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.

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 deepening of the transformation of the steel industry towards automation and intelligence,intelligent equipment systems based on machine vision have been widely applied due to their outstanding advantages of low cost,high accuracy,stability and reliability. Based on extensive practical experience in industrial sites,this article studies and designs an intelligent equipment software platform for the steel industry. It adopts a layered structure,builds underlying libraries,development templates,communication protocols,instruction sets,etc. as the basic support,and provides essential software functional modules in the intelligent equipment system to assist in the rapid construction of new applications. The platform module involves a comprehensive process of data acquisition,storage, image algorithm processing,device control,data display,and fault diagnosis,fully considering the structural requirements and functional support for intelligent equipment in industrial scenarios. Through this software platform,the development time of specific intelligent equipment applications can be greatly reduced,and the requirements for application implementation can be met while ensuring system stability.

The steel manufacturing process is complex and dynamic,involving multiple stages,numerous constraints,and nonlinear couplings. Each unit is heterogeneous and diverse,making it challenging to provide mathematical descriptions and solutions. Based on multi-agent technology, various process rules are transformed into behavioral constraints for intelligent agents. A multi-level networked intelligent agent dynamic simulation model for the steel manufacturing process has been constructed. Through connections, collaboration, feedback, and coordination among intelligent agents, emergent properties vividly demonstrate self-organizing behavior characteristics arising from nonlinear interactions between unit processes under different production states and between unit processes and the overall process. The simulation accuracy for runtime is within 10 minutes,and the hit rate is not less than 95% . This provides a new quantitative and iterative approach for formulating process operational mechanisms, validating process optimization rules, and facilitating collaborative scheduling across different processes.

An online thickness prediction algorithm for strip steel was proposed to address issues of strong coupling and low accuracy in the thickness mathematical model. Firstly,the rolling data is used to establish an online thickness prediction model based on light gradient boosting machine (LightGBM) model. Then improved bat algorithm (IBA) is applied to improve the LightGBM model parameters,and a self-learning system is deployed to optimize the results. Finally,the predicted results are compared with the actual thickness to verify the accuracy of the prediction model. The experimental results show that the online thickness prediction algorithm can quickly and accurately predict the strip thickness. When the IBA-LightGBM model was used to predict the 2 mm,3 mm,4 mm,and 5. 65 mm  strips,root mean square error ERMS (RMSE) can be controlled within 11. 0 μm,11. 5 μm,11. 6 μm and 16. 4 μm respectively. The results can improve the accuracy of the thickness mathematical model for hot rolling strip and enhance the level of the thickness control system.

The blast furnace ironmaking process is of great significance in the iron and steel industry. However,due to the complexity of the blast furnace ironmaking process and the existence of high temperature,high pressure,and complex physical and chemical reactions,it is still a major challenge to establish an effective process monitoring model. Aiming at the requirement of multi-step real-time prediction of hot metal key quality indicators in the process of blast furnace ironmaking,this paper refers to the theory of continuous learning (CL) that simulates the human brain's hippocampus and neocortex,and constructs a CL-based time series data pre diction modeling framework based on multihead self-attention mechanism,one-dimensional residual neural network (ResNet),long short term memory network (LSTM) and multi-layer perceptron (MLP) in order to realize the multi-step realtime prediction task of hot metal key quality indicators of the blast furnace ironmaking process. The experimental results show that the proposed CL-based method is superior to the traditional deep learning models,achieves higher prediction accuracy,and with the increase of prediction time step,the proposed CL-based model shows strong robustness.

Under the development trend of the digital transformation of the steel industry,it innovates the working mode of data management,conducts in-depth management for the full life cycle of data generation,circulation,processing and application,and promotes thematic data governance. Relying on advanced automation and information technology,it has realized the interconnection between systems at all levels and intelligent single equipment,and made full use of "data-driven + platform support" to build a new production and manufacturing and service system. In-depth exploration of data governance,big data analysis and utilization of effective ways,through the " three steps" of " data interconnection,data governance,data application" ,to achieve the effective application of tens of thousands of information of the company,to achieve the conversion of data resources to assets. Open up the data network to form enterprise data assets,realize the transformation of the " data flow" in the system to the "value stream" of enterprise development in a real sense,and improve the intelligent level of the company忆s lean operation management and control,analysis and decision-making process through thematic data governance.

