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.

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

In 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.

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.

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.

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

The 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.

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.

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 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.

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.