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.

The smelting intensity (SI) affects the physical and chemical reactions within the blast furnace,causing the relationship between the gas utilization rate (GUR) and the blast supply parameters undergoes variations with changes in SI. Disregarding the SI means neglecting the dynamic correlation between GUR and blast supply parameters,resulting in adverse effects on predicting GUR using blast supply parameters. This paper introduces a GUR prediction model that takes into account the classification of SI. Firstly,the impact of SI on the state parameters of blast furnaces is evaluated from the perspective of molten iron smelting mechanisms. Then,a weighted kernel fuzzy c-means clustering method (WKFCM) based on state parameters is proposed to classify the SI. Subsequently, supervised principal component analysis (SPCA) is employed to reduce the dimensionality of the input data and a support vector regression (SVR) method is used to predict the development trend of GUR. Finally,the model is applied to predict real GUR data under different SI. Analysis of actual production data indicates that the prediction method considering SI classification is more suitable for forecasting GUR time series in the complex production environment of blast furnaces.

In view of the shortcomings of the existing carbon content detection methods of continuous casting billets,the predecessors tried to establish an exponential function model for predicting the carbon content of high-carbon steel in continuous casting billets based on the corresponding relationship between the grayscale of the macrostructure image and the carbon content,but the degree of carbon segregation is more difficult to characterize for low-carbon steel. The partial segregation degree of low carbon steel is obvious,and the maximum segregation index is greater than 3. 0,so it is necessary to carry out efficient characterization of carbon content. A typical low-carbon steel continuous casting billet sample was selected and an exponential function model for carbon content prediction based on  macrostructure image grayscale was established. The R-square coefficient of the function fitting result was 0. 62,and the average relative deviation (ARD) was 29. 7% . Then,a carbon content prediction neural network model based on the color parameters of macrostructure images was established,and the ARD of the training results was 19. 5% . Finally,a comprehensive model for carbon content prediction was established based on the characteristics of the prediction results of the neural network model and the exponential function model. The ARD between the prediction results of the test set of comprehensive model and the results of electron probe detection was 14. 27% . The relative deviation between the average value of the prediction results and the average value of the electron probe detection results is 3. 43% . Compared with the commonly used carbon content detection methods,the error has basically reached the same order of magnitude,and some prediction results have lower errors than the commonly used detection methods. Since the macrostructure image and its color information acquisition process are simple to operate,the cost is low,the pixel scale can be at the micron level,and the acquisition range can be targeted at the entire continuous casting billet section or large area. This model can provide guidance and lay the foundation for the automatic fine detection and evaluation of carbon segregation in similar steel billets,which is meaningful for the corresponding automatic and digital intelligent analysis.

Based on production experience and slab cutting control rules,an intelligent cutting system for slab caster has been developed with functions of production plan,tracking and control,number automatic generation and information inquiry for the problem of low slab assignment rate in Beijing Shougang Co.,Ltd. The system in which the optimizing cut-length model of intermediate slab and tail slab has been development,changes the previous "make to heat" mode to "make to order" mode to better adapt new situation of multi-variety and small-batch orders. After the application of the system, the slab assignment rate has increased from 75% to more than 99% ,which provides a technical support for the improvement of order fill rate and the realization of low-carbon production.

