provide a basis for iron and steel enterprises to carry out network optimization of reverse logistics.

Optimization Study on Hardness of Cold Rolled Strip Steel Based on a Data-Driven Approach

As a key index to measure product quality and processability, the hardness of cold-rolled strip steel has an important influence on the production efficiency of iron and steel enterprises. However, due to the coupling between the process parameters at each stage of the continuous annealing process, and the setting of traditional process parameters depending on the experience of the operator, the lack of scientific and systematic optimization methods, which easily leads to the problem of high hardness fluctuation and low resource utilization. In this study, the data-driven method is used to conduct a preliminary correlation analysis to further clarify the optimal combination of process parameters for achieving the target hardness. The Pearson correlation coefficient is used to determine that carbon content, quenching furnace temperature, and strip speed significantly affect strip hardness. To further clarify the optimal combination of process parameters to achieve the target hardness, a Genetic Algorithm (GA) was tried in this study. The results show that the optimized process parameter combination can effectively improve the stability and consistency of strip hardness, and provide a scientific basis for the process parameter setting in the cold rolled strip production process, which is helpful for enterprises to improve product quality, reduce cost, and enhance market competitiveness.

Research on the Evaluation of Competitiveness of Steel Industry Enterprises

The steel industry, as a fundamental industry in the organic development framework of China's economy, has been facing the long-standing problems of production exceeding consumption, high asset liability ratio, severe product homogenization competition, and high pollution and carbon emissions in the production process in recent times. At the same time, due to the impact of the sluggish demand caused by the COVID-19, the rapid rise in the cost of raw materials such as iron ore, and the gradual disappearance of a series of policy dividends such as supply side reform, the financial situation of steel enterprises is becoming more and more pessimistic. How to cope with the increasing financial risks and gain competitive advantages through strengthening financial competitiveness in the fierce market competition has become a major issue that China's steel enterprises urgently need to solve. This article selects steel industry enterprises as the research object for analysis. By reviewing relevant literature on enterprise competitiveness, the relevant concepts of enterprise competitiveness are extracted, and the research results and evaluation system are organized. Based on factor analysis, 16 targeted indicators are selected based on scale, efficiency, growth, operational capacity, debt paying capacity, and research and development capacity. An analysis model is constructed and analysis conclusions are drawn. In the analysis results, problems are identified regarding the poor competitiveness of steel industry enterprises. The following research results were obtained: 1) The overall competitiveness score of enterprises in the industry is not high, all less than 1. Among them, operational capability has the greatest impact on the competitiveness of steel industry enterprises, followed by scale, debt paying ability, efficiency, research and development ability, and growth. 2) The worst scoring indicators among the six enterprise competitiveness indicators are the scale indicator and the growth indicator. There is a significant gap in industry scale, with 12 companies scoring positive in the scale factor, accounting for 34.29% of the industry; The scale factor score of 23 enterprises is negative, with an industry proportion of 65.71%. In addition, there are growth indicators, with 12 companies in the industry having positive growth factor scores and 23 companies having negative growth factor scores, indicating a significant head effect on overall growth capacity.

Low-Carbon Transformation of Chinese Steel Enterprises Under the Dual Carbon Goals: Taking Baowu Group as An Example

The profound changes in the environment have led to increasing global concerns about the negative impacts of carbon emissions. Against the backdrop of the environmental crisis, the concept of “dual carbon” emerged in China, aiming to achieve carbon peaking and carbon neutrality and mitigate the adverse effects of climate change. The establishment of these goals in 2020 marked a significant milestone in global efforts towards a more sustainable future. The paper initially delves into a comprehensive macro and micro analysis, reviewing significant policy measures introduced since the "dual carbon" goals were established in 2020. It pays particular attention to documents that have had a profound impact on the steel industry, emphasizing the urgency of low-carbon transformation among steel enterprises. Amidst the challenges posed by the macroeconomic environment and within the framework of the "dual carbon" policy, the paper takes Baowu Group, China's leading steel enterprise, as the focal point of this analysis. The paper meticulously scrutinizes seven specific measures implemented by Baowu Group in its journey towards low-carbon transformation, adopting a multifaceted perspective. It provides a comprehensive assessment of the effectiveness of Baowu Group's transformation from diverse perspectives, highlighting the successful outcomes of both Chinese government policies and corporate efforts. In conclusion, the paper presents tailored policy recommendations and transformation strategies tailored specifically for the steel industry.

