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 injection of hydrogen-rich fuel in blast furnace is an important measure for blast furnace to achieve green and low-carbon transformation and practice the dual carbon strategy. In order to accurately evaluate the effect of Shougang 2 650 m3 blast furnace injection coke oven gas on the reduction behavior of the comprehensive charge, this paper studies the effects of the hydrocarbon ratio, charge structure, charging mode and reduction temperature on the hydrogen-rich reduction behavior of the blast furnace according to the on-site production data and the national standard iron ore reduction method. The results show that with the increase of H2 ratio from 7% to 23%, the reduction degree of pellets and lumps increases significantly to 94.48% and 94.47%, while the reduction degree of sinter decreases to 88.36% due to carbon separation. The change of the comprehensive charge ratio has little effect on the reduction degree, and the overall reduction degree is stable at about 88%. With the temperature from 700 ℃ to 1 100 ℃, the degree of reduction was significantly improved, among which pellets performed the best (76.57% to 91.09%), followed by sinter (75.83% to 90.23%), lump ore was slightly lower (75.4% to 90%), and the reduction degree of comprehensive charge was the highest at 91.7%. The alkaline pellets have stable structure and excellent reduction performance at 900 ℃, and the surface begins to bond at 1 000 ℃, and the bond and crack intensification at 1 100 ℃ lead to a significant decrease in strength and performance. The reduction efficiency of the coke ore charging method was the highest, and the reduction degree of the comprehensive charge was 92.43%, which was significantly better than that of coke ore (86.61%) and coke ore blending (89.76%). The results of this study provide some data reference for the study of the influence of hydrogen enrichment in Shougang 2 650 m3 blast furnace on the reduction behavior of comprehensive charge.

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.

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 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.

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.

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.

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.

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.

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.

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.

In the steel continuous casting process, due to dynamic changes in production conditions, such as steel grade, casting speed, and temperature, single models exhibit poor adaptability in detecting surface defects of continuous casting slabs, and often fail to flexibly adapt to changes in different production environments, and tend to lose accuracy in complex practical operations, thereby leading to frequent false positives and missed detections, severely affecting the quality control of continuous casting slabs. To address these challenges, this study proposes a data-driven slab defect detection method based on multi-model fusion. First, a Gaussian Mixture Model was employed to cluster industrial process data, effectively distinguishing samples from different distributions. Next, local anomaly detection sub-models were constructed using autoencoders for each distribution. Control limits and reconstruction errors are determined for each sub-model. Finally, Bayesian inference was used to fuse the detection results of the local sub-models, enabling global anomaly detection in complex multi-condition environments. Using data from the continuous casting process of a certain steel manufacturing company, the proposed method is validated through comparative analyses of the detection performance of various models. The results demonstrate that the proposed approach achieves superior performance, effectively reducing both false negative and false positive rates. This method can be extended to other industrial data analysis and modeling scenarios, offering significant reference value for improving product quality through industrial data utilization.

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 advanced and intelligent manufacturing, the product structure of China’s cold-rolled strip industry is continuously upgrading, shifting from homogeneous competition dominated by general-purpose sheets toward a high-end pattern centered on advanced high-strength steels and premium coated sheets with higher added value. However, the control systems and their associated models, as the core support of cold rolling production, have long relied on imported technologies, leading to repeated introduction and poor adaptability, which makes it difficult to meet the requirements of high precision, high stability, and fast response under complex operating conditions. To address these challenges, a cross-process collaborative digital modeling and dynamic pre-control technology framework for cold-rolling quality was established. Relying on a cross-process metallurgical industrial internet platform, the framework enables data integration between cold rolling and its upstream and downstream processes, and introduces fusion and governance methods for multi-source heterogeneous data, thereby consolidating the data foundation for quality modeling and control optimization. Building on this foundation, hot rolling information was further utilized to develop head-tail trimming optimization and side scrap blockage pre-control strategies, enhancing feed-forward identification and dynamic intervention capabilities. Meanwhile, by combining hot rolling and cold rolling process parameters, an intelligent high-precision setup model for the cold-rolling process was developed to improve the accuracy and adaptability of rolling parameter setting. Finally, by integrating data-driven and mechanism-based modeling approaches, a quality prediction and multi-objective collaborative optimization strategy was proposed, enabling dynamic process regulation and continuous quality improvement under complex conditions. The above research provides systematic technical support for building an intelligent cold rolling quality control system with cross process perception and dynamic regulation capabilities.