With the concepts of " carbon peak" " carbon neutrality" and " low-carbon metallurgy" ,green intelligent manufacturing in the iron and steel industry has become the general trend. The process before ironmaking is the front end of the iron and steel metallurgy process,and it is also the primary energy consumption link. Therefore,the realization of green intelligent manufacturing for the process before ironmaking has crucial economic value and environmental protection significance. Focusing on the green intelligent manufacturing for the process before ironmaking in the iron and steel metallurgy process, taking the low-carbon metallurgical technology of " smart carbon use" as the core,and it summarizes the research progress of the intelligent perception of operating state,the intelligent control of operating parameters,the intelligent optimization of operating performance,and the intelligent collaborative management and control. The intelligent perception of operating state is the main method to obtain information about the operating state that is difficult to detect,including operating state monitoring and operating state recognition. The intelligent control of operating parameters is a prerequisite for the normal operation of the process before ironmaking,which mainly includes intelligent control based on human experience,intelligent control based on parameter prediction,and integrated intelligent control for multiple objectives. The intelligent optimization of operating performance is the main measure to improve the performance of operating state,including intelligent optimization of operating parameters and intelligent optimization of operating indicators. The intelligent collaborative management and control for the iron and steel metallurgy process focuses on the collaborative integration of perception,control,and optimization technologies. Finally,the current opportunities and challenges are analyzed. The big data analysis and intelligent perception of operating state,the integrated intelligent collaborative management and control,and performance improvement and optimization control of the whole process may become the prospects of green intelligent manufacturing for the process before ironmaking.

Considering that the performance degradation of the hydraulic looper control system would influent the production quality and efficiency in hot strip mill processes,system performance recovery oriented fault detection and fault-tolerant control schemes for hydraulic looper systems was proposed. The process model for a hydraulic looper system was established firstly,and an observer-based system performance degradation prediction index was constructed based on the Bellman equation. Then, a performance-based fault detection scheme was developed for the hydraulic looper system using the information provided by the performance degradation prediction index. Based on the information provided by the fault detection system,a system performance recovery oriented fault-tolerant control scheme was investigated for the hydraulic looper system. This fault-tolerant control scheme can recover the system performance degradation without changing the existing controller,and meanwhile enhance the robustness and fault-tolerant margin of the hydraulic looper systems. Finally,the proposed methods of fault detection and fault-tolerant control were validated by the simulation on a hydraulic looper model of a hot strip production line. 

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.

The iron and steel industry plays an important role in national economy. However,due to the characteristics of the iron and steel industry, such as long process, mutual coupling between processes,extreme production conditions,complex internal physical changes and chemical reactions, the process modeling,production control and prediction optimization of the iron and steel industry are severely limited,which further affects the improvement of production quality and production efficiency. In recent years,the vigorous development of digital twin in the industrial scenes has provided new ideas for the transformation and upgrading of the iron and steel industry. This paper first introduced the definition and connotation of digital twin,then analyzed the research hotspots of digital twin in the iron and steel industry,sorted out the relevant research results,and finally analyzed the current shortcomings in the application and development of digital twin,providing ideas for researchers'subsequent research,so as to promote the digital twin to play a greater role in the intelligent manufacturing of iron and steel. 

5G and industrial Internet have been combined with many aspects of the iron and steel industry under nation policies and practical needs,they have played a positive role in realizing the development of various links of iron and steel industry from decentralization and automation to centralization,intelligence and green. However,5G and industrial Internet stay in the production auxiliary link and have not yet entered the real production core link. Informatization and industrialization have not really been integrated. The development direction and key technologies of 5G + industrial Internet and industrial control were described in detail from this perspective,new network convergence technical architecture was proposed,which includes cloud PLC technology,5G+TSN end-to-end low delay and deterministic network supporting PLC cloud deployment key technology. Finally,the theory and technical framework of industrial control,computing and communication integration was put forward, and the latest progress in the scene test of mutual integration of 5G+TSN and cloud based PLC technology was introduced. 