Study on Oxygen-Enriched Combustion Characteristics of Blast Furnace Gas Boiler

ABSTRACT To study the dynamic characteristics of OEC (oxygen-enriched combustion) in a BFG (blast furnace gas) boiler, the BFG boiler of an iron and steel enterprise is taken as the research object, and a thermal test bed for OEC of BFG is set up, and a thermodynamic simulation model of the BFG boiler is constructed based on the heat loss of chemical incomplete combustion obtained from thermal test, using MSP (Multi-Subject Simulation Platform). The effects of OC (oxygen concentration) and the position of oxygen entering the furnace chamber on the key parameters of the OEC process of the BFG boiler under different working conditions were analyzed. The results showed that the excess air coefficient of 1.2 and exhaust temperature of 120°C was kept constant during the thermal test. With a fuel volume of 2.4 Nm3/h, for example, after the OC was increased from 21% to 40%, the combustion flame temperature in the furnace was increased by about 34°C, the heat loss due to unburned gas was reduced by 0.21%, and the boiler efficiency was increased by 1.38%. In the dynamic model, at the instant when the OC is changed, the disturbance of the whole system is relatively large, and the FAT (furnace average temperature) outlet, the pressure of the steam ladle, and the MST (main steam temperature) will have a step change, and then gradually level off again. With the increase of OC, the combustion reaction is more and more intense, the steam side of the heat transfer process is strengthened, the boiler efficiency is improved, and the FAT chamber outlet, the steam ladle pressure and the MST after OEC in different ways of oxygen entry into the furnace chamber will be longer and longer to reach a stable value. The results of the study can provide a reference for the OEC operation of BFG boilers.

When is the right time to invest in hydrogen metallurgy projects for iron and steel enterprises?

Hydrogen metallurgy technology is considered a key technology for the iron and steel industry to achieve carbon neutrality. However, it has been slow to be widely promoted in China due to factors such as stranded costs from carbon metallurgy equipment, hydrogen production costs, consumer preferences, and carbon costs. Based on these comprehensive consideration, this paper establishes a predictive model for investment timing of hydrogen metallurgy projects, takes an example of a Chinese steel enterprise with medium production scale. The research results show that the hydrogen metallurgy project with a hydrogen-rich blast furnace injection technology (hydrogen-rich BF) route is the most economically advantageous after 2026; after 2048, the net income of the hydrogen metallurgy project with a hydrogen-based shaft furnace direct reduction technology (hydrogen-based DRI) route will be greater than the stranded costs of carbon metallurgy projects, making it the most economically advantageous project. However, market dynamics and equipment variations significantly influence project viability.

Steel mill solid waste transformation: Optimizing converter sludge recycling through RHF technology

Current methods for treating converter sludge include reintroducing it into the sintering or pelletizing process. However, this method leads to the enrichment of zinc in the blast furnace, which affects the stability of the production operation. To solve this problem, a direct reduction process utilizing multi-solid waste collaborative rotary hearth furnace (RHF) has been implemented in steel plants to recover and safely dispose of valuable components in waste. The laboratory results show that the dezincization rate of the metallized pellets is 99.12 % and the metallization rate is 80.51 % under the premise of reducing 50 °C by calcination at 1200 °C for 15 min in the nitrogen atmosphere. In the industrial test conducted in RHF production line of Shagang from March 2022 to August 2023, the temperature in the high temperature zone was reduced by 50 °C, and the time in the high temperature zone was controlled to 15 min. The industrial test results showed that the residual zinc content in the metallized pellets was 0.63 %, and the metallization rate was about 75.12 %. These particles can be used as feedstock or coolant in the steelmaking process. The average grade of by-product zinc oxide coarse powder can reach 65.88 %, which can be further processed into zinc products with remarkable economic benefit. Through RHF integrated management of converter sludge and other solid waste in steel mills, metallized pellets and high-value products can be produced, which can effectively reduce solid waste in steel enterprises and improve resource utilization.

