In the production process of continuous casting slab hot delivery and hot charge, the charging temperature is one of the key factors leading to cracks on the surface. The traditional single-point measurement method is susceptible to the interference of oxidized skin, with low measurement accuracy and poor stability, for this reason, this paper proposes a measurement method based on the full-field temperature of the slab surface. For the problems of temperature field deformation and slab sticking, a combination of automatic thresholding and nonlinear interpolation was proposed on the basis of spatial coordinate transformation, which realizes the dynamic tracking of the temperature field and slab segmentation; for the interference problem of iron oxide skin, a morphological expansion method was adopted to restore the temperature of the interfered area of the slab; in order to express the distribution law of the temperature of the surface of the slab, a partitioned characterization of the full-field temperature was proposed, and a specific analysis method was established for the sensitive area that generates quality problems. In order to express the temperature distribution law on the surface of the slab, the full-field temperature zoning characterization was proposed, and the specific analysis method was established for the sensitive areas that produce quality problems. The above methods have been applied in production, and the statistical data show that the temperature measurement accuracy and stability have been improved by about 20-50 ℃; the temperature of slab in the furnace is concentrated at 500-600 ℃, which is in line with the requirements of hot loading temperature; the temperature of 1/4 of the slab is higher than the center temperature, which is a sensitive area for quality defects; and the preliminary finding is that the trend of the temperature of the slab in the furnace has an effect on cracks. The above results provide an important support for optimizing the furnace entry process system and heating furnace temperature control.

The ironmaking-steelmaking interface, as a crucial link in the steel production process, has a direct impact on the stability of production rhythm and resource utilization efficiency through dynamic scheduling and optimization. Iron transport scheduling is essential for the smooth and efficient operation of the iron-steel interface, with scheduling efficiency and rationality directly affecting key indicators such as molten iron temperature drop. This paper proposes an iron transport scheduling decision-making technology tailored to the “one-ladle-to-the-end” model adopted in Tangsteel New Area. The technology includes optimization of molten iron ladle grouping and transport planning, locomotive task allocation rules, and transport task path planning. It aims to ensure tapping safety and meet steelmaking time requirements by optimizing ladle grouping and task allocation while considering locomotive load and real-time positioning constraints. Additionally, it features dynamic adjustment capabilities under abnormal conditions, allowing for real-time optimization of transport plans in response to equipment failures, schedule changes, and other unexpected situations, thereby ensuring production continuity. Practical applications in Tangsteel New Area have demonstrated that this technology significantly improves molten iron transport efficiency, path conflict avoidance rates, and overall scheduling performance, providing practical support for the intelligent and green development of the steel industry.

The heating process of coke ovens belongs to a complex thermal process characterized by “intermittent operation of individual combustion chambers and continuous operation of the entire oven”, which is subject to multiple interfering factors. The traditional manual adjustment mode for setting coke oven temperatures, relying on empirical experience, suffers from long temperature measurement cycles, crude target temperature adjustments, poor stability, and high energy consumption. This paper aims to replace manual temperature adjustment with automated intelligent heating control technology, achieving enhanced production stability, improved coke quality, and reduced energy consumption. To achieve these objectives, the study proposes three core components: a flue temperature prediction model integrating STL time series decomposition and Transformer algorithm (STL-Transformer model),a target flue temperature setting model based on particle swarm optimization algorithm, a flue temperature control model. These models have been implemented in an intelligent coke oven heating control system. Experimental results using real operational data from Nanjing Iron and Steel demonstrate: the flue temperature prediction model achieved a mean absolute error of 1.82 ℃, outperforming comparable algorithms. The target temperature setting model reduced average errors to 2.49 ℃ (machine side) and 2.55 ℃ (coke side), representing 42.23% and 40.56% improvements over manual control respectively. After system implementation at Nanjing Iron and Steel, the intelligent heating control system delivered: 3% reduction in overall coke oven energy consumption, 0.2% improvement in post-reaction coke strength. The system has significantly contributed to production stability enhancement, coke quality improvement, and energy efficiency optimization.

The online shear optimization method for medium and thick plates is an important factor affecting shear quality and production efficiency. The traditional method uses a single shape feature index or a fitting reference line to determine the shape of the steel plate and provide a cutting strategy. Usually, only a single solution was calculated or a solution was misjudged as no solution, resulting in low tolerance.The shear optimization method based on Support Vector Machine (SVM) considers the contour points on both sides of the rolled plate with head and tail removed as two categories of SVM, and then calculates hyperplane and maximum interval. The hyperplane and the head-tail shear lines form a maximum shearable parallelogram. By using analytical methods, calculate the shear optimization solutions within all parallelograms to form a solution space, and then select typical solutions in accordance with order requirements. Finally, a comparative experiment was conducted with 419 orders. The proposed method reduced invalid solutions to zero and decreased the proportion of unsolvable cases by over 5%, verifying the effectiveness and accuracy of the approach.

With the vigorous development of computer technology, the application of AI and large models in the steel industry has become a key force in promoting the intelligent transformation of the industry. This research mainly focuses on the application of large-scale models in the steel industry. Firstly, it summarizes the construction methods and typical application areas of industrial large-scale models; Secondly, elaborate on the characteristics of the steel industry and summarize the relevant technologies of the steel industry's large-scale model; Finally, a discussion will be conducted on the application scenarios of the large model, highlighting typical application scenarios of the large model in the steel production process, such as breakthroughs in perception and cognitive tasks. In the future, the steel industry is expected to deeply apply big models to the development of new products and systems, as well as provide comprehensive decision support, realizing the application of energy, raw material scheduling, and full process monitoring of steel enterprises, promoting the high-end, intelligent, and green development of the steel industry, and providing new ideas for digital development.