Continuous processing, inaccessible internal states, pronounced lag between actions and quality outcomes, and rare-yet-costly failure samples distinguish the manufacturing of metallic materials from discrete manufacturing and dictate that industrial AI for steel rolling cannot remain at the level of prediction and generation, but must advance toward a goal-driven closed loop of perception, inference, decision-making, execution, and feedback. Taking goal-drivenness as the central thread, this paper analyzes the capability leap of artificial intelligence from generative AI to agentic AI and Physical AI, and proposes explicitly introducing “goal-driven” into the “data-intensive-intelligence-emergent-human-machine collaborative” paradigm to form a new paradigm for industrial control. A Vision-Memory-Controller (VMC) functional loop is used to characterize the core capabilities of the world model, clarifying its relationship to and distinction from digital twins, embodied intelligence, and Physical AI; three technical routes—full-stack-supported, spatial-simulation-based, and abstract-prediction-based—are summarized together with their bottlenecks. On this basis, a rolling industrial AI system architecture is constructed, in which the cloud-based world model provides global “Knowing,” domain-specific agents together with deterministic control provide field-level “Acting,” and full-stack vertical integration provides the engineering foundation. Given the marked differences among microstructure/properties, geometric dimensions, and shape quality in measurability, mechanism maturity, and execution complexity, three differentiated modeling paths are proposed: “data first—knowledge empowered,” “mechanism led—data compensated,” and “problem driven—virtual-physical integrated.” Representative engineering cases show that the relevant technologies have formed verifiable local closed loops within individual domains and are accelerating toward cross-process, multi-objective coordinated full-process autonomous decision-making, providing a theoretical foundation and engineering pathway for the intelligent transformation of metal industries such as iron and steel, as well as other large-scale complex process industries.

sadvantages in different 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 dievaluation 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.

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.

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 ”.

Problems in quality,cost,environment,stability and other aspects of the iron and steel industry need to be solved urgently,and " uncertainty" has become a major challenge faced by the iron and steel production process. With the development of digital economy and digital technology,data analysis technology has become the most effective method to solve the uncertainty problem. By giving full play to the advantages of application scenarios and data resources in the iron and steel industry, take the industrial Internet as the carrier,take the digital twin as the core,conquer key generic technologies,and build a future oriented digital innovative application. Relying on the full-process and full-scene digital transformation of iron and steel,accelerate the construction of iron and steel material innovation infrastructure,grasp the core competitiveness of enterprises,promote China忆s iron and steel industry to realize digital transformation and high-quality development

Hoist is the key equipment of dry quenching coke charging system,which has large load,frequent start and stop,unstable working condition,complex gear box structure and large reduction ratio.Therefore,the continuity and reliability of the hoist has always been the focus of production and equipment managers.An artificial intelligence algorithm based on multi-dimensional perception and neural network was proposed by the paper to identify the instantaneous fault of hoist equipment in real-time and monitor the hoist life cycle.By extracting and identifying the acoustic and vibration characteristics of the hoist gear box,combined with convolutional neural network (CNN) and long-short term memory (LSTM) neural network algorithm,extracting vibration space characteristics and time operating characteristics,judge and predict the operating state of equipment,so as to achieve real-time monitoring of equipment operating state,fault diagnosis and predictive maintenance.Engineering application practice shows that the method can effectively achieve 95.3% accuracy of fault identification,and can quantify the specific stage of the hoist′s operation life cycle,so as to remind maintenance personnel to respond to equipment faults in time,achieve predictive maintenance of hoist,reduce equipment failure rate,reduce maintenance costs,achieve continuous safe production,promote equipment professional management and smart factory construction.

To address the issue of unknown molten iron yield prior to tapping in blast furnaces,which leads to inefficiencies in transportation and scheduling of molten iron ladles,a prediction model about molten iron yield constructed and trained using the GA-XGBoost algorithm was proposed. After testing and comparing multiple models,the proposed method demonstrates a certain advantage in predicting molten iron yield from a multi-feature dataset,achieving an accuracy of 89. 64% within a ±10 t error range. Firstly,the missing and anomalous values in the experimental dataset is corrected. After normalization,the structured data is used for model training. The grey relational analysis method is then used to identify the key influencing factors of molten iron yield,and redundant parameters are removed based on process principles. In the end,15 feature variables are selected to form the input vector for model construction. Additionally,to quantify the impact of different operational parameters on molten iron yield,the SHAP calculation framework is employed,providing data support for regulating parameter in blast furnaces. This study achieves the prediction task of molten iron yield of blast furnace based on furnace characteristics,contributing to more efficient blast furnace regulation to improve production. Furthermore,by leveraging the prediction results,workers can pre-plan the transportation routes for the ladles,reducing heat dissipation and thus improving the cost-efficiency of blast furnace smelting.