In order to further improve the intelligent manufacturing level of production planning and scheduling at Laiwu Iron and Steel Group Yinshan Steelmaking Plant,a systematic study was conducted on the complex scheduling plan of 4 furnaces,4 machines,and non matching furnace machines. By analyzing the operation cycle and production operation mode of the steelmaking refining continuous casting process,four bottom level matching principles were explored and summarized,including the dynamic total balance principle,furnace machine matching principle,non interrupted pouring principle,and space proximity principle. Based on the four principles of matching logic,a furnace machine matching scheduling model was established,which achieved automatic scheduling of steelmaking plans for multiple furnaces to multiple machines in the non matching state of the furnace machine. This ended the production scheduling mode that was completely centered on dispatchers and driven by manual experience,greatly improving scheduling efficiency and planning accuracy. In addition,the dynamic scheduling model of the furnace machine based on the matching logic of the four principles has strong flexibility and universality,and can be applied to more steel enterprises.

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.

Crane scheduling in steelmaking and continuous casting section is a typical multi-machine and multi-task constraint problem,which is of great significance to the connection and forward flow of each process and the control of the production rhythm of the whole steelmaking plant. In order to im- prove the efficiency of crane operation and ensure the stability of production,a crane scheduling mod- el based on spatio-temporal heuristic algorithm was established by analyzing the process flow and the constraints of crane job scheduling in steelmaking and continuous casting section. Partition rules,task allocation rules,collision avoidance rules and state update rules are designed to characterize the oper- ation process of the crane. The model is solved by a heuristic method,which can avoid the high com- putational complexity of traditional theoretical methods in large-scale problem solving. The results show that the model can provide a reasonable scheduling scheme and effectively avoid the spatio-tem- poral conflict during the operation of the crane. Compared with the actual production scheduling method,the performance of the crane under the heuristic algorithm model has been improved,and the operation efficiency of the crane has been improved.

Aiming at the outstanding problems of insufficient digitization of iron making process,low degree of intelligence,lack of a unified intelligent platform,and far from adapting to the development needs of intensification,digitization and intelligence,Angang Steel Co.,Ltd. established a big data centre for intensive control of blast furnace with blast furnace group as the core and covering other processes. The big data centre breaks the information silos of each regional information system,releases the effectiveness of data. The blast furnace group and subsidiary process form a centralized control and management centre for data sharing and efficient collaboration,realizing the blast furnace process upgrading from an intelligent unit to an intelligent platform. At the same time,the intelligent application model of blast furnace is built,which realizes the visualized intelligent monitoring of the safe production and operation of blast furnace,and guides the production operation of blast furnace,as well as improves the digitization and intelligence level of the production,technology and management of the blast furnace of Angang Steel Co., Ltd.

The production scheduling problem of steelmaking-continuous casting process in steel enterprises has characteristics such as multiple paths,multiple interferences,multiple constraints,and multiple objectives. Traditional methods struggle to adapt to the complex and dynamic on-site environment. The paper summarizes the rules of steelmaking scheduling and proposes a simulation optimization modeling method based on multi-agent technology that is adapted to it. This method abstracts the complex steel production process into a multi-agent system,simulating and optimizing the complex logistics system. It can fully reflect the details of the process,time requirements,and process constraints,addressing the intelligent production scheduling and dynamic scheduling issues in steelmaking plants.