With the advancement of artificial intelligence (AI) technology and the increasing demand for deeper digital-physical integration, the traditional L1-L5 five-layer system in the steel industry can no longer meet the requirements of digital transformation. This paper begins by analyzing the connotation and characteristics of digital transformation in group-type steel enterprises.It elaborates on the digital system architecture of such enterprises driven by new-generation information technologies, as well as the three foundational pillars essential for digital transformation and smart factory construction: the industrial internet platform, big data center, and steel AI engine platform. Practical applications in smart governance, smart manufacturing, and smart services are illustrated through specific scenarios. Finally, using Baowu Steel Group, a typical group-type steel enterprise, as an example, the paper explains the evolution path of digital transformation—based on the integration of digital and realworld processes—in response to changes in steel manufacturing models and advancements in AI technology. This path progresses from specialized management at the individual enterprise level to crossdomain integrated operations, from AI-steel integration enabling fullprocess intelligence, and from singleentity management to ecosystem collaboration across the entire value chain. The digital transformation path and system architecture proposed in this paper provide significant reference value for promoting the digital transformation of group-type steel enterprises in China.

With the rapid advancement of general-purpose language models such as DeepSeek, research in the industrial domain is increasingly shifting from foundational model exploration to their specialized application in vertical, domain-specific scenarios. Taking converter steelmaking as a representative application context and the technological evolution of industrial-scale foundation models was systematically reviewed, including language, vision, and temporal models. Based on this analysis, it proposes a preliminary design for a multimodal large-scale model architecture tailored to the converter steelmaking process. The proposed architecture leverages heterogeneous data sources—such as scientific literature, flame video streams, flue gas composition, and molten iron chemistry—to address key technological challenges, including complex furnace condition recognition, dynamic endpoint prediction, and real-time process control. The objective is to overcome the theoretical limitations of conventional approaches in modeling high-temperature multiphase reactions, multivariate coupling behavior, and real-time decision-making under complex operating conditions. Furthermore, this study analyzes the core challenges encountered in the deployment of large-scale models in metallurgical applications—such as data heterogeneity, domain adaptation, and model interpretability—and discusses corresponding mitigation strategies. Finally, future development directions are explored to provide a theoretical foundation and methodological reference for the intelligent transformation of the steelmaking industry.

In the current era when the wave of intelligence is sweeping the world, the steel industry is confronted with an urgent need for transformation and upgrading. As a core production factor in the new era, the extraction and utilization of data value are of great significance for enhancing production efficiency, controlling product quality, and promoting digital development in the steel industry. Although the steel industry is rich in data resources, it is currently facing a deep-seated predicament in the release of data value. Aiming at the common problems such as "insufficient data and poor-quality data" faced in the application and development of large model technology in the steel industry, this paper designs a set of data engines that meet the data requirements of large models, based on three technologies: data enhancement technology of time series large models, data spatio-temporal alignment technology of process mechanisms, and cross-process data association of knowledge graphs. Establish a twin data asset shell file with materials as the object, combine the semantic knowledge of metallurgical production processes with the time series data of the production process, provide corpus information of the entire product life cycle for industry large models, and meet the demand for semantic data in the training and application of large models. Meanwhile, the traditional offline data governance mode was transformed into an online mode by using the workflow mode. Taking the data of a continuous casting production line in a certain steel plant as a pilot, significant improvements were observed. The abnormal rate of data within batches was reduced to 0.02%-0.5%, facilitating the digital transformation of steel enterprises.

As materials science research enters the fourth paradigm , artificial intelligence technologies are reshaping research  in  this  field.   currently ,  large  language  models  ( LLMs) ,  trained  on  massive datasets and utilizing extensive parameter scales , have overcome the technical limitations of traditional machine learning in text processing and human-computer interaction through capabilities such as multi - task integration ,  context-aware  generation ,  and  decision-making .    These  advancements  have  opened new  avenues for the intelligent transformation of the steel industry .  The technical features , research di- rections and application of LLMs were systematically summarized.  considering the data characteristics of the steel industry , a detailed overview of frameworks for large language models tailored to steel ap-plications was provided , along with proposed evaluation criteria specific to the industry .  The potential applications of LLMs  in  steel were  explored  in  depth , including  data  extraction  and  protection , per- formance modeling and prediction , inverse design of materials , and intelligent steelmaking .  Addition- ally , a comprehensive analysis of the  current development of LLMs in the steel industry was presented , key challenges were identified , and corresponding strate gies were proposed.

Integrated scheduling in steel production faces core challenges such as process coupling, resource constraints, dynamic disturbances, and conflicting objectives across stages including iron and steel ladle management, steelmaking, reheating furnaces, slab yards, hot rolling matching, and crane coordination. Existing intelligent agents, reliant on predefined rules and decision algorithms, exhibit limitations in comprehending complex rules and adapting to dynamic changes. This research proposes and validates a hierarchical hybrid multi-agent architecture based on large language models (LLM). Its core advantage lies in the effective integration of LLM advanced reasoning capabilities with the execution efficiency of traditional intelligent agents, compensating for the latter's deficiencies in adaptability, planning, and learning. This significantly enhances the system's capacity to manage complex rules and dynamic environments. Through the application of LLM, the system is endowed with the ability to autonomously understand business requirements, automatically utilize the model context protocol (MCP) to invoke a toolset for generating scheduling decisions, and provide natural language explanations. This approach not only optimizes human-computer interaction but also improves the system's scenario adaptability and scalability. This research offers crucial technical insights for developing next-generation intelligent steel scheduling systems capable of understanding requirements and adapting to changes, potentially accelerating the implementation of agents in scheduling systems.