In the sintering process, accurate prediction of the sintering burn-through point is essential to control the operation of the sintering machine, as it determines the quality of the sintered material and the efficiency of energy use. However, the current common sintering burn-through point prediction models mainly focus on single-step prediction, and they tend to ignore the spatio-temporal characteristics of the sintering process data. In view of the multi-step prediction task requirements of the sintering burn-through point and the complex spatio-temporal characteristics of process data, a multi-step prediction model of the sintering burn-through point based on encoder-decoder architecture was proposed. The spatio-temporal encoder-task decoder architecture effectively extracts the spatio-temporal features of sintering process data by stacking temporal convolutional networks and spatial attention mechanism, and adopts a task-specific guidance decoder to correspond to each step of prediction in the form of independent units. The effectiveness of the proposed method was verified by an actual sintering industrial process case. This study can assist operators to obtain information about the future state of the sintering process in advance, so as to adjust the operating parameters more accurately, reduce the quality fluctuation and energy waste caused by the lag, and improve the quality stability of the sinter.

At present, a new wave of technological revolution is emerging globally, represented by industrial 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 gradually emerged. Enterprises also have uneven information levels and serious information island phenomena. 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 images, sensor monitoring data, system control data, external drawing information and management systems, 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 complex 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".

Aiming at the problems of low production efficiency and poor information collaboration in the traditional steel rolling industry, this study carries out the construction practice of a digital rolling plant based on the industrial Internet platform. Through a series of measures, including constructing a centralized control center, upgrading digital production lines, promoting full-process digitalization of production operations, realizing intelligent decision-making for production control, establishing a visual performance dialogue mechanism, strengthening precise energy consumption control for processes, implementing digital management of the entire equipment life cycle, and improving the digital monitoring system for safety and environmental protection, the factory’s intelligent system is comprehensively improved. During the practice, the data barriers in all production links are effectively broken, and the deep sharing and integration of full-process data flow and information flow are achieved. This successfully promotes the transformation of the enterprise from the traditional heavy industry model to a new innovative development model with significantly improved automation, informatization, and scientific management levels. Specifically, product energy consumption is reduced by 12.7%, the yield rate and qualification rate are increased by 0.1% and 0.7% respectively, and production efficiency is improved by 18%. This study provides practical experience and theoretical basis for the digital upgrading of the steel rolling industry.

Hot rolling production planning is a key link in the iron and steel manufacturing process, involving product delivery fulfillment, capacity balance of multi-line and multi-process resources, and achievement of product specifications, properties, and quality. The level of planning optimization directly affects cost reduction, efficiency improvement, energy saving, and carbon reduction in steel enterprises. This paper analyzes the current status and research progress of production planning methods for hot-rolled coils and medium-heavy plates from the perspective of the steel production process. Considering the evolution of research objects and methods, as well as the difficulties in engineering applications, the paper discusses potential enabling approaches of intelligent optimization technologies, such as industrial big data and deep learning, under the new demands for high-end, intelligent, and green development of steel enterprises. Furthermore, the common trends in the development of production planning models for hot-rolled coils and medium-heavy plates are summarized. Key collaborative optimization issues are discussed, including steelmaking-continuous casting-hot rolling coordination, slab yard and reheating furnace coordination, and virtual-real slab collaborative planning. It is suggested that future research should focus on complex combinatorial sequencing optimization with multi-objective trade-offs and dynamically adjustable complex constraints, so as to develop a human-machine collaborative optimization method driven by the coupling of business rules, intelligent models, and dynamic data. This paper provides new ideas for research and industrial application of hot rolling production planning optimization methods in the iron and steel industry.