In order to realize the rapid construction of the knowledge graph for the convertible steelmaking process scene while balancing its accuracy and practicality, this paper proposes a knowledge graph construction mode that integrates top-down and bottom-up approaches. On the one hand, guided by the principle of event evolution and under the direction of expert experience, this method completes the definition and classification of ontology classes and constructs the schema layer of the knowledge graph for converter steelmaking process scenarios using a top-down approach. Concurrently, it leverages a knowledge extraction tool based on LLM Graph Transformer to achieve automatic extraction of entities, relations, and attributes, thereby constructing the data layer of the knowledge graph for converter steelmaking process scenarios through a bottom-up approach. By integrating the schema layer and data layer, this method efficiently constructs the knowledge graph for converter steelmaking process scenarios.

This paper focuses on the innovative applications and development trends of digital twin technologies in the field of metallurgical engineering. As core components of Industry 4.0, digital twin demonstrates enormous application potential and broad prospects in the steel industry. Firstly, this paper reviews the definition, background, and core development technologies of digital twin technology, and expounds its significance in the metallurgical field. Secondly, based on the production process of the steel manufacturing system, it systematically combs the practical paths and application scenarios of digital twin technology in the metallurgical field. Finally, from a multi-dimensional perspective, it analyzes the technical bottlenecks in the industrial implementation process of steel digital twin, and based on the characteristics of the industry and technological development, prospects its future development direction. It is worth noticing that this article tracks the latest progress of cutting-edge technologies such as generative artificial intelligence algorithms, large language models, and intelligent agents, and analyzes the integration potential of such technologies with digital twin technology at different levels, providing a theoretical direction for the construction of the next-generation metallurgical digital twin system with intelligent cognition.

In response to challenges such as overcapacity in the steel industry, the “dual carbon” goals, the upgrading of customer customized demands, and the technological transformation of Industry 4.0, Xichang Vanadium and Titanium Steel has initiated the construction of a smart factory for high-quality vanadium-titanium steel. The aim is to achieve cost reduction, efficiency improvement, flexible production, and sustainable development through digital transformation. Addressing the problems of data isolation horizontally and attenuation vertically in the traditional pyramid architecture of information systems, Xichang Vanadium and Titanium Steel has carried out top-level architecture design, adjusted business, technical, data, and application architectures, and explored the migration of existing applications to a cloud-edge collaborative industrial Internet platform to achieve deep integration of systems and data. Focusing on scenarios such as “digital design of processes, dynamic optimization of processes, intelligent online detection, online operation monitoring, intelligent warehousing, real-time monitoring and emergency response of safety risks, environmental pollution monitoring and control, and supply chain optimization”, data empowerment has been carried out. A domestic largest-scale integrated control center for the iron-making area of vanadium-titanium magnetite smelting has been built, and a three-in-one digital system for consistent quality management has been constructed. Centering on the customer, the entire supply chain data and collaborative optimization from production to sales, distribution, and transportation have been integrated, significantly enhancing product quality and enterprise competitiveness.

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.

In modern industrial production, precise and efficient steel grade matching plays a pivotal role in material selection, scheduling, and process optimization. However, confronted with the escalating demands for complex multi-dimensional, fuzzy matching, similarity ranking, and weighted queries, traditional relational database-based query methods exhibit inherent limitations, resulting in low efficiency and insufficient flexibility. To address these challenges, this paper proposes an innovative framework for steel grade matching based on vector embedding and high-performance vector databases. The method first standardizes diverse and heterogeneous attributes of steel grades, such as chemical composition, mechanical properties, and heat treatment status, transforming them into unified-dimensional vector representations (embeddings). Subsequently, these vectors are stored in a high-performance vector database employing a hierarchical navigable small world (HNSW) graph index, enabling rapid and accurate steel grade querying and matching through Approximate Nearest Neighbor (ANN) search techniques. This paper elaborates on how various practical matching scenarios—including target value matching, range matching, multi-dimensional weighted matching, and similarity-based lookup from existing steel grades—are unified and implemented as retrieval problems within the vector space. Experimental results clearly demonstrate that when handling steel grade data at the scale of hundreds of thousands, the proposed vector-based method achieves millisecond-level query responses, showcasing significant efficiency gains and a breakthrough in capabilities for fuzzy and complex query scenarios. This research significantly enhances the efficiency, accuracy, and scenario compatibility of steel grade matching, offering a novel technical paradigm and implementation path for data-driven intelligent material management in industrial applications.

