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.

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

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

The 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.

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

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

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.

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. 

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

 

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.

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

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.

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.

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

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

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

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.

As a key index to evaluate the shape quality of medium and heavy plate,the plate crown plays an important role in determining the market competitiveness of steel companies. With the devel- opment of computer technology,intelligent control has become the most focused subject in steel in- dustry. However,artificial intelligence models of predicting plate crown still lacks interpretation and are difficult to effectively guide the practice. Based on problems above,a medium and heavy plate crown prediction model based on data mining and multi-model fusion was proposed. Firstly,rolling data was projected onto spaces which have less dimensions,which solved the multi-source heteroge- neity problem of data. Secondly, samples were expanded by using the improved synthetic minority oversampling technique ( SMOTE) method based on Bootstrap,which solved the problem of low mod- el learning performance and accuracy caused by imbalance data. Then,an integrated model was pro- posed,which used predicted value of Ridge regression model as the main value and prediction of BPneural network model as the deviation. The experimental results show that mean square error (MSE) of this model is 0. 004 mm 2 ,and absolute error within 100 of model is more than 95% . On one hand, the range of prediction error is further reduced and the prediction accuracy of the medium and heavy plate crown is improved through multi-model fusion. On the other hand,the changes of feature varia- bles affect the plate crown is quantitatively obtained by reversely deducing plate crown prediction for- mula,which not only made data reflect and effectively guide the actual production,but also improved interpretability of plate crown prediction model as well. This paper provided a strong reference for control of plate crown and useful new ideas for future development and research of related fields.

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.

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

A method of unifying the coordinate system of multi-line laser cameras and the coordinate system of rotating axis based on the light cylindrical shaped target was proposed,which aims to realize the high precision dimensional measurement of the shaped workpiece. By combining the data collect- ed by three linear laser cameras and the dimensional prior information of cylindrical shaped targets,a series of nonlinear equations with the attitude of linear laser cameras as variables are established. By using RANSAC and nonlinear least squares algorithm to solve the equations,the attitude of the line laser camera is obtained and verified by practical measurement. The results show that the calibration accuracy of this method can reach 0. 043 mm,and the accuracy of measuring the train wheel with a diameter of 760 mm can reach 0. 27 mm,and the percentage accuracy can reach 0. 036% . This cali-bration method provides a new idea in the field of rotation measurement of shaped workpiece. 

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

Based on the 1 580 mm hot rolling production line of a certain factory,in response to the problem that traditional thickness models cannot accurately reflect actual thickness,an improved stochastic configuration network ( SCN) based strip thickness prediction model was proposed. Firstly, from the perspective of rolling mechanism, the reasons for thickness fluctuations in hotrolled strip products were analyzed. Secondly, based on the original SCN, a stochastic configuration network based on hunting prey optimization algorithm ( HPO-SCN) and a stochastic configuration network based on hunting prey optimization algorithm and orthogonal triangular decomposition ( HPO-QR-SCN) were proposed. Then,using onsite measurement devices,parameters of three different thick ness specifications of strip steel products were collected to form a database of strip thickness. Parame ters related to export thickness were selected as input values for the model,and the Pauta rule was used to preprocess the original rolling data. SCN,HPO-SCN, and HPO-QR-SCN prediction models were established,and their prediction results were compared. The experimental results show that the proposed HPO-QRS-CN thickness prediction model has the shortest prediction time and the highest accuracy,with a model determination coefficient of 0. 963 8. At the same time,based on the model with the best predictive performance,the influence of rolling force and roll gap on the exit thickness of the strip steel was tested,and the results were in line with actual physical laws. The asymptotic be havior of the model was tested,with a root mean square error ERMS (RMSE) of 0. 059 8,indicating good approximation performance. 

Currently , many imported temper mill flatness closed-loop feedback control systems employ a sequential control strategy , Which proVes to be ineffectiVe in controlling compound WaVes and fourth- order  flatness  deViations.    MoreoVer ,  these   systems  struggle  to  handle  the   collaboratiVe  regulation among different flatness control mechanisms.  Focusing on a six-high CvCtemper mill at a steel plant , the  existing sequential control strategy Was optimized and a flatness closed-loop feedback control strate- gy based on the  RMsprop  gradient  descent  algorithm  Was  proposed.  The proposed strategy considers three regulation methods : Work roll bending , intermediate roll bending , and CvCshifting , aiming to quickly determine the combination of regulation quantities that minimizes flatness deViations.  This en- sures the full utilization of each control mechanism s(/) potential.  simulation and comparatiVe experiments demonstrate that the  flatness  control  strategy  based  on  the  RMsprop  algorithm  significantly  enhances the control of high-order and complex flatness defects , markedly improVing flatness quality compared to the original sequential control strategy .  Furthermore , the RMsprop-based flatness control strategy Was implemented Via  CFC programming ,  addressing  program  load  rate  issues  on-site  by  integrating  the strategy into the primary  control  system  in  parallel.   Results  confirm  that  the  RMsprop-based  strategy achieVes excellent application outcomes.