With the in-depth advancement of the digital transformation in the steel manufacturing industry, intelligent quality management has become one of the key links to enhance the core competitiveness of enterprises. Aiming at the product quality defect analysis process of steel enterprises, an architecture for a quality defect analysis system based on a retrieval-augmented large language model was proposed. The system adopts a modular design to build a case knowledge base, implements a two-stage knowledge retrieval enhancement architecture that combines semantic vector recall and confidence re-ranking, and designs a hierarchical information extraction and aggregation mechanism for local causal extraction and global causal aggregation. It realizes similar case matching and cross-case knowledge collaborative reasoning, while embedding an interpretability support mechanism and a dynamic knowledge update mechanism to ensure the reliability of the analysis results and the long-term effectiveness of the system. The system has been deployed and run in a steel enterprise, effectively improving the efficiency of the product quality defect handling process and providing a successful case for the application of large models in the field of industrial product quality management.

As a vital pillar of the national economy, the iron and steel industry faces challenges such as high costs, low efficiency, and complex processes, which urgently require intelligent transformation to break through development bottlenecks. With its cross-modal understanding and multi-scenario generalization capabilities, large model technology provides a new path for deep data value mining and optimization of production processes in the steel industry. This research proposes an overall construction framework of the "Five-in-One" large model platform for the steel industry and elaborates on ten key technologies required to build a large model platform adapted to the industry needs. Through the deep coordination of platform, computing power, data, model, and scenario, and the integration of general models with steel industry knowledge, this framework forms an integrated "AI + Steel" solution, which significantly reduces the research and development threshold and cost of models in the steel industry and enables AI technology to better integrate into the industry. Finally, this research combines the business characteristics and practical exploration experience of the steel industry and forecasts the application scenarios, providing insights for the industry's intelligent transformation.

In recent years, large language models (LLMs) have achieved breakthrough progress in natural language processing. The powerful semantic understanding and reasoning capabilities accelerate the intelligent and digital transformation of traditional industries. However, existing LLMs remains a lack of systematic and professional evaluation benchmark in traditional manufacturing industry areas such as iron and steel metallurgy. We proposed StiBench, a Chinese evaluation benchmark for the steel and metallurgy industry area, to assess the performance of existing open-source LLMs. StiBench integrates a large amount of technical documents, process manuals, professional examinations, etc. through methods such as open source crawling and optical character recognition, de-duplicates through semantic similarity detection, and finally forms the evaluation benchmark consisting of over 2 000 questions in multiple formats (multichoice, true or false), covering knowledge topics such as ironmaking, steelmaking, rolling, metallurgical physical chemistry, heat treatment and surface engineering. We conducted few-shot and zero-shot experiments using open-source models such as Baichuan, GLM, and Tongyi Qianwen, employing both classification and generation-based evaluation approaches. Results show that while LLMs have made progress, the semantic understanding of specialized content in the steel and metallurgy domain still needs improvement. Research results provide an evaluation benchmark for LLM performance in the steel and metallurgy area, which can be a valuable reference for future model development and industrial application.

As a key index to reflect the stability of furnace condition and operation smoothness, the accurate prediction of blast furnace gas permeability index is of great significance to recognize and prevent abnormal furnace condition in time. A method for predicting the gas permeability index of blast furnace based on generative time series model was proposed. Firstly, the box plot method was used to identify and correct the anomalies in the data, and secondly, an algorithm based on the fusion of Boruta and SHAP (shapley additive explanations) of random forest was proposed to select the features of the permeability index. Finally, a time series generation (TSG-GPT) model was designed to predict the gas permeability index of the blast furnace. The model was trained, validated and tested using actual production data, and the results show that the proposed model can accurately predict the gas permeability index.

To address the problems of large subjective deviations and permeability attenuation in traditional argon blowing control, the relationship between the dynamic characteristics of the exposed area of the steel liquid surface and the argon stirring intensity was analyzed. An indirect quantification method of argon intensity based on machine vision was studied. This method captures the dynamic characteristics of the steel liquid surface in real time, calculates the liquid surface exposure rate (SER) using artificial intelligence large model technology, and establishes an argon blowing automatic control strategy based on the relationship between the real-time SER and the ideal SER, thus realizing the precise regulation of the argon flow rate. The research results show that this method overcomes the limitations of traditional flow preset and manual adjustment, effectively improves the control accuracy of the stirring intensity, and was applied and verified in the actual VD refining production system, proving its industrial feasibility and engineering promotion value, and providing an engineering solution for the intelligent upgrade of the refining process.

In the context of new industrialization, digital and intelligent transformation has become a core pathway for the steel industry to break through efficiency bottlenecks and achieve high-quality development. Meanwhile, large language models, as a rapidly advancing artificial intelligence technology in recent years, have demonstrated remarkable results across various sectors. Therefore, how to leverage general-purpose large models to accelerate the intelligent transformation of the steel industry has emerged as a significant research and application direction. This paper focuses on exploring three mainstream methods for constructing large models in the steel domain: Domain-continual pretraining, which enhances the model’s understanding of industry-specific knowledge through continuous pretraining on steel-related corpora; supervised fine-tuning on scenario-specific tasks, which improves model performance on domain-specific tasks using labeled data from real-world industrial applications; and knowledge distillation, which leverages a powerful teacher model to extract knowledge from steel industry corpora and transfer it to a smaller student model during training, thereby enhancing the model’s retention of domain-specific knowledge. Finally, the advantages and disadvantages of these three methods were evaluated using a constructed steel knowledge assessment dataset. Through systematic analysis and comprehensive comparison, this research provides practical guidance and methodological references for building specialized large language models in the steel industry and other vertical domains, thereby facilitating industrial intelligent upgrading.

