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

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.

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

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

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

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

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

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

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 ”.

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.

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, this paper proposes a performance optimization method for hot rolled strip steel that integrates machine learning performance prediction model and Shapley additive explanation (