Towards Green and Low-Carbon Transformation via Optimized Polygeneration System: A Case Study of the Iron and Steel Industry

Polygeneration systems have significant potential for energy conservation and emission reduction and can effectively promote green and low-carbon development in energy-intensive industries, such as the iron and steel industry. However, its application faces the difficulty in technology selection under multiple objectives simultaneously, which is to determine the technology portfolio to achieve the synergy of energy conservation goals and air pollutant emission reduction goals, as well as ensure the economic benefits of the enterprises. This study investigated a case polygeneration system where the iron and steel plant are the core with four polygeneration paths and twenty polygeneration technologies. A multi-objective optimization model is developed to select the optimal technology combination of each polygeneration path under energy conservation, emission reduction, and cost control objectives, which is solved by the non-dominated sorting genetic algorithm-II (NSGA-II). The optimal results can reach significant energy conservation and emission reduction effects while obtaining economic benefits. However, synergistic and conflicting relationships among the objectives exist in both scales of iron and steel plants. The final decision scheme can achieve the mitigations equivalent to 15.9–27.1% and 16.3–42.6% of the energy consumption and air pollutant emissions of the steel enterprises with annual production of 3 Mt/a and 9 Mt/a, respectively. There are thirteen and twelve technologies that are selected as the final decision scheme in the polygeneration system in these two case enterprises. These findings demonstrate the significant roles the polygeneration system plays and provide critical insights and methodology in the technical selection of the polygeneration system.

Co-abatement of greenhouse gas and air pollutants in Shanghai, China: Spatial hotspots identification, effects assessment and policy implication

Driving co-abatement of Greenhouse Gas (GHGs) and Air Pollutants (APs) in the city level is crucial for fostering societal green and low-carbon transitions, yet comprehensive and refined researches at this level remain limited. To facilitate urban fine management of GHGs control and APs reduction, this study targeted nine categories of anthropogenic emission sources in Shanghai, a typical megacity of China, analyzing the co-benefits of three types of GHGs (CO2, CH4, N2O) and seven types of APs (SO2, NOx, CO, VOCs, NH3, PM2.5, PM10) via emissions flow, spatial distribution, hotspot regions identification, and scenario prediction. Results highlighted the source heterogeneity of different types and significant contributions of energy consumption. CO2 emissions showed a strong spatial correlation with SO2, NOx, and CO, followed by VOCs and PM. Hotspot regions for CO2-VOCs, CO2-NOx and CO2-SO2 co-abatement included power plants, petrochemical enterprises and chemical industrial parks in the southern coastal areas, iron and steel enterprises and power plants in the northern coastal areas, and airport areas in the central and eastern coastal areas, presenting great potential maximum reduction benefits. Achieving positive co-benefits in industrial sector would depend on the steady decline of CO2 emissions in power generation and steel industries. Introducing carbon capture devices and improving energy efficiency would be more beneficial to CO2 emission reduction, while increasing the share of clean energy and phasing out outdated vehicles, machinery, or production capacities are more effective in reducing APs. These mitigation measures could achieve 68.8 % and 47.6 % reduction for CO2 and APs by 2050, respectively, and the co-effect of CO2 and APs emission reduction would gradually increase with the continuous implementation of these measures.

An Efficient and Accurate Quality Inspection Model for Steel Scraps Based on Dense Small-Target Detection

Scrap steel serves as the primary alternative raw material to iron ore, exerting a significant impact on production costs for steel enterprises. With the annual growth in scrap resources, concerns regarding traditional manual inspection methods, including issues of fairness and safety, gain increasing prominence. Enhancing scrap inspection processes through digital technology is imperative. In response to these concerns, we developed CNIL-Net, a scrap-quality inspection network model based on object detection, and trained and validated it using images obtained during the scrap inspection process. Initially, we deployed a multi-camera integrated system at a steel plant for acquiring scrap images of diverse types, which were subsequently annotated and employed for constructing an enhanced scrap dataset. Then, we enhanced the YOLOv5 model to improve the detection of small-target scraps in inspection scenarios. This was achieved by adding a small-object detection layer (P2) and streamlining the model through the removal of detection layer P5, resulting in the development of a novel three-layer detection network structure termed the Improved Layer (IL) model. A Coordinate Attention mechanism was incorporated into the network to dynamically learn feature weights from various positions, thereby improving the discernment of scrap features. Substituting the traditional non-maximum suppression algorithm (NMS) with Soft-NMS enhanced detection accuracy in dense and overlapping scrap scenarios, thereby mitigating instances of missed detections. Finally, the model underwent training and validation utilizing the augmented dataset of scraps. Throughout this phase, assessments encompassed metrics like mAP, number of network layers, parameters, and inference duration. Experimental findings illustrate that the developed CNIL-Net scrap-quality inspection network model boosted the average precision across all categories from 88.8% to 96.5%. Compared to manual inspection, it demonstrates notable advantages in accuracy and detection speed, rendering it well suited for real-world deployment and addressing issues in scrap inspection like real-time processing and fairness.