The steel industry serves as the backbone of most industrialized nations and is closely intertwined with national economies. With the rapid advancement of new-generation artificial intelligence and industrial large language model technologies, data-driven soft sensing modeling has emerged as a critical driving force in steel manufacturing. Steel rolling, a pivotal process in this industry, plays an essential role in understanding material science and developing novel alloys. Investigating the relationships between steel chemical compositions, rolling process parameters, and final product mechanical properties holds paramount importance. Yield strength (YS) and tensile strength (TS) represent key metrics for evaluating the mechanical property of rolled steel products. To achieve accurate soft sensing of these properties, a framework based on offline reinforcement learning for continuous action space modeling was proposed for pretraining mechanical property prediction models. First, the steel rolling process—from raw materials to finished products—is formulated as a markov decision process (MDP). Subsequently, an actor-critic architecture integrated with deep neural networks is employed to learn deterministic policies, optimizing continuous action selections to enhance training stability and improve the model’s generalizability across diverse steel grades. Validation experiments were conducted using rolling data from two steel grades at a production facility. The results demonstrate that the proposed soft sensing model outperforms conventional algorithms, including gated recurrent units (GRU), and deep belief network (DBN), in terms of prediction accuracy.

In the steelmaking production of metallurgical enterprises, plant operational efficiency directly affects the smoothness of production logistics and the coordination between processes. This study aims to improve the efficiency of overhead crane scheduling in steelmaking production of metallurgical enterprises, and innovatively proposes a scheduling method that integrates Deep Reinforcement Learning (DRL) and simulation. By constructing a simulation model to replicate the crane operating environment and designing a DRL algorithm to process real-time spatial information, this study can formulate optimized scheduling plans for uncertain transportation tasks. Training with historical data enables the model to make optimal decisions under varying conditions. Through simulation modeling technology, dynamic optimization and real-time monitoring of scheduling strategies are realized. The reward function, as a key metric, is used to monitor on a temporal dimension, significantly enhancing the intelligence and operational efficiency of the scheduling process. Experimental results indicate that the A2C method proposed in this study shows significant advantages in scheduling efficiency and task completion time. The algorithm′s final cumulative reward gap value is significantly better than other reinforcement learning methods (gap=7.89%) and traditional methods (gap more than 100%).

This paper presents a method for predicting electricity consumption in the production process of steel enterprises using TabNet-XGBoost, aiming to improve prediction accuracy and optimization efficiency. Preprocessing techniques such as data augmentation and outlier handling are employed to enhance model performance. By leveraging the interpretability and feature selection capabilities of the TabNet model, this study identifies key influencing factors for feature selection. Furthermore, Bayesian optimization and grid search methods were applied to fine-tune the hyperparameters of eXtreme Gradient Boosting (XGBoost), thereby further enhancing the model′s effectiveness. Comparative experiments are also conducted using machine learning models including Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Light Gradient Boosting Machine (LightGBM). The experimental results indicate that CO2 emissions, lagging reactive power, Number of Seconds from Midnight (NSM), weekly status, and specific days of the week are identified as critical predictors. These factors reflect indirect indicators of electricity usage, the efficiency of the power system, potential waste during non-working hours, and cyclical patterns of production activities. The predictive performance of the model was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that the TabNet-XGBoost model achieves an MAE of 0.326, RMSE of 1.097, and MAPE of 1.032 on the test set, representing a noticeable improvement in prediction accuracy compared to traditional methods. In summary, the proposed model offers significant advantages in addressing the challenge of electricity consumption prediction in the steel industry′s production process, providing new research insights and technical solutions for related fields.

The construction of standards in the steel industry, as a crucial support for industrial transformation, is of great significance in promoting high-quality industrial development and enhancing competitiveness. To improve the query efficiency and usage accuracy of steel industry standard knowledge and promote the in - depth integration of standard implementation and technological innovation, a research on the development of an intelligent question-answering platform based on retrieval augmented generation (RAG) technology was conducted. 60 representative steel industry standards are selected as the basic data source. The DeepSeek-R1 model was used to parse the standard texts and extract knowledge, and a knowledge graph of steel industry standards was constructed to provide structured knowledge support for intelligent question - answering. Under the LangChain development environment, two different architectures of question - answering system solutions, GraphRAG and LightRAG, were designed and implemented. The system performance was evaluated from multiple dimensions such as query accuracy, comprehensiveness of answer scope, and depth of technical analysis.The research results show that the two question-answering systems have their own advantages and disadvantages in different evaluation indicators. The GraphRAG system performs excellently in handling complex knowledge queries and associative reasoning, and can provide more in-depth and comprehensive answers. In contrast, the LightRAG system has certain advantages in query response speed and simple question processing.The intelligent question-answering platform developed effectively improves the query efficiency and usage accuracy of steel industry standard knowledge, and provides powerful tool support for the implementation of industry standards and technological innovation. In the future, the construction of the knowledge graph and the architecture of the question-answering system can be further optimized, and by integrating more domain data and advanced technologies, the intelligent development of the steel industry can be promoted.