In order to achieve more accurate calculation of alloy addition amount,a comprehensive al- gorithm based on genetic algorithm ( GA) optimization for BP neural network was adopted. During the training process of the BP neural network,the refining starting composition of the molten steel was used as input parameters for the BP neural network model. Then,the fitness function of the GA was used to optimize and adjust the weight and threshold of BP neural network,predicting the refining endpoint steel composition of the LF furnace. By comparing the prediction results of BP neural net- work algorithm and GA-BP neural network algorithm, it was found that the GA-BP algorithm has smaller mean absolute error (MAE) and mean square error (MSE) ,and the prediction results aremore accurate and basically consistent with the actual steel composition,indicating that this model can be used in production. Based on the GA-BP neural network model,the alloy addition amount was determined according to the composition of the molten steel at the beginning of LF refining and con- trol composition requirements. After deploying a predictive model in the 140 ton ladle LF refining sys- tem of a steel plant and tracking 188 furnace data,the difference between the actual amount of alloy added and the predicted amount was within ±30 kg,then the prediction accuracy of high manganese alloy was 91. 3% ,high chromium alloy was 90. 4% ,ferrosilicon alloy was 90. 2% ,and the predic- tion accuracy of carburant was 91% in furnaces,from which can guide the determination of alloy ad- dition amount in the actual refining process. 

Sintering is one of the important processes in blast furnace ironmaking. Using intelligent technology to accurately predict the state,quality and other parameters during the sintering process to control the sintering process is crucial for reducing production costs,optimizing the sintering process, and improving production safety. Firstly,the role of big data platforms in sintering parameter prediction technology was analyzed. Secondly,the application of intelligent technology in sintering production process was compared and analyzed from the aspects of sintering endpoint position prediction, sintering material layer permeability prediction,sintering endpoint temperature prediction,FeO content prediction,drum strength prediction,ignition temperature prediction,bellows valve opening prediction,and economic and technical index prediction. The development process of FeO content prediction from manual observation method to mathematical modeling method and then to artificial intelligence method,revealing the development status and evolution law of intelligent sintering prediction technologybasedonbigdata.Inaddition,theshortcomingsanddevelopment trendsof intelligent sinteringpredictiontechnology insinteringproductionpredictionwerediscussed.

Continuous casting is the central link in the steel manufacturing process, and the quality of continuous casting slab directly determining the production efficiency and quality of steel products. In the process of steel manufacturing,online prediction of the occurrence and disappearance of quality defects in the casting slab,timely tracing of the causes of defects,plays an important role in improving the quality of the casting slab,stabilizing the process connection,and reducing production costs. In response to the characteristics of the continuous casting production process,with slag inclusion defects on the surface of the slab as a breakthrough point,based on the principles of the continuous casting  process and the data platform for analyzing the continuity of the entire process of HBIS Group Laoting Iron and Steel Co.,Ltd.,information communication technology,computer technology,and big data mining technology are applied to develop a continuous casting slab quality analysis and monitoring system,which achieves functions such as matching and integrating slab quality data,predicting and tracing slab quality defects. The system has been applied online on the slab continuous casting machine. The prediction accuracy of slag inclusion defects on the surface of continuous casting slab is 99. 95%,the false alarm rate is 0. 02%,and the missed detection rate is 35. 41% . The successful online application of the continuous casting slab quality analysis and monitoring system has promoted the transformation and upgrading of intelligent manufacturing at HBIS Group Laoting Iron and Steel Co.,Ltd.

Improving the operation efficiency of cranes in the steelmaking area can reduce energy consumption for transportation while effectively linking the preceding and succeeding processes, which is of certain value for green production,cost reduction,and efficiency increase. In this regard, this article proposed a crane scheduling optimization method driven by simulation modeling and machine learning. Firstly,multi-agent is used to establish a production simulation model for the steelmaking area,which is driven by historical production plans and crane scheduling workflows. Subsequently,the simulation model is run multiple times to obtain a large number of high-quality crane operation samples through built-in sample evaluation formulas. Finally,a random forest model is employed to learn from the samples and obtain a machine learning model for matching cranes with transportation tasks. Experimental analysis shows that applying the machine learning model to crane scheduling decisions can increase the proportion of effective transportation time,thereby reducing energy consumption losses caused by mismatched transportation tasks,path avoidance,etc. This advantage is particularly significant under heavy production loads. Furthermore,the crane scheduling machine learning model is decoupled from the steelmaking plan,exhibiting high flexibility in practical app lications.