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

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

Although scrap bundles have many advantages , the diversity of scrap types has an impact on smelting .  considering the complexity of the steel plant environment and the  complexity of the  current scrap type recognition technology , the use of mobile devices to realiZe the accurate recognition of scrap bundles in complex scenes is of vital significance to improve the accuracy and productivity of smelting models .   The  dataset  was  enriched  by  adding  new  pictures  of  scrap  bundle  under  complex  lighting scenes in the original dataset , and the improved hybrid network model was applied to  study the  scrap bundle recognition algorithm.  The results of the study show that the improved Edge Next hybrid model has a better performance in the recognition scenarios .  on the experimental dataset , its test accuracy is improved by 2. 81%  compared to Mobilenetv3 ; one round of training time consumed is reduced by 16 seconds compared to  the  viT model ;  and  the  model  shows  better  convergence  speed  and oscillation amplitude during the training process .  In summary , the improved Edge Next model provides solid theo-retical support for improving the intelligent recognition of scrap bundles . 

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. 

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

The surface defects of the billets directly affect the quality and performance of the steels. On-line surface detection of high-temperature continuous casting billets is very important to surface quality control of billets and steels. In this paper,an developed on-line surface defect detection method of high-temperature special-shaped continuous casting billets is introduced. Optical imaging and image recognition methods are used to detect surface defects of high-temperature special-shaped con tinuous casting billets online. The method utilizes short-wavelength blue laser illumination combined with precision narrow-band filtering to capture high-definition images of the high-temperature specialshaped continuous casting billets. Because of the complex surface and various specifications of the  special-shaped billets,two high-resolution linear CCD cameras are used to separately capture the left and right parts of the special-shaped billets. An image stitching method suitable for the surfaces of special-shaped billets was developed to merge images captured by different cameras,forming a complete image of the special-shaped continuous casting billet. Additionally,a YOLOv5-based object detection algorithm was developed,incorporating an attention mechanism to enhance model robustness and improve the accuracy of detecting surface defects in special-shaped billets. The mAP0. 5 value of crack detection is 95. 8% ,while the mAP0. 5 values of uncommon defect detection are more than 80% .

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.

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. 

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

The end-point carbon content and temperature  of the  basic  oxygen furnace  (BOF)  are  the crucial factors for  ensuring  the  seamless  operation  of  steelmaking  production.   consequently ,  a noVel end-point prediction model based  on  the  L6Vy  flights  Whale  optimization  algorithm   ( LWOA)  and ε - tWin support Vector regression  ( εTSVR)  has  been  established.   Firstly ,  the  box  plot  Was  employed  to filter the data.  Subsequently , the key factors influencing the end-point carbon content and temperature Were identified through  metallurgical  mechanism  and  spearman  correlation  analysis.  Finally ,  the  pa- rameters in the εTSVRalgorithm Were automatically optimized by utilizing LWOA, Which possesses the characteristics of simple  adjustment  parameters  and  rapid  conVergence speed.  The simulation  results indicate that the proposed LWOA-εTSVRprediction model haVe hit rates of 89%  and 93%   at the  end- point carbon content and temperature satisfying the error tolerance of  ±0 . 005%   and  ± 10 ℃ , respec- tiVely .   MeanWhile , the  double  hit rate  reaches  83% .   compared  With  the  other  three  prediction  mod- els , the proposed LWOA-εTSVRmodel demonstrates superior adVantages.  Furthermore , by setting dif- ferent error interVals , the reliability of the performance of the proposed prediction model Was also Veri- fied.   MoreoVer ,  the  proposed  prediction  model has higher  prediction accuracy than the  actual steel plant process , proViding robust technical support for steel enterprises.