Post-rolling cooling is a crucial method for regulating the microstructure and properties of ho-rolled steel strips, where the heat transfer coefficient serves as a key parameter in temperature models, directly determining the accuracy of coiling temperature control. However, traditional mechanism models have limited accuracy in calculating the water-cooling heat transfer coefficient, making it difficult to meet the control requirements for low-temperature coiling of X80 pipeline steel, which results in significant temperature prediction deviations. To address this issue, a temperature prediction model integrating data-driven and mechanism approaches was proposed. Based on heat transfer theory, the model calculates the internal heat conduction of the steel strip by solving a one-dimensional heat conduction differential equation. At the same time, a data-driven model was constructed to predict the water-cooling heat transfer coefficient, improving the accuracy of surface heat transfer calculations. In developing the data-driven model, the effectiveness of artificial neural networks, gradient boosting decision trees, and support vector regression in predicting the heat transfer coefficient was compared, and the optimal machine learning algorithm was selected to construct the integrated model. Experimental results show that, compared to the mechanism model, the integrated model based on artificial neural network prediction of the heat transfer coefficient reduces the coiling temperature prediction error by 5 ℃, with the mean squared error and mean absolute percentage error decreasing by 76.8% and 49.5%, respectively, significantly improving the accuracy and stability of coiling temperature prediction. This integrated model effectively compensates for the shortcomings of the mechanism model in heat transfer coefficient prediction, providing a feasible solution for the precise control of coiling temperature in hot-rolled steel strips.

In response to the systemic challenges faced in the “multi-variety，small-batch” production model for special seamless steel pipes—including complex quality inspection dimensions，the absence of full-process single-unit traceability，heavy reliance on manual experience for production scheduling，and inadequate closed-loop quality control—this paper proposes and implements a “four-in-one” digital and intelligent factory solution encompassing “inspection-tracking-scheduling-control.” This solution establishes a quality inspection system based on deep learning and multi-modal visual fusion，achieving high-precision online measurement of pipe defects and dimensions. It pioneers a single-pipe tracking method that integrates “video AI+event logic”, overcoming the industry-wide challenge of accurately mapping data to individual pipes throughout the entire process. A dynamic scheduling optimization model，based on a hybrid genetic and simulated annealing algorithm，was developed，significantly enhancing scheduling efficiency and resource utilization in complex order environments. Furthermore，a collaborative quality management system based on the plan-do-check-act (PDCA) cycle was established，enabling systematic and proactive quality management. Application results demonstrate that the system achieves a defect detection rate of 99.95%，a material tracking accuracy of 9998%，an increase in scheduling efficiency of over 60%，and a 50% reduction in product non-conformance rates. This work provides a valuable and exemplary model for the digital transformation of China’s special steel industry.

At present , a neWWaVe of technological reVolution is emerging globally , represented by in- dustrial technologies such as artificial intelligence , big data and industry 4 . 0 .  The traditional chemical plant control model can no longer meet the  needs  of digital  manufacturing.   Typical  problems  such  as complex production processes and difficulty in collaboratiVe operations in the steel industry haVe gradu- ally emerged.  Enterprises also haVe uneVen information leVels  and serious information island phenome- na.  In this regard , the factory and production line based on emerging technologies such as digital tWin technology , internet of things , machine learning and Video intelligent recognition , accesses Video ima- ges , sensor monitoring data , system  control  data , external  draWing  information  and  management  sys- tems , and the status of production line  equipment and materials  in real time Were  established , Visual linkage of  data  information  Within  the  production  line  Was  displayed ,  and  information  islands  Were eliminated.  At the same time , artificial intelligence algorithms Were used to merge and splice the com- plex and scattered Video images , and splice the production line monitoring images.  For large-scale key areas , real-time monitoring Was achieVed in the form of“ one picture ”.

Metal material testing and certification is a critical process for ensuring material quality, involving complex standard systems and diverse testing requirements. The traditional manual generation of work orders suffers from low efficiency and high error rates due to the vast number of standards and intricate inter-referencing relationships. An intelligent agent framework was proposed based on multi-step reasoning, integrating chain-of-thought (CoT) and retrieval-augmented generation (RAG) techniques to automate the entire process from product information input to structured test order output. Experimental results demonstrate that this approach significantly improves the accuracy and efficiency of test order generation, providing effective support for the intelligent automation of material testing and certification.

Three persistent challenges in steelmaking-continuous casting process optimiZation were  ad- dressed : inefficient process modeling , imprecise parameter control , and inadequate  multi-process co- ordination.  Three core technological innovations were developed : intelligent ladle furnace  (LF) metal- lurgy , precision-controlled continuous casting solidification , and dynamic multi-process collaboration.Firstly , an intelligent LFcontrol system through the  integration of metallurgical principles with inter- pretable machine  learning  was  established.    This  system  enables  accurate  regulation  of molten  steel  temperature , alloy composition  ( si/Mn content) , and  argon  stirring parameters , substantially reduc - ing both material consumption and energy usage while eliminating traditional reliance on empirical ad- justments and repeated sampling.  secondly , for continuous casting optimiZation , a solidification cool- ing strategy based on steel phase-transformation characteristics was proposed.  Through noZZle arrange- ment optimiZation and secondary-phase  precipitation control , this  approach effectively mitigates  crack formation and segregation defects in microalloyed steels .  A predictive model combining principal com- ponent analysis with deep neural networks further enhances process control by guiding real-time param- eter adjustments .   To  resolve  production  coordination  challenges  in  complex  steelmaking  workshops , dynamic collaborative operation technologies rooted in metallurgical process engineering theory was de- veloped.  The implementation of quantitative  coordination metrics  ensures  efficient  material flow  man- agement , significantly improving operational synchroniZation across multiple processes .