To address the issues of low detection accuracy and slow speed in the inspection of slab on steel continuous casting production lines,an algorithm named DMS-YOLOv8 (YOLOv8 with depthwise separable convolution,multi-pooling,and SE-EMA) that combines machine vision,image processing,and deep learning was proposed. This algorithm replaces standard convolution with depthwise separable convolution based on reverse residual and multi-scale pooling,reducing the burden of redundant networks,decreasing memory usage,and improving computational speed. The mixed attention mechanism SE-EMA allows the model to selectively focus on and weight information from different parts when processing input data,enhancing the model's expressive power. Finally,by conducting comparative analysis and ablation experiments on the self-made slab dataset and the PASCAL VOC2012 dataset,the study validates that the proposed method can effectively enhance slab detection accuracy in real-time operating conditions while ensuring a certain level of efficiency,laying the foundation for efficient production in subsequent hot rolling processes.

Addressing the challenges of the heavy workload involved in scrap steel classification and the lack of uniform grading standards,this paper employs machine learning to identify and compare scrap steel images for determining the scrap steel grade. A scrap steel classification dataset is constructed,and the Siamese neural network is utilized to train the dataset,selecting the optimal weights to enable the model to accurately distinguish different types of scrap steel. The similarity between the scrap steel benchmark and the image to be tested is compared using the Siamese network calculation method. Based on the similarity results,the scrap steel grade is determined. When the similarity approaches 1,the scrap steel shapes are considered similar. When the similarity approaches 0,the shapes are deemed different,allowing for the determination of the distribution of scrap steel types in the image. Experimental results demonstrate that the method of scrap steel classification using similarity comparison and the Siamese neural network exhibits excellent accuracy and reliability. Compared with traditional manual classification methods,this approach not only significantly improves classification efficiency but also achieves standardization and consistency in scrap steel grading.

In order to solve the widely existing problem of excessive head thickness deviation of the first strip after finishing roll change in hot strip mill,a hybrid feature selection method based on variance selection,mutual information,L1 /2 regularization combined with experts'experiences was proposed. The method is used to select features from historical production data of the first strip after roll change in a 1 700 hot strip mill in China,and the feature selection results are used as the training set of GA-BP neural network-based head thickness deviation prediction model for the first strip coil after finishing roll change. A series of experiments are carried out on the model,and mean absolute percentage error (MAPE),mean square error (MSE),coefficient of determination (R2) and other indicators are used as model evaluation criteria. The results show that the hybrid feature selection method  proposed in this paper has significantly improved the prediction accuracy of the model after training compared with the traditional mathematical feature selection method. Through testing on data samples of different steel grades and different thickness intervals of the main products of a steel mill,it is verified that the model has a high prediction accuracy with a certain degree of generalizability. The method has good application prospects in production practice.

According to the analysis of the functional requirements of blast furnace bottom filtration process (normally called OCP) and the structure of bridge grab-crane,the intelligent control system of grab crane was proposed. The system adopts the technology of automatic positioning of crane and grab bucket,frequency conversion speed control,anti-sway grab,wireless network communication, video surveillance,knowledge learning algorithm,etc. ,to achieve the intelligent control of the grab crane. The system divides the filter tank into grid according to the process requirements, and adopts an efficient slag grabbing strategy to automatically control the grab bucket to complete the grabbing and slag discharge operations. The practical application results of the intelligent control system of grab crane in the bottom filtration process of many blast furnaces show that the operation rate of the system is more than 99%,which is 32. 5% higher than the efficiency of manual slag grabbing,reducing staff 66. 7%,and the maintenance cycle is doubled.