Research and practice of virtualization PLC (vPLC) technology in 5G industrial network

Abstract Under the influence of information technology innovation, the empowerment of 5G and industrial internet in the steel industry has gradually expanded from production assistance to industrial control. In this paper, a deep integration network of 5G and industrial control is constructed, and a virtualization PLC(vPLC) system solution deployed on private cloud servers is proposed. Secondly, IoT protocols and security policies for cloud control are designed. Finally, the proposed scheme was successfully applied to the remote control of equipment in the steelmaking production process of a certain steel enterprise. At the same time, the real-time performance, reliability and stability of the 5G-enabled vPLC system were tested. The result indicated that the 5G industrial network can work stably with a delay of less than 10 ms and 99.99% reliability. The vPLC system ran continuously for 2000 hours and maintained stable control functions. This result can provide a research foundation and technical direction for achieving centralized control and one-stop production management in the future.

A study on long-term industrial electricity consumption and carbon emission forecasting methods based on TimesNet models

Abstract Aiming at the prediction problem of industrial electricity consumption and carbon emission, a TimesNet model is established to improve the accuracy of industrial electricity consumption and carbon emission prediction. Five significant influencing factors were screened out by grey correlation analysis; based on the energy consumption dataset of an iron and steel enterprise in 2023, the dataset was preprocessed and inputted into the TimesNet model, the prediction results were integrated and summarized, and the prediction results of the DLinear, LightTS, PatchTST, and iTransformer time series prediction models were compared and analyzed. The results show that the predicted values of the TimesNet model fit the actual values, and its mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are 0.31, 0.36, 0.6, and 1.12, respectively, which are better than those of the other models. It proves that the model can predict industrial electricity usage and release of carbon more accurately, providing a practical method for industrial electricity usage and release of carbon prediction.

The comprehensive utilization of mass and heat of hot blast furnace slag and coordinated disposal of chromium-containing sludge to prepare glass-ceramic

Currently in steel enterprises, the mass and heat of hot blast furnace slag (BFS) are not fully recovered, and the produced chrome-containing sludge (CCS) poses a significant threat to the ecological environment. In this work, CCS together with other ingredients is added into hot BFS to produce high value-added glass-ceramic (GC), after the mixed molten slag is cooled down. The obtained GC shows excellent physical and chemical properties (bulk density: 2.76 g cm−3, water absorption: 0.059 %, compressive strength: 78 MPa, Vickers hardness: 7.54 GPa, acid resistance: 98.63 %, alkali resistance: 99.84 %, thermal conductivity: 1.049 W m−1 °C−1, and thermal expansivity: 6.56 × 10−6 m m−1 °C−1). These excellent physical and chemical properties render the as-prepared GC a high value-added building material. In addition, the leaching concentrations of total Cr element and Cr(VI) in the as-prepared GC are 1.6 mg L−1 and 1.03 mg L−1, respectively, which are below the standard threshold for hazardous waste extraction toxicity. Compared with the traditional sintering method, the sensible heat of the hot BFS can be used as the thermal source in the molten slag cooling method, which can save a lot of energy. The use of hot BFS and CCS to produce high value-added GC not only fully utilizes the mass and heat of hot BFS but also fixes the Cr element in the CCS within the crystalline phase of GC, thereby eliminating the pollution of CCS to the environment.

Prediction of the pressure fluctuations in by-product gas pipeline networks in iron and steel enterprises using a spatiotemporal method

To overcome the problems of gas release, energy waste and pipeline leakage of dust erosion wear caused by the fluctuation of the by-product gas pressure in iron and steel enterprises, a 3D mathematical model of a by-product gas pipeline network was constructed in this study. Multi-condition numerical simulation analysis and spatiotemporal pressure fluctuation prediction modelling were also conducted. Firstly, the 3D mathematical model of a by-product gas pipeline network, with an error < 10%, was established by using computational fluid dynamics. Secondly, numerical simulations were conducted under multiple working conditions to study the pressure fluctuation law of the pipeline network and the erosion wear of the pipe wall. Finally, a rapid, accurate, adaptable prediction model was constructed using a spatiotemporal graph convolutional gated recurrent unit method with a combination of the numerical simulation data and pressure fluctuation law. The pressure fluctuation of the by-product gas pipeline network after a change in the blast furnace flow could be predicted in <2 s and the mean absolute percentage error of the prediction was < 2.6%.