The  current  status  of  steel  scrap  utilization ,  classification ,  and  classification  standards  in china were analyzed , the current situation of steel scrap utilization and classification sorting were  ex- plored , and  the  existing  problems  were  put  forward.   On  this  basis ,  development  and application of steel scrap intelligent identification system and rapid detection technology were analyzed.  It is consid- ered that there is still a big gap between the development level of steel scrap classification and sorting technology and the actual demand of steel enterprises .  In the future , the coupling of the two technolo- gies must be  achieved  in  order  to  realize the  intelligent  smelting  of  electric  arc  furnace.  The  article points out that the classification and sorting of steel scrap in china is still relatively primary and exten- sive , it is not yet possible to achieve rapid and effective identification of the  composition of steel scrap.under the background of“ dual-carbon”, as china , s steel industry shifts from the stage of large-scale and high-speed development to the  new  stage  of green  low-carbon  and high-quality  development , the importance of steel scrap  quality  is  becoming  more  and  more  important .   The  utilization  level  of  steel scrap  should be improved as soon as possible , not only to improve the scrap ratio ofsteelmaking , more importantly , it is necessary to improve the technical level of steel scrap classification and sorting , so as to lay a foundation for the realization of intelligent smelting .

Against the backdrop of intensifying global market competition, fluctuations in energy and raw material prices, and the implementation of carbon tariff policies, Xingcheng Special Steel has addressed issues such as insufficient enterprise customization capabilities, a lack of systematic product quality evaluation, high energy consumption due to “black-box” operations in blast furnace ironmaking, poor production coordination in the steel rolling process, and the absence of systematic energy management. Leveraging digital technologies such as the industrial internet, artificial intelligence, big data, and simulation, and drawing on the concept of lighthouse factory promoted by the world Economic Forum, Xingcheng Special Steel has deployed over 40 use cases of the fourth industrial revolution. This has led to remarkable improvements, including a 35.3% increase in customized orders, a 47.3% reduction in nonconforming product rates, and a 10.5% decrease in energy consumption per ton of steel. Through typical use case practices such as manufacturing process customization design supported by big data analysis, transparency in blast furnace black-box operations through multimodal simulation, steel quality enhancement via intelligent closed-loop control, efficient steel rolling process enabled by AI, and optimization of energy, water, and carbon resources driven by advanced analytics, the company has achieved significant success in improving production efficiency, reducing costs, innovating product development, enhancing product quality, and boosting customer satisfaction. These efforts provide a valuable reference for the digital transformation of the steel industry.

The prediction of the endpoint carbon content and molten steel temperature in converter steelmaking is of great significance for the precise control of molten steel composition and temperature and the improvement of product quality. Due to the high dimension, high noise and strong nonlinearity of the converter steelmaking process data, it is difficult to obtain high-quality data. Direct modeling not only has a low hit rate but also is prone to overfitting. To address this problem, this paper proposes a random forest prediction model based on adaptive SMOTE data enhancement technology was proposed. Firstly, the feature selection of the endpoint carbon content and molten steel temperature iswas performed by recursive elimination method. Secondly, the adaptive SMOTE algorithm iswas used to enhance the original data. Finally, random forest iswas used to predict the endpoint carbon content and molten steel temperature respectively. The actual industrial data shows that the prediction hit rate of the end-point carbon content within the target error value ±0.02% is 88.9%, and the prediction hit rate of the end-point molten steel temperature within the target error value ±20°C is 92.3%, which is significantly improved, and provides a reference for end point prediction and control of converter steelmaking.

With the rapid development of industry 4 . 0 and intelligent manufacturing , the  steelmaking industry is facing great opportunities  and challenges of transformation and upgrading .   The  application of steelmaking  intelligent  technology  not  only  improves  production  efficiency  and  optimiZes  product quality , but  also  significantly  reduces  energy  consumption  and  environmental  pollution ,  which  pro- motes the development of the steel industry in the direction of green , intelligent and sustainable.  The current status and future development trend ofsteelmaking intelligence was reviewed , and the integrat- ed application of artificial intelligence , big data , internet of things and other advanced technologies in the electric arc furnace , converter and refining process was focused on.  The key technologies of steel- making intelligence , including intelligent control system , data acquisition and monitoring technology , machine learning  algorithms , refining  process  optimiZation ,  etc.  ,  were  detailed.   It  also  summariZes the application cases of various types of intelligent technologies in improving the control of molten steel composition , smelting process precision , and energy utiliZation efficiency .  In addition , the challenges and bottlenecks ofsteelmaking intelligence in practical applications were discussed , such as data qual-ity , system integration , real-time and adaptability issues .  Finally , the future development direction of intelligent steelmaking technology was looked into , especially the potential in the fields of digital twin , automation control , intelligent prediction and optimiZation , aiming to  provide  reference  and guidance for the technological innovation and intelligent transformation of the steel industry .

 

The  prediction  of  the  endpoint  carbon  content  and  molten  steel  temperature  in  conVerter steelmaking is of great significance for the precise control of molten steel composition and temperature and the improVement of product quality .  Due to the high dimension , high noise and strong nonlinearity of the conVerter steelmaking  process  data ,  it  is  difficult  to  obtain  high-quality  data.  Direct  modeling not only has a loWhit rate  but  also  is  prone  to  oVerfitting.   To  address this problem ,  a random forest prediction model based on adaptiVe SMOTEdata  enhancement technology  Was  proposed.   Firstly ,  the feature  selection of the  endpoint carbon content and molten steel temperature Was performed by recur- siVe elimination method.   Secondly ,  the  adaptiVe  SMOTEalgorithm  Was  used  to  enhance  the  original data.   Finally , random forest Was used to predict the endpoint carbon content and molten steel tempera- ture respectiVely .  The actual industrial  data  shoW that  the  prediction  hit  rate  of the  end-point  carbon content Within the target error Value   ±0 . 02%  is 88 . 9% , and the prediction hit rate  of the end-point molten steel temperature Within  the  target  error  Value   ± 20  ℃   is  92 . 3%  ,  Which  is significantly im- proVed , and proVides a reference for end point prediction and control of conVerter steelmaking.