Traditional mechanical properties prediction and optimization methods are mostly based on experience and mechanisms,and do not fully consider the value contained in the data. One of the current research hot spots is how to explore the linear and nonlinear transfer relationship between steel performance and related process parameters,construct high-precision performance prediction models, and achieve process optimization. Based on the high-dimensional process quality dataset of the throughout manufacturing process of hot rolled strip,a performance optimization method for hot rolled strip steel was proposed that integrated machine learning performance prediction model and Shapley additive explanation (SHAP) interpretation framework. This method first uses maximal information coefficient (MIC) metrics to select effective variables that have a significant impact on mechanical performance indicators from high-dimensional process parameters dataset. Then,by comparing the prediction accuracy of performance prediction models based on multiple output support vector regression (MSVR),support vector regression (SVR),and random forest,the optimal performance prediction model is selected. Finally,based on the SHAP interpretation framework and optimal prediction model,process parameter evaluation is conducted to measure the quantitative impact of each process parameter on the final performance,and the operational variables are adjusted according to the results of SHAP analysis to verify the effectiveness of performance optimization. The experimental results indicate that the performance optimization method proposed in this paper can significantly improve performance indicators according to demand,and has guiding significance for mechanical performance control in steel production processes.

The use of metallurgical robots to replace manual labor to engage in some dangerous and repetitive labor has gradually become one of the important elements of the construction of unmanned production lines in major factories. In some application scenarios,the robot忆s end-effector needs to be in close contact with the workpiece. When an abnormal situation occurs,the robot may trigger a collision alarm,and require the operator to manually recover the robot,greatly affecting the production efficiency and posing a large safety risk. For those applications that require contact work,based on the Rapid language of ABB series of robots,a collision adaptive control program and anti-collision monitoring along with distance compensation program were developed. Then without manual intervention, the robot can automatically carry out the next action,to ensure the normal operation of the production process. Both solutions have been put into use in the steel plant,the effect is good,and improve the level of intelligence in the steel plant.

The camber defect in the hot rolling rough rolling area affects the shape quality and rolling stability of the strip steel. The research on online detection and control technology of camber has always been a hot topic of industry. This study develops an online detection and control technology for camber based on image recognition. By installing high-definition cameras in front and behind the stand to obtain strip steel images,image processing technology is used to measure and recognize the contour shape of the strip steel. Combined with actual measurement data,equipment data,model setting data,etc. during the rolling process,a camber adjustment model is constructed,online automatic feedforward control function for camber is implemented. This technology has been put into online application on a hot rolling production line in China,with an accuracy rate of over 95% for adjusting the camber direction. It can effectively reduce production costs and labor intensity,improve the automation level of the production line,and has high application value and development prospects.

Due to the increasing requirements of current steel product quality,a stacking algorithm based on multiple machine learning models was proposed to improve on-site production while reducing slag defects caused by high-frequency data fluctuations within the continuous casting. Firstly, high-frequency feature data which extracted from the continuous casting mold is segmented into subsequences by sliding window,important features in each window are extracted from the fluctuation patterns of the high-frequency data. Secondly,the locations of the slag defects which identified by the hot-rolling surface inspector are corresponded to the window locations. Finally,a stacked classification algorithm that combines multiple machine learning models is utilized for defect prediction. Comparison experimental results show that the stacking classification model performed better than each single machine learning model and voting classification model,and the overall prediction performance and  generalization ability is better than others. Currently,this slag defect analysis model based on the stacked ensemble algorithm has been deployed on a hot-rolling production line. The comparison of online prediction results with actual data demonstrates that the occurrence and location of slag defects can be predicted in advance by the model to improve the efficiency of slag defect analysis and new approaches and insights are given for investigating the causes of slag defects.

Iron ore sintering is a key preliminary process in blast furnace ironmaking. Online realtime intelligent sensing of key production indicators in the sintering process is one of the key technologies to achieve green,low-consumption,and efficient development of the sintering process. However, on one hand,traditional methods for measuring sintering production indicators (such as FeO content) are time-consuming and difficult to meet the needs of real-time control. On the other hand,the sintering process data has nonlinearity,multi-source heterogeneity,and time lag,which poses a great challenge to improving modeling accuracy. To this end,this paper proposes a multi-source heterogeneous  data fusion and real-time sensing technology for the sintering industry. This study uses a multi-source heterogeneous information fusion method to extract shallow and deep features respectively through expert knowledge and the FcaNet model based on discrete cosine transform (DCT) for the cross-sectional image data of the sintering machine collected by the infrared thermal imaging camera,achieving feature level and data level fusion. In the prediction task,the two-dimensional convolution block is applied to the time series data,and FcaBlock is used as the convolution block for feature extraction, which effectively extracts the frequency component information of the time series data. On the iron ore sintering data set of a real steelmaking plant,the prediction accuracy and stability of this model are superior to existing models,and it significantly improves the online real-time sensing of key quality indicators of the sintering process.