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.

Aiming at the issues of low switching frequency and significant current harmonics in standalone medium-voltage high-power grid-tied converters, a coordinated control method for multiple grid connected inverters on the continuous rolling grid side based on variable control period digital phase-locked technology was proposed. The distributed control units of each grid-tied converter sample the utility grid voltage as the synchronization signal. Through a digital phase-locked loop with a variable control period, they divide each fundamental wave cycle of the grid voltage synchronization signal into several segments and assign numbers to them. Subsequently, based on the corresponding segmentation points within the fundamental wave cycle corresponding to the sequence number of the grid-tied converter, triangular carrier increment and decrement counting was activated. This facilitates uniform phase shifting of the triangular carriers for each grid-tied converter, eliminating the need for additional interconnection lines between controllers. The correctness and effectiveness of the proposed control strategy were verified based on the on-site operational results of a 1 850 mm strip hot rolling production line at a specific steel plant.

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.

Implementing a full-process material tracking system for high-speed wire rod (HSWR) production can significantly enhance an enterprise’s control over the production process and product quality, facilitating its transition towards specialty steel manufacturing. Current material tracking in continuous casting, reheating, rolling, and finishing areas faces challenges including reliance on manual input, difficulty in achieving individual coil and per-billet tracking throughout the entire process, and insufficient accuracy and stability. The less than 100% accuracy of machine vision technologies used for identifying steel heat numbers and C-hook numbers contributes to the instability of HSWR material tracking in industrial practice. This paper proposes methods to improve environmental perception accuracy, such as constructing state machines, utilizing multi-system data comparison, and adding verification bits, thereby enhancing the stability of material tracking. For instance, the verification bit method applied to C-hook identification improved system stability by a factor of over 600. By establishing and linking the correspondences between “steel heat number→cast billet number→wire rod material number→C-hook number”, the system moves beyond single-process tracking to achieve a stable and reliable full-process material tracking solution. Practical applications confirm the effectiveness of the proposed methods and system, which effectively support quality traceability and cross-process coordination.

The intelligent upgrading of metallurgical industry standard interpretation faces multi-dimensional technical challenges, primarily characterized by the multi-version iterative nature of standard texts and strong semantic coupling between provisions. To address the limitations of retrieval-augmented generation (RAG) technology in processing standards, specifically knowledge fragmentation and weakened semantic relevance, this study proposes a hierarchical semantic recursive RAG. Firstly, a multi-level semantic parsing model based on document structural features was established, which achieving hierarchical decomposition and structured semantic representation of standard texts. Secondly, a recursive semantic aggregation algorithm was designed to enhance contextual semantic coherence through bottom-up multi-granularity summarization technology. Finally, a dynamic correlation discovery module was developed, leveraging large language models’ named entity recognition and semantic reasoning capabilities for deep exploration of cross-level constraint relationships. Experimental evaluations demonstrate that compared with baseline RAG methods, HSR-RAG achieves a 14.46% improvement in average F1-score on BERTScore metrics for QA results in professional test sets. In multi-hop QA scenarios, the framework reaches an average ROUGE-L score of 43.69%. This research provides a novel technological pathway for intelligent interpretation of metallurgical industry standards, offering significant practical value for promoting digital transformation in the sector.

This paper focuses on the intelligent flexible production smart factory，exploring the practical paths and methods of building wire production line transformation towards intelligence and digitalization. By analyzing the production characteristics of the wire industry and the pain points in intelligent and digital transformation,this study integrates advanced technologies such as 5G,digital twins, and industrial internet to construct an architecture and implementation plan for a flexible production smart factory based on the industrial internet.It summarizes innovative models of smart factories in flexible production, efficiency improvement, and quality optimization,providing replicable experience for peers in the industry. The research shows that building a smart factory can significantly enhance the flexibility and intelligence of wire production,achieving multiple goals including quality improvement,efficiency optimization, and cost reduction.The value of this research lies in its implementation from research to production practice, all elements involved in the intelligent factory of excellence, such as intelligent decision-making digital twin, human-machine collaboration, production and operation management, supply chain coordination, R&D and design functions are observable and available, and equipment intelligence, industrial internet technology, management, etc. represent the latest achievements in the industry, and as one of the 19 steel enterprises selected as the first batch of 235 national pilot enterprises, is a model factory for the industry to learn from.