Under the background of green and intelligent construction in the steel industry , traditional single-process quality control systems become insufficient to meet the demands of smart steel manufac - turing.  They need to eVolVe into full-process integrated process control systems.  These systems need to transition from  mere  model  calculations  to  collaboratiVe  Whole-process  process  model  manufacturing , production data  mining ,  and  intelligent  system  decision-making ,  self-adaptation ,  and  self-learning.  with this objectiVe in mind , the system architecture , research and deVelopment content , and anticipa- ted Vision for the deVelopment and application of intelligent process control systems and process models across  four  dimensions : intelligent  egaipment , horizontal  process  floW, Vertical  information  transfer , and time Were explored.

In the iron and steel industry, traditional PID control of blast furnace hot stoves faces challenges such as nonlinear hysteresis and poor multi-condition adaptability, which is difficult to meet the dual requirements of energy efficiency optimization and environmental compliance. A fusion control method based on AI agents was proposed, and a three-in-one intelligent control system of “prediction-knowledge-optimization” was constructed. Through the double-layer coupling of the LSTM algorithm and the knowledge embedding technology, the mean absolute error (MAE) of the dome temperature prediction is achieved at 5.3 ℃, which is 58% higher than that of the traditional ARIMA model. By establishing a four-dimensional knowledge representation system and combining it with the Rete rule engine, a response of 9.8 s for abnormal working conditions is achieved. By developing the DeepSeek lightweight decision-making engine, multi-objective optimization was achieved with 3.2 iterations of convergence, reducing the unit consumption of hot air per ton of iron by 14.8% and achieving a quarterly energy-saving benefit of 9 million yuan. Industrial validation demonstrates that this system significantly enhances thermal efficiency and environmental performance in 3200 m3blast furnace applications, providing theoretical and engineering references for the intelligent upgrading of industrial furnaces.

Against the backdrop of the intelligent transformation of the global steel industry, Digital Technology (DT) has become the core driving force for the intelligent upgrading of Hot Strip Mill (HSM) production lines. This paper systematically analyzes the application status and development trends of DT in wide strip Hot Strip Mill (HSM) production lines. By integrating key technologies such as the Industrial Internet of Things (IIoT), big data analysis, digital twin, and artificial intelligence, it explores the application paths and innovative achievements in process control optimization, intelligent equipment operation and maintenance, quality closedloop control, energy efficiency improvement, and integrated coordination of production and marketing. Taking the 1 780 mm HSM production line as the research object, this paper summarizes the role of digital technology in promoting the production efficiency, product quality, and cost control of HSM: the production efficiency of hotrolling technical personnel has increased by more than 30%, and the comprehensive yield has reached 97.02%. Finally, the challenges and future development directions of DT are proposed. The research shows that the deep integration of digital technology and HSM production lines will promote the hot strip rolling process towards intelligence and greenization.

The quality  disputes  during  inspection  of  scrap  steel  have  always  been  an important issue plaguing major steel enterprises .   In order to  solve the problems  such as the  great influence  of subjec - tive factors , the  difficulty  in  tracing  the  grading  process ,  and  quality  disputes  existing  in the  scrap steel quality  inspection  process ,  the  scrap  steel  intelligent  grading  system  based on  artificial  intelli- gence technology has emerged as the times require and has received great attention in the steel indus- try .  As a new thing , many enterprises have  doubts , incomplete  and unscientific  understandings , and even misunderstandings about the scrap steel intelligent grading system.  Based on the technical princi- ples  and  the  functions  of  the  technical  architecture ,  the  key  technological  breakthroughs  in  aspects such as the automatic collection of pictures , standard unification , the intelligent grading ofspecial ma- terial types like  briquettes , and  intelligent  deduction  of  impurities  were  expounded  on.   At  the  same time , it points  out  the  engineering  challenges  faced  in  aspects  such  as  the  identification  of  chemical compositions , the recognition of small material types , and the internal quality inspection of briquettes .Finally , it puts forward the development process of the application of the scrap steel intelligent grading system in enterprises and its future trends .

As a key equipment in the hot-rolled Wide and thick plate production line , it is a complex system With multidimensional factors coupled With each other.  Especially during operation , due to the strong coupling and nonlinear characteristics  of Various  influencing  factors , the  rolling  mill  operation process is in a“ black box ”state , Which makes it difficult to accurately grasp the rolling mill operation status in real time and formulate preVentiVe measures.  This has become  a bottleneck problem that re- stricts the high-precision and stable rolling of the hot-rolled Wide  and thick plate  production line.   To solVe this industry problem , data-driVen monitoring and analysis technology for the status of hot-rolled Wide  and thick plate rolling mills Was introduced.  Based on deep perception of Various data in the roll- ing process , big data algorithms are applied for data correlation analysis , and a rolling process dynam- ic model and rolling mill status online monitoring system platform With multi-source data-driVen fusion mechanism are constructed.  After being applied in releVant production lines , the accuracy of the sickle bending offset model has exceeded 95% , and the Vibration speed of the precision rolling mill has been reduced by 64 . 8%  ~72 . 8% .   It can effectiVely  identify 90%  of the rolling mill state data , effectiVely improVing the real-time accurate perception of the rolling mill state and the ability to analyze and pre- dict abnormal states.