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 ironmaking front-end processes, encompassing the raw material yard, sintering, pelletizing, coking, and blast furnace ironmaking, constitute the most energy-intensive and carbon-emitting segment of steel manufacturing. Consequently, the level of intelligence achieved within these processes directly determines the overall competitiveness and sustainability of the entire production chain. Taking industrial big data empowerment as the central narrative, this paper systematically reviews the nearly fifty-year evolution of digital and intelligent technologies in ironmaking front-end processes. This evolution is delineated into four distinct stages: automation enlightenment, digital transformation, intelligent upgrading, and whole-process collaborative breakthrough. From this historical analysis, three overarching developmental trajectories are distilled: the hierarchical advancement of control architecture, the paradigm shift in modeling approaches, and the progressive evolution of decision-making modes. Building upon this foundation, the paper provides an in-depth examination of the current applications of industrial big data and artificial intelligence technologies across the five core unit operations, elucidating the fusion mechanisms between data-driven methods and mechanistic models under diverse operational scenarios. Through a systematic comparison of domestic and international technological pathways, the paper identifies existing limitations regarding the depth of data-mechanism-scenario integration and the breadth of cross-process coordination. Looking forward, future innovation directions are envisioned from four critical dimensions: industrial big data technologies, research on large foundation models, smart manufacturing technologies, and the synergistic integration of green and low-carbon strategies. This review aims to provide a systematic reference for both theoretical research and engineering practice in the intelligent manufacturing of ironmaking front-end processes, thereby supporting the high-end, intelligent, and green transformation of the steel industry.

In automation control for metallurgical unmanned overhead cranes,a critical challenge lies in balancing operational speed and anti-swing measures to enhance crane efficiency while ensuring safe operation. A metallurgical unmanned crane anti-swing full-range speed control method was pro- posed based on controller parameter switching derived from human expertise under multi-run states in this article. The method first plans the full-range speed control for the overhead crane linked by a large and small vehicle under anti-swing conditions. Then anti-swing control is optimized by switching PID parameters based on human crane operation experience under different running states,forming an integrated feedforward-feedback hybrid control for the unmanned overhead crane. Simulation experi- ments demonstrate that,compared to the commonly used manual operation of large and small vehicle linkage control,this method reduces overall operation time by 24. 9% . Field experiments show thatthe maximum swing angle is controlled within the range of 0. 45°-0. 85 °,and positioning accuracy is maintained within 10 mm. The relevant indicators are in a leading position in similar domestic scenar- ios. This method has been successfully applied and validated in the steel industry in China.

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.

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 AI-native steel plant architecture is designed to address prevalent industry pain points in the process of empowering intelligent manufacturing with large language models, such as fragmented architecture, bolted-on AI, and insufficient production safety constraints. The reference architecture comprises three layers from bottom to top: the Digital Asset Interface Layer (DAS), the Intelligent Core Layer, and the Application Agent Layer. Taking an industry-specific large model as the domain-wide intelligent engine, the architecture relies on the DLA digital twin ontology to accomplish structured modeling of production lines and activities, delineate the inference boundaries of the large model, verify generated outputs, and constrain model behavior, thereby achieving a dialectical unity between unleashing large model capabilities and enforcing industrial production safety constraints. This paper analyzes the positioning, core functions, and innovative value of each of the three layers, providing theoretical guidance and an implementation reference for the engineering deployment of AI-native steel plants.

The crane is an important transportation tool and core execution equipment for auxiliary operations connecting the upstream and downstream processes of the steelmaking-continuous casting section. Its scheduling level directly affects the logistics transportation efficiency and production rhythm of the steelmaking-continuous casting section. This article introduced the research status and progress of crane scheduling in the steelmakingcontinuous casting section in China. Firstly,the characteristics and existing problems of crane scheduling in the steelmaking-continuous casting section were introduced and analyzed. Secondly,the research methods and advantages and disadvantages of crane scheduling in the steelmaking-continuous casting section were summarized. Finally,the currently developed crane scheduling management system for the steelmaking-continuous casting section was introduced and summarized,providing research ideas and theoretical guidance for further achieving digital management and intelligent operation of the crane scheduling task process in the steelmaking- continuous casting section,and improving the integration level of production and scheduling in steelmaking-continuous casting section.