To address the insufficient accuracy of traditional rolling force models, this paper proposes a method that integrates rolling mechanism analysis with industrial measurement. Based on the geometric characteristics of the rolling deformation zone, an Elliptical Velocity Field (EVF) is established, and the corresponding mechanistic model of rolling force is derived. To overcome the inherent prediction bias of the mechanistic model, the exponential smoothing method is incorporated. Furthermore, a trend adjustment factor is introduced to dynamically compensate for errors, thereby enhancing the adaptive correction capability. This process ultimately yields a high-precision Double Parameters Exponential Smoothing Prediction (DPESP) model. Verification using Q345 steel production data shows that the DPESP model achieves an average error of only 2.83%, which signifies a significant improvement in prediction accuracy. The research results can provide scientific guidance for constructing high-precision rolling force models and optimizing the rolling procedures for hot-rolled strips.

In steel production, full-process billet tracking is critical for ensuring production efficiency and product quality. Traditional technologies rely on multi-source sensors, which suffer from issues such as information errors and delays. Existing visual cross-domain algorithms are difficult to apply due to the limitations of steel plant scenarios. To address these issues, a cross-domain tracking algorithm based on Agent trajectory information association was proposed. Leveraging the fixed transportation paths and well-defined process nodes of billets, the algorithm integrates visual perception data with production rules through the collaboration of overhead crane and roller table Agent at network nodes, achieving precise cross-camera trajectory association without overlapping views or significant appearance features. Experimental results show that the algorithm achieves multi-object tracking accuracy (MOTA) of 95.8% and 82.1% in roller table and overhead crane cross-domain scenarios, respectively, outperforming comparative algorithms. This research breaks through the limitations of traditional tracking technologies and existing vision algorithms, providing an effective solution for intelligent tracking in metallurgical production. Future work will focus on enhancing performance through multi-modal data fusion and model optimization.

In industrial inspection, duplicate detection of X-ray weld images is crucial to prevent whole image or local area forgery and evaluate training sample consistency. Existing methods primarily address whole image duplication. However, it is difficult for them to detect tampering in specific regions. To address this problem, this paper propose a duplicate detection method based on segmentation of the weld bead and parent material regions in X-ray weld images. First, the gray-scale distribution characteristics of the weld bead and parent material regions were used to adaptively determine the boundary of the weld bead by comparing the actual gray-scale curve and the fitted gray-scale curve, and as a result, the weld bead and parent material regions were divided. Then, the perceptual hashing algorithm with high computational efficiency and robustness to small changes in brightness and contrast was used to calculate the perceptual hash values of the weld bead and the parent material regions respectively. Finally, the similarity between the regions was evaluated by calculating the Hamming distance of the perceptual hash values of the weld bead and the parent material regions of the two compared images, and the duplicate detection was determined by a given threshold. And the cross-area weighted fusion strategy can be also used to evaluate the whole image similarity. The proposed detection method was applied to test the tampered images in the duplicate detection experimental dataset constructed based on the GDXray dataset, with the detection rate serving as the evaluation metric. Experimental results demonstrate that the proposed method achieves superior detection accuracy, with an average detection rate of 95%, in scenarios such as region replacement, region modification, and composite tampering.

Slab number is a critical identifier in steel manufacturing for process tracking and intelligent logistics. However, poor print quality and harsh industrial environments often damage image data, seriously reducing the accuracy of slab number recognition. To improve the recognition capability of damaged images, a novel algorithm—Feature Recognition Inference Network (FRI-Net)—which combines damaged region detection, contextual feature reasoning, and attention mechanism to achieve high-quality restoration and accurate recognition of degraded slab number images. FRI-Net adopts a modular architecture, introduces a feature feedback optimization mechanism and Knowledge Consistent Attention (KCA), and significantly enhances the restoration capability for complex defective regions. Experimental results on multiple public and industrial datasets demonstrate that FRI-Net outperforms existing mainstream methods in recognition accuracy and fault tolerance, effectively enhancing the stability and intelligent level of slab tracking systems.

Incomplete cuts during the continuous casting slab cutting process can seriously affect production stability. The phenomenon of sparks reflection was regarded as an indicator of such incomplete cuts. However, relying on manual visual monitoring leads to high labor intensity and risks of missed detections. This paper proposes and implements a real-time automatic detection method for sparks reflection during slab cutting, based on the EfficientDet object detection algorithm. Experimental results show that the trained EfficientDet-D2 model achieves a precision of 97.87%, Recall of 92.00%, and a mean average precision (mAP) of approximately 97.76%. During a two-month on-site deployment, the method successfully detected sparks reflection events, significantly enhancing the level of automation in continuous casting process monitoring.