As an important link in the whole process of iron and steel production,the ironmakingsteelmaking interface covers the processes of blast furnace ironmaking,ladle allocation,ladle transportation,and KR station entry. The technological paths between the various procedures are complicated,the production scenes are complex,there are many abnormal situations,and the scheduling rules are complicated. In order to better meet the actual production needs,the dynamic scheduling system of ironmaking-steelmaking has changed the original inefficient communication and scheduling methods. With the goal of improving the coordination of blast furnace tapping operation,iron transportation,and steelmaking processes,enhancing scheduling efficiency,and to provide scientific and efficient scheduling methods,the dynamic scheduling system is utilized to enhance the scheduling capabilities of the ironmaking-steelmaking interface. By flexibly adjusting based on the complex and changing actual operating conditions while integrating existing scheduling rules, collaborative dynamic scheduling between ironmaking and steelmaking is achieved.

The hanging of blast furnace is one of the abnormal conditions which can easily occur during blast furnace smelting. Because the hanging diagnosis in actual production mainly depends on the experience of the operator,the personal subjectivity is large and the transmission is poor,and it is easy to misjudge and lead to a large fluctuation of the furnace condition. With the development of computer technology,data-driven process monitoring theory has gradually matured,and fault diagnosis based on machine learning has been widely used in complex industrial production and has achieved good diagnostic results. Therefore,this study analyzed the hanging phenomenon and its formation causes,and conducted data cleaning on the initial feature set based on expert experience. Recursive feature elimination (RFE) algorithm was used to screen valid variables and determine their importance. The gradient boosting decision tree (GBDT) was established according to the selected key indexes for real-time hanging diagnosis. The results show that the accuracy rate of GBDT classification by integrated learning voting mechanism is more than 90% ,showing strong robustness,that is,the diagnosis model has excellent performance in both accuracy and computational efficiency. Based on the diagnosis model,the intelligent diagnosis system can help the BF operators to monitor the hangings in real time,so as to ensure the stable running of the BF.

With the development of Internet and Internet of Things technology,computer technology, communication technology,GPS and other information technology are widely used in the field of logistics management,and it has become a general trend to improve the operation management level of transportation enterprises. In the construction of logistics centralized control platform,the Internet and Internet of Things technology are used to achieve comprehensive information solutions specifically for transportation / distribution enterprises,which can view statistical data such as transportation management,fleet management,financial management,and customer management,effectively improving the standardized,transparent and safe operation efficiency of transportation. Intelligent logistics provides an important basis for enterprise decision-making,effectively realizes accurate transportation,reduces transportation costs and improves user experience,makes a qualitative leap in logistics management, and creates greater economic benefits.

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

Focusing on the process of hot metal ladles entering and exiting the steelmaking area,a simulation model of entry and exit of hot metal ladles in steelmaking area composed of multiple agents is constructed,which uses multi-agent technology to research on key objects such as trains and hot metal ladles. By designing a multi-agent model and considering collaborative rules among multiple agents,the model simulates the process of trains transporting hot metal ladles through a railway net- work in the steelmaking area of a steel plant. Simulation experiments have demonstrated the accuracy of process data output from the simulation and validated the effectiveness of the simulation model, which shows the model can be used to support the research and formulation of operational plans for entry and exit of hot metal ladles in steelmaking area.