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.

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.

The stable control of casting speed in the continuous casting process of 65Mn steel is very important for the quality and safety of the casting billet. The current control scheme is to design a new control strategy by analyzing and summarizing the fault data in the past,but it is unable to deal with the fault equipment in the running state in real time. Based on the backstepping method and Lyapunov stability theory and so on,the fault-tolerant control(FTC) strategy proposed in this paper can realize real-time fault processing in the system under the condition of simultaneous sensor and actuator faults,avoid the problem of control accuracy and product quality decline caused by faults,and thus improve the reliability and accuracy of the casting speed system when faults occur. After the fault occurs,the method can still ensure the semi-global stability of the casting speed system,and the error converges to a small neighborhood near the origin. The simulation example verifies the effectiveness of the control strategy.

The continuous deepen of the global Industry 4. 0 revolution has driven the development of computationally intensive industrial applications such as smart steel. Steel production,characterized by diverse processes, complex procedures, and concentrated high-temperature and high-pressure equipment,necessitates real-time monitoring and analysis of production status to optimize production processes. Due to the opaque nature of each process,with real-time status being difficult to accurately obtain,digital twin (DT) technology offers a solution by facilitating real-time simulation and transparency in steel production,thus improving efficiency and quality. A multi-layer collaborative scheduling framework for industrial networks based on DT is presented,aiming to enhance resource management and network security. Challenges and explores solutions for novel resource scheduling and network security under DT-enabled industrial Internet of things (IIoT) is analyzed. Moreover,the exploration  of cross-layer resource scheduling strategies attracts extensive focus,facilitated by twin data at both the physical layer and data link layer. Finally,it points out the emerging trends in DT-enabled smart steel resource management,offering valuable insights for the digital transformation of the steel industry.

In traditional pyrometallurgical copper smelting, the cooling water temperature signals of the metallurgical furnace are often transmitted to the control system through hard wiring,which not only means high investment and large quantity of work,but also will cause inconvenience of operation and maintenance. In order to solve the above problems,fieldbus was used to replace the original signal transmission mode to build a cooling water temperature monitoring system. Taking a copper smelting project as an example,the temperature monitoring of cooling water in the side-blown melting furnace and top-blow furnace was realized by solving the problems of fieldbus type selection,key parameters setting,system configuration and program development. The actual results showed that the temperature monitoring system achieved the expected effect.

Sintering is a pre-process of blast furnace ironmaking,and the quality of sintered ore directly affects the quality and quantity of hot metal in the ironmaking process. Intelligent prediction and control of key parameters plays an important role in improving the quality of sintering ore in the sintering process. Firstly,the flowchart is introduced and its process characteristics is analyzed. Then, the predictive modeling research status for quality indicators and state parameters in the sintering process are reviewed. On this basis,the control methods about burn through point and ignition temperature in detail is illustrated. Finally,conclusions and prospects for the predictive and control modeling of key parameters in the sintering process are made.

With the rapid development of digital transformation in China′s steel industry, steel enterprises have accumulated massive knowledge and data assets. How to efficiently unearth the value of knowledge and data assets and gradually transform from digitalization to intelligentization has become a challenging problem. Large model has entered the stage of large-scale application, and the industry large-scale model is the key to its deep penetration into vertical fields. As a typical process industry, the steel industry has abundant scene resources and data assets, and urgently needs to be empowered by industry big models to create a new business model driven by the integration of knowledge, data, and intelligence, in order to achieve intelligent upgrading and high-quality development. This article first proposes the architecture design concept of the steel industry′s large model, and studies the data architecture, platform architecture, and application architecture; Then, the application of knowledge engines, intelligent agents for deep knowledge insight reports, metallographic detection models, and embodied intelligence models were introduced, exploring the application modes of natural language models, visual models, and multimodal models; Finally, the future development of the steel big model was discussed from the aspects of industry data space, collaborative system of large and small models, and application security protection.

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 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.