To address the challenges of infrequent detection and prolonged measurement cycles for FeO content in iron ore sinter, this study proposes a soft-sensor model for online estimation of FeO content using routinely monitored parameters from the sintering process as input variables. The model development first employs the Random Forest (RF) algorithm to identify high-impact variables, subsequently constructs a Multilayer Perceptron (MLP) to capture the intricate nonlinear relationships between input variables and FeO content, and finally integrates the Wild Horse Optimizer (WHO) algorithm to optimize MLP hyperparameters, thereby enhancing both model fitting accuracy and generalization capabilities. Validation using real-world production data demonstrates that the proposed model achieves an accuracy of 91.26% within ±0.5% error margin. This framework provides actionable insights for industrial applications, effectively mitigating FeO content fluctuations and advancing operational precision in sintering production, with significant implications for optimizing process stability and elevating metallurgical manufacturing standards.

To address the issue of low thickness control accuracy during non-steady-state rolling processes, such as specification switching and roll change in hot continuous rolling, this paper proposes a data-driven model based on Stacking ensemble learning for thickness prediction during specification switching. The optimal hyperparameter configurations of both the base learners and the meta-learner are determined using a Bayesian optimization algorithm. The performance of the Stacking model is systematically compared with that of other mainstream models. Additionally, the SHAP (SHapley Additive exPlanations) method is introduced to interpret the Stacking model and evaluate the importance of input features. The results demonstrate that the proposed Stacking model effectively integrates the predictions of base learners, significantly enhancing the accuracy of thickness prediction. The performance evaluation metrics of the developed Stacking model are as follows: root mean squared error (RMSE) of 0.019 9, mean absolute error (MAE) of 0.014 9, and coefficient of determination (R2) of 0.999 9. The probability that the error between the predicted and actual thickness is within ±35 μm reaches 94.8%, while the probability within ±50 μm is 97.8%, achieving high-precision thickness control during the specification switching process in hot continuous rolling.

The control of the final cooling temperature after medium plate rolling is a core technology for improving product quality and production efficiency. The accurate prediction and regulation of the temperature hit rate have attracted much attention from both the academic and industrial communities. Early research was dominated by mathematical analytical models, which constructed modified Newton′s cooling law models based on heat transfer theory. Although these models had high computational efficiency, their adaptability to complex working conditions was limited. With the development of computer technology, the finite difference method (FDM) and the finite element method (FEM) have been widely applied in temperature field simulation, enhancing prediction accuracy through discretization. However, they rely on a large amount of experimental data for parameter calibration and have high computational costs. In recent years, machine learning models, with their strong nonlinear mapping capabilities, have become a research hotspot. Algorithms such as BP neural networks and XGBoost models have shown significant advantages in predicting the final cooling temperature hit rate. For model optimization, scholars have proposed innovative methods such as adaptive tuning of hyperparameters. By integrating mechanism-based and data-driven strategies, these methods effectively address the bottlenecks of industrial data noise sensitivity and insufficient generalization ability.Although machine learning technologies have demonstrated significant application value in industrial production, the steel industry—as a quintessential traditional complex industrial sector—features production processes characterized by multi-stage coupling and high dynamism.This results in multiple adaptation challenges when introducing and implementing machine learning technologies, further constraining their large-scale application. Future research should focus on multi-modal coupling modeling, energy-saving cooling process optimization under low-carbon targets, and the application of explainable artificial intelligence in industrial decision-making, thereby promoting the leapfrog development of hot rolling cooling control towards intelligence and greenness.

The slag grinding process is essential for manufacturing slag powder, which is a green raw material. The quality of its indicators directly affects the economy and safety of the entire process. However, this process involves multiple controlled variables and features strong nonlinearity, large feedback delays, and frequent fluctuations in operating conditions, making it difficult to control the indicators of quality, throughput, and temperature. To address these issues, this paper designs an intelligent control system for slag grinding based on model predictive control. The system first performs intelligent optimization of the control targets through an existing platform, and then controls the quality, throughput, and temperature indicators using the model predictive control method. Practical operating results show that the application of this system reduces the unit gas consumption and unit power consumption from 35.7 m3/t and 40.4 kWh/t to 34.1 m3/t and 38.7 kWh/t, respectively. These results indicate that the proposed method can significantly reduce energy consumption while maintaining stable product quality, thus demonstrating its practicality and effectiveness.