In the steel manufacturing process,the ironmaking-steelmaking interface,serving as the crucial link connecting the ironmaking and steelmaking stages,is vital for the production efficiency, energy conservation and emission reduction,cost reduction,and enhanced competitiveness of steel enterprises. In recent years,the " One Ladle Technology" model,as an emerging interface technology, has received extensive attention. However,research on the technology of recommending the most suitable hot metal ladle in advance at the blast furnace is still insufficient. To address this issue,a hot metal ladle intelligent matching technology was proposed,which tracks the real-time location and status of hot metal ladles and takes into account the steel plant忆s smelting plans to predict the future location and status of the hot metal ladles over long periods. This allows for the selection of the most suitable hot metal ladles for matching,thereby enhancing the smoothness and efficiency of operations at the iron-steel interface. Verified through practical application in the Tangshan Iron and Steel New Area,this technology has significantly improved indicators such as the turnover rate of hot metal ladles,the number of hot metal ladles online,and the entering temperature of hot metal KR at the steelmaking station,providing effective technical support and optimization solutions for the production operations at the iron-steel interface.

In response to the scenario where multiple blast furnaces in Tanggang New Area supply multiple steel mills with two types of iron ladles,the rhythm of molten iron supply and demand fluctuates greatly,and manual iron separation has the problem of incomplete information acquisition of molten iron supply and demand,which can easily lead to unreasonable iron separation results. An intelligent iron separation technology with the goal of balancing molten iron supply and demand was proposed. Intelligent iron separation considers the amount of iron in transit,blast furnace tapping plan, steelmaking plant smelting operation plan,and continuous casting schedule,accurately calculates the supply and demand rhythm of molten iron,based on laminar flow operation criteria and on-site operation rules,automatically and reasonably separates iron,and dynamically adjusts it according to changes in on-site performance. Through practical application in Tanggang New Area,it has been verified that this technology has a significant improvement effect on indicators such as the running time of heavy molten iron ladle and the number of online iron ladles,meeting the needs of on-site use.

Under the situation of no great innovation in metallurgical process,the optimization of material flow and energy flow directly is facing bottlenecks. With the rapid development of intelligent big data,through the research and development of intelligent information system,the information flow and control flow of iron and steel production are optimized to realize process reengineering,particularly, through changes in control flow,integration and optimization can be influenced across positions, units,operating areas,and even departments within the factory. Under the joint action of intelligent information system and process reengineering,a three-level technical framework of " intelligent decision-intelligent centralized control-intelligent operation at site" is established,which is practice of the new generation of intelligent manufacturing technology system HCPS in steel industry,and ensuring the intelligence and precision of the entire steel production has improved efficiency,reduced manual labor intensity,and enhanced intrinsic safety. The integrated solution of intelligent manufacturing of iron and steel has been applied in iron and steel enterprises,the number of operating areas has been reduced by 60%,operating positions has been reduced by 40% ,the coke ratio of blast furnace has been reduced by 25 kg/ t,the coal ratio has been increased by 24 kg/ t,and the output has been increased by 2.2%. The optimization of information flow and control flow,makes the optimization of material flow and energy flow,which constructs a new mode of intelligent manufacturing of steel industry.

With the rapid development of manufacturing industry,more and more data management problems are gradually exposed,such as difficulties in data aggregation,lack of unified representation of data,and difficulty in data integration,which reduce data utilization and make it difficult for enterprises to analyze data effectively. Aiming at the above problems,a data space platform for the whole life cycle of steel rolling process is designed. A six-dimensional data model is proposed to describe the metadata of steel rolling process,the data entity association network model is completed by constructing the metadata model of steel rolling process,and the tasks of transformation,storage,management,query and analysis of multi-source heterogeneous data are realized. The class of rolling process metadata is determined by data analysis of the obtained hot strip rolling process data,and a six-dimensional data model is proposed on the basis of data unified modeling technology,and a rolling process metadata structure model is constructed according to the relationship among class and data. 摇 According to the data relationship and related attributes,an ontology model is constructed to complete the data entity association network model;tracking the information of data flow,data storage,data processing and display based on metadata through data consanguinity analysis,and realizing data traceability and data query. Finally,a knowledge graph of data relations and attributes for rolling process is constructed,which is convenient for users to query the association relationships of various entities in the data space more intuitively and conveniently,manage various metadata in the database,and complete the construction of the rolling process data space.