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

In the process of hot strip rolling , the temperature rise  curve  of billet in reheating furnace has a significant impact on the quality of products .   Due to the bad conditions in the furnace , the actual temperature of the billet is difficult to be directly monitored , so it is necessary to establish a real-time temperature prediction model for the billet heating process .  The traditional mechanism model based on partial differential equation  (PDE) is usually difficult to meet the needs of real-time prediction due to its high computational  complexity ; The  neural  network  model  has  the  characteristics  of  low  accuracy and relying on a large number of training labels based on the actual temperature of billet , so it has not been well applied in practice.  Aprediction model of hot rolling billet temperature based on generaliZed physics-informed  (GPINN)  was  proposed.   Firstly , the  position  of the  billet  in  the  reheating  furnace was tracked according to the action signal of the walking beam , and the temperature of the whole fur- nace was  predicted  by  cubic  spline  interpolation ;  secondly ,  the  PDE describing  billet  heating  was solved by PINN, and  combined with the“ branch  net-trunk  net ”structure  of deep  operator  network (DeepoNe t ) , the different initial temperatures and specific time and space positions of billet were co- ded respectively , which effectively realiZes the real-time prediction of billet temperature in the heating process with different initial temperatures ; Finally , the effectiveness of the method was verified by the case  study of a real steel plant .  Compared with the traditional neural network method and mechanism - based method , GPINN integrates  the  advantages  of  physical  information  and  neural  network ,  better captures the heat conduction characteristics in the process of billet heating and improves the interpret- ability and prediction accuracy of the model.

In the sintering process , accurate prediction of the sintering burn-through point is essential to control the operation of the  sintering machine , as  it determines  the  quality  of the  sintered  material and the efficiency of energy use.  HoWeVer , the current common sintering burn-through point prediction models mainly focus on single-step prediction , and they tend to ignore the spatio-temporal characteris- tics of the sintering process data.  In VieWof the  multi-step prediction task requirements of the  sintering burn-through point and the  complex  spatio-temporal  characteristics  of process  data , a  multi-step  pre- diction model of the sintering burn-through point based on encoder-decoder architecture Was proposed.  The spatio-temporal encoder-task decoder architecture  effectiVely  extracts  the  spatio-temporal  features of sintering process data by stacking temporal conVolutional netWorks and spatial attention mechanism , and adopts a task-specific guidance decoder to correspond to each step of prediction in the form of in- dependent units.  The effectiVeness of the proposed method Was Verified by an actual sintering industrial process case.  This study can assist operators to obtain information about the future state of the sintering process in adVance , so as to adjust the operating parameters more accurately , reduce the quality fluc - tuation and energy Waste caused by the lag , and improVe the quality stability of the sinter.

In the automatic gauge control ( AGC) system for hot rolling,a combination of pressure AGC and monitoring AGC is often used for control. In the case of a large preset deviation,monitoring AGC is prone to causing excessive adjustment amplitude of the final stand roll gap due to its fast ad- justment and hysteresis characteristics,resulting in wave phenomenon. Based on actual rolling process conditions,a monitoring AGC control method based on multiple factors such as bending roll control and loop control was proposed. Based on the relative reduction rate of each stand,upper and lower limits of bending roll force adjustment,adaptive range of loop angle,variable monitoring proportion adjustment range,and monitoring AGC hysteresis deviation,a monitoring AGC control method for pre- cision rolling units was constructed. Practical applications have shown that the monitoring AGC con- trol strategy has achieved the expected results,increasing the speed and stability of strip head rolling, and effectively improving the overall thickness hit rate of the strip.

To avoid the problem of weakened active compensation effect for roll eccentricity caused by improper dead zone setting in the thickness control system,a dynamic dead zone method for rolling force under active compensation of roll eccentricity was designed. For the thickness control system of strip with thickness deviation dead zone,through in-depth theoretical analysis and mathematical deri- vation,the system analyzes the influence of the thickness deviation dead zone on the thickness accu- racy under the condition of active roller eccentricity compensation. A dynamic dead zone of rolling force that can follow the change of roll eccentricity state was designed,and the working principles un- der three working conditions were given. Combined with theoretical derivation,it was verified that it could further reduce the influence of roll eccentricity on plate thickness deviation when shared with active compensation for roll eccentricity. Simulation experiments were conducted using an eccentricitymodel identified based on production data,and the results show that in a thickness control system with active compensation for roll eccentricity,the designed rolling force dynamic dead zone achieves better plate thickness accuracy compared to the two cases of not using the dead zone and using the plate thickness difference dynamic dead zone. 

In response to the practical problem of materials being unable to flow out of the conical material silos under their own weight,resulting in arching,skinning and blockage,which leads to im- balanced feeding ratio and affects normal production, this article aims to ensure the continuity of process production and studies a monitoring and preventing blockage system for nonferrous metal smelting material silos based on optimized fuzzy control. From the perspective of optimizing the silo structure,a rotating scraper type anti blocking device is added inside the silo,while considering the motion characteristics of material particles and supplemented by simulation for optimization verifica- tion. The monitoring system for material flow conditions was studied,a method of using laser scanners was proposed to continuously measure the material flow to form a grid analysis plane object,and judge the degree of material blockage in the silo in real time,so as to carry out anti blockage treatment in the early stage of blockage. Optimized fuzzy control is introduced,and preventing blockage strategies is developed for silo monitoring to improve the real-time and fast performance of the system.