With the advancement of technology and economic development, the steel industry is facing new challenges and opportunities. Intelligent factories are a new type of industrial life form that deeply integrates digital technology and manufacturing industry. They have become the core force driving the upgrading of manufacturing industry during the critical period of industrial intelligence transformation and upgrading. This article takes Hebei Iron and Steel Group Shijiazhuang Iron and Steel Co., Ltd. (hereinafter referred to as “Shisteel”) as an example, focusing on the construction and operation of a life form intelligent factory, and systematically studying the transformation and upgrading path of special steel enterprises driven by the deep integration of digital technology and process innovation. Through the integration of industrial Internet, big data, artificial intelligence and other cutting-edge technologies, the cluster deployment of intelligent equipment and the implementation of intelligent manufacturing in the whole process, the intelligent high-quality development of production lines will be pushed to a new height, that giving life a strong physique and intelligent thinking. The fact shows that the construction of intelligent factories in the form of living organisms has achieved significant results in production efficiency, quality and cost control, and green development for Shigang, providing a new model for the high-quality development of the steel industry that can be referenced.

Blast furnace burden distribution is one of the crucial aspects of blast furnace ironmaking.  Due to the airtight nature of the top loading equipment , it is not possible to intuitiVely and accurately obserVe the actual distribution of furnace burden in the furnace throat , such as the distribution of bur- den layers and ore-to-coke ratio.  During production , blast furnace operators typically rely on the distri- bution of cO2  , coal gas temperature , or gas floWrate in the coal gas at the furnace throat for upper ad- justments.  With the adVancement of detection technology , operators can utilize infrared cameras , ther- mal imaging , laser technology , and radar technology to perform more precise upper adjustments.  HoW- eVer , these technologies are only capable of capturing information pertaining to the  surface of the fur- nace material , and they fail to proVide a detailed understanding of the distribution information regard- ing the material layer and ore-to-coke ratio.  Based on the mathematical model of blast furnace burden distribution , the research comprehensiVely applies cutting-edge technologies such as numerical simula- tion , supercomputing , and artificial intelligence.  Through big data cloud platforms and intelligent neu- ral netWorks , an intelligent monitoring system for the distribution of the entire blast furnace burden lay- er Was constructed.  This system can achieVe online tracking and simulation of the material distribution process , assisting blast furnace operators in understanding the  distribution of furnace materials.   After the application of the system , Various economic and technical indicators of the blast furnace haVe been significantly improVed.   compared  With  the  past ,  the  monthly  output  has  increased by  an  aVerage  of 4. 7% , the  fuel  ratio  has  decreased  by 2. 37  kg/tFe ,  and  the  Vanadium  recoVery  rate  is  all  aboVe 8o% .

In order to ensure the manufacturing quality and yield of high-end products, a data-driven quality insight analysis system based on HBIS digital industrial internet platform was constructed. The system integrates machine learning algorithms and image processing technology through multi-source data collection and intelligent processing, achieving quality data tracking of the entire iron production process. The system has established a linkage mechanism of online monitoring-intelligent diagnosis-closed-loop control, which optimizes process parameters, traces defects, and predicts abnormal working conditions for key processes such as steelmaking, hot rolling, and cold rolling, forming a digital control loop covering quality index prediction and process diagnosis. Since the application of the system, through the analysis of quality data throughout the entire process, the product qualification rate has been improved, the narrow window control level of indicators has been enhanced, and the quality assurance needs of high-end manufacturing have been effectively supported.

The connection and scheduling of the casting-rolling interface is an important factor affecting the production efficiency and economic benefits of iron and steel enterprises.  For the continuous cast- ing and hot rolling production site of a steel enterprise , a multi-agent simulation model of the casting- rolling interface Was established , and the  complete  production  and  transportation  process  on  the  pro- duction site Were modeled by designing the roles and tasks of Various agents and the cooperatiVe inter- action mechanism betWeen  agents.   A Variety  of  different  production  rhythms  Was  simulated  and  ana- lyzed by multi-agent model , and the precise matching betWeen different production rhythms of continu- ous  casting and different production rhythms of hot rolling on the production line Was studied.  The sim- ulation results shoWthat the model can accurately reflect the actual production process , Which Verifies the effectiVeness of the simulation model.  According to the simulation model , as the interVal time be- tWeen the different streams of the casting machine increases , the conVeying time of the hot slab in the process of traVersing trolley and the total conVeying time of the hot slab shoWed a trend of first decrea- sing and then rising , and reached their loWest When the interVal time betWeen the  different streams of the casting  machine  Was  2  min.    when  only  producing  hot  billets ,  With  the  increase  in  the  casting speed , the aVerage total conVeying time increases , the total production time  decreases , and the  aVer- age reheating time increases first and then decreases  after the  casting  speed  aboVe  1 . 9  m/min.   As  a result of the optimization , the aVerage reheating time Was reduced to 95 . 7%  and the aVerage total con- Veying time Was reduced to 68 . 2%  of the pre-optimization leVel.

LFrefining is a metallurgical process inVolVing multi-process , multi-steel type and complex enVironment , a single-process  control  model  cannot  meet  the  demand  for  full-process  automation  and intelligence in smelting.   Based on data and experience , by adopting  self-learning algorithms , mecha- nistic models , expert system , image  recognition , mathematical model to  create  models  for alloy , slag formation , temperature control , argon bloWing , and Wire feeding , and using a Variety of detection de- Vices  and multi-functional robots to  achieVe the  intelligent temperature  sampling , and  closely integra- ting multi-process model  and  L1  automated  control  system ,  an  intelligent  control system for  the full- process  of LFrefining Was realized.  The system adopts a modularized and multi-threaded control meth- od , completing the  smelting steps by a single-step incremental Way through time sequence analysis.  It is  applied in 150t double-station LF, and the aVerage operation time is shortened about 4 min , the hit rate  of molten steel prediction temperature error Within  ± 8  ℃  is more than 93%  , and the success rate of temperature measurement and sampling is 97% .