With the rapid development of the engineering machinery manufacturing industry,the heat treatment process of steel plates has higher requirements for plate shape. The quenching process is a key factor in determining the shape quality of heat-treated plates. Since steel plates experience both phase change stress and thermal stress during the quenching process,defects are prone to appear on quenched plates,affecting the quality of the final product shape. Therefore,there is an urgent need for a method that can identify the shape of steel plates to provide meaningful guidance for controlling the quenching shape of steel plates. To address the shape recognition problem,a quenching process shape recognition neural network based on an attention mechanism and lightweight design was proposed. Additionally,the plate shape image is processed using K-means to enhance defect characterization. Tests were conducted on the slab shape dataset,and the experimental results demonstrate that this lightweight network module has higher recognition accuracy mean and a single inference speed im- provement of 20-50 ms compared to other networks. 

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.

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.

Continuous annealing is a pivotal process in the production of high value-added cold-rolled strip steel. Efficiently allocating strip steel across different continuous annealing lines and determining the optimal processing sequence are essential not only for enhancing production efficiency and prod- uct quality but also for ensuring the timely delivery of final products. To address these challenges,a mixed integer programming model was established. This model accounts for the distribution and se- quencing of strip steel on parallel continuous annealing production lines,incorporating process regula- tions and production organization requirements to achieve on-time order fulfillment. Furthermore,an improved tabu search algorithm was developed to expediently solve industrial-scale problems. This al- gorithm introduces multiple neighborhoods and dynamically alternates among them to balance explo- ration and exploitation in search processes. In the small-scale instance tests,the proposed algorithm’ssolutions had an average deviation of 5. 46% from the optimal solutions. In the large-scale instance tests,the proposed algorithm’s solutions showed an average improvement of 24. 95% over the heuris- tic solutions. Based on four weeks of actual data,the test results indicate that intelligent scheduling, compared to manual scheduling,improved on-time contract delivery rates by 11. 2% and reduced the number of transitional material usages by 9. 8 times. 

To improve the accuracy of parameter prediction for the leveling process of high-strength plate,enhance production efficiency and product quality,the particle swarm optimization ( PSO) al- gorithm was introduced to optimize the weights and biases of the BP neural network,and the PSO-BP model was constructed. Through the training and testing of 550 sets of actual production data,the re- sults show that the PSO-BP model significantly outperforms the traditional method in terms of predic- tion accuracy. In the optimization process,combining the Levenberg-Marquardt (LM) algorithm with the PSO algorithm,the optimal network weights and thresholds are systematically searched for by iter- atively updating the positions and velocities of the particles. This method effectively overcomes the problems of BP neural network that is easy to fall into local optimal solutions and slow convergence speed. The experimental results show that the root mean square error (RMSE) and error rate of the PSO-BP model on the test set are improved by 0. 075 and 5. 5% ,respectively,which indicates thatthe model possesses excellent adaptability and reliability,and also shows that the results of this re- search are of great practical significance for parameter optimization in industrial production.

KR desulfurization is a typical approach for hot metal pretreatment. With the increasing demand of low sulfur steel products,achieving stable control of end sulfur content in molten iron and subsequently reducing the overall cost of desulfurization processes are of paramount importance. A critical aspect of this process is the ability to predict whether the desulfurization endpoint complies with the required standards. Therefore,a modeling method combining a sample class balance processing method based on cost-sensitive strategy and Bayesian-optimized extreme gradient boosting(XG-Boost) algorithm with a binary classification analysis method for solving end sulfur prediction problem in KR desulfurization process is proposed. Firstly,the end sulfur content of the desulfurization data is processed into conforming or non-conforming two categories based on the binary classification analysis method. The category sample weights are adjusted based on the cost-sensitive strategy to alleviate the imbalance issue for constructing the feature dataset. Then,using actual production data from a steel company,the model is trained via cross-validation with cost-sensitive strategy and Bayesian-optimized XGBoost with the optimal parameters are selected based on Macro-F1 metric to form the final desulfurization conformity prediction model for the KR process,achieving the data prediction for desulfurization conformity and non-conformity targets. Experimental results comparing with support vector machine(SVM) and back propagation neural network(BPNN) prediction models show that the proposed method can effectively deal with the imbalance issue in desulfurization data,showing good practical effects in desulfurization conformity prediction.