Synergistic reutilization of spent pot lining and water quenched blast furnace slag as mold flux

A treatment method that synergistic reusing spent pot lining (SPL) and blast furnace slag (BFS) as mold flux (a functional material used in steel enterprises) was developed to achieve the harmless treatment of hazardous substances and recycling of valuable resources in spent pot lining. Mold flux made of SPL and BFS shows outstanding metallurgical performance. More importantly, toxic fluoride and cyanide in SPL were effectively harmless-treated utilizing industrial waste heat after making mold flux. The fluorine leaching ratio of amorphous mold flux residual after a leach test for 128 h is 1.08 wt%, which is 1/24 of that of raw SPL. The microstructure characterization proved the addition of BFS enhanced the fluorine immobilization effect by optimizing the network structure of amorphous mold flux residue. Compared to the traditional landfill treatment, the estimated additional economic benefits of processing one ton of SPL with this method are approximately 1924 USD.

Optimal algorithms for solving green online reheat furnace scheduling problems with deterioration effect

Optimizing reheating furnace scheduling in terms of energy saving and reheating time reduction is a vital component in improving competitive advantage of the steel enterprises. However, current reheating furnace scheduling models are still limited since (1) most of the scheduling problems involving reheating furnace suppose that the steel orders and their information are known in advance and (2) the reheating time of a slab in reheating operation is a constant in the majority of current reheating furnace scheduling models. To address these issues, three online reheat furnace scheduling models with deterioration effect are proposed in order to minimize the maximum residence time, the maximum energy consumption as well as total energy consumption of all slabs in reheating operation, respectively. Furthermore, for these online scheduling problems, deterministic online algorithms are presented and shown to be optimal, respectively. In addition, the computational experiments confirm that the three online algorithms developed in this paper are particularly efficient and effective in practice.

Analysis of production decision-making evolution of steel enterprises under carbon border adjustment mechanism

This work explored the changes in production decision-making trends of Chinese steel enterprises under the influence of the carbon border adjustment mechanism. First, using evolutionary game theory, the interactive mechanism of complex production strategies among steel enterprises considering the carbon border adjustment mechanism was studied, including the impact of government subsidy coefficients, additional profits and carbon tax prices on enterprise decision-making. Second, the influence of key parameters on the dynamic evolutionary process was analysed. On this basis, the empirical simulation method was used to verify the game model and the main conclusions. Finally, the sensitivity analysis of the selected parameters was determined using Matlab software. The results showed that additional profits from green investment, government subsidy coefficients, input-output values and carbon tax prices had a higher impact on the evolution of enterprise production strategies. The results of this study provide a decision-making basis for the selection of future production methods for steel enterprises.

Energy Saving Analysis of Coal Gas Resource Utilization in Iron and Steel Enterprises

The article discusses the preparation of a gas balance sheet, as well as the recovery, storage, transportation, and use of gas. An analysis was conducted on the occurrence and consumption quotas of various gas media, and specific energy-saving measures were elaborated for each process. This provides a theoretical reference for saving gas and reducing energy consumption in steel enterprises.

Analysis of Steel Slag Treatment Process and Comprehensive Utilization

As a by-product of steel enterprises, steel slag has a huge output and is rich in valuable minerals, but its comprehensive utilization rate is very low. The article mainly introduces the hot splashing method, hot sealing method, and drum method for the treatment of steel slag outside the furnace, and compares and analyzes the advantages and disadvantages of the production operation process and steel slag treatment process. At the same time, it also introduces the residual slag+double slag process and gasification dephosphorization slag circulation steelmaking technology for steel slag treatment inside the furnace, providing direction for steel enterprises to clean and comprehensively utilize steel slag.

Exploration and Application Practice of Energy Conservation Theory and Method in Steel Manufacturing Process System

This article briefly discusses the theoretical basis and overall goals of energy conservation in the steel manufacturing process system. It is proposed that in the process of implementing system energy conservation, it is necessary to fully recognize and utilize the characteristics and functional advantages of the steel manufacturing process, pay more attention to energy quality, firmly grasp the overall goal of system optimization, focus on the integrated optimization of gas, steam, and waste heat systems, and propose the idea of constructing a "steel chemical gas electricity heating cooling multi generation system". Based on practice, the main principles, models, and effects of implementing systematic energy conservation in steel enterprises have been proposed.