Aming at the problems of rolling force calculation and process optimization in the hot continuous rolling process of high-strength non-oriented silicon steel, the influence of deformation temperature and deformation rate on the hot rolling deformation process of high-strength non-oriented silicon steel was studied through thermal simulation experiments and analysis of true stress-strain curves. A corresponding deformation resistance model was constructed, and the regression coefficient of the deformation resistance curve was solved. Based on the rolling force model, the influence of different rolling speeds and deformation temperatures on the difference in rolling force changes was analyzed. Furthermore, with the goal of balancing the equipment capacity of each stand rolling mill, improving product performance quality and production line efficiency, the objective functions of balancing the remaining proportion of rolling mill equipment capacity and controlling the temperature and speed of hot rolling process were established. The optimization technology of high-strength non-oriented silicon steel rolling process was developed, and the reasonable setting of inlet temperature and rolling speed was achieved, and significant application effects were achieved.

In the strip steel grinding operation of iron and steel enterprises, although the use of robots to replace manual labor can significantly improve grinding efficiency, stabilize grinding quality, and reduce missed grinding areas, the adaptability between robots and moving strip steel still needs to be improved, and the core issue is the path planning of robots for grinding moving strip steel. Aiming at this key technical problem in practical scenarios, this paper first analyzes the relative motion law between the robot and the strip steel through a graphical method, and accordingly determines that the robot end adopts a grinding path of reverse grinding-oblique connection. On this basis, the relationship between the robot’s motion parameters and the width, length of the ground area of the strip steel, and the strip steel speed is established, and a corresponding algorithm is proposed based on this relationship. To verify the effectiveness of the designed grinding path, a simulation experiment was carried out in Robotstudio. The results show that the path can ensure no missed grinding areas during the strip steel grinding process, verifying its feasibility and reliability.

In hot strip rolling mills, loopers are installed between finishing stands to ensure the balance of metal mass flow and the stability of strip tension during the rolling process. Therefore, the control accuracy and dynamic response characteristics of the looper system directly determine the quality of the final strip product. To address the control challenges caused by the multivariable strong coupling, nonlinear and time-varying characteristics of the hydraulic looper system, this paper designs a composite intelligent control strategy that combines an Improved Particle Swarm Optimization (IPSO) algorithm with Back Propagation Neural Networks (BPNN) Proportional-Integral-Derivative (PID). A feedforward compensation decoupling control loop is constructed to weaken the coupling effects in the system. An improved PSO algorithm with dynamic adjustment mechanisms for inertia weights and learning factors is developed to solve problems in traditional BP neural network PID, such as strong randomness in weight initialization, tendency to fall into local optima, and slow convergence speed. Simulation experiments based on the MATLAB/Simulink platform demonstrate that the proposed IPSO-BP-PID algorithm achieves better control performance compared to both BP neural network PID and traditional PID under unit step input signals, verifying the effectiveness of the IPSO-BP-PID algorithm.

Longitudinal cracks are common and serious quality defects in slab production. Accurate prediction of longitudinal cracks is of great significance to improving slab quality and production efficiency. However, in actual production, the number of longitudinal fissure samples is far less than that of normal samples, resulting in extremely unbalanced data distribution, which affects the effect of model training and brings huge challenges to model construction. How to improve the generalization ability and prediction accuracy of the model has become a key problem. To this end, firstly based on a large amount of measured temperature data of thermocouples on the copper plate of the mold, the sliding window technology was applied to extract temperature samples of longitudinal cracks′ and other conditions. Then, a slab surface depression-type longitudinal crack prediction method based on grid search optimized Convolutional Neural Network (CNN) and Bidirectional long Short-Term Memory (BiLSTM) network was proposed. The samples were input into the CNN-BiLSTM network, CNN was used to obtain the local features of the time series, and BiLSTM was used to obtain the long-term dependency features. Finally, the slab longitudinal crack prediction output was performed through the fully connected layer. Experimental results show that the proposed grid search optimized CNN-BiLSTM model performs significantly better than other models on the test set, with a prediction hit rate of 99.29% for longitudinal crack temperature waveforms, a false alarm rate of 0.71%, and a Matthews Correlation Coefficient (MCC) as high as 0.96, and the training and prediction speeds of the model are relatively fast. The research results provide a reliable theoretical basis for the identification of longitudinal cracks on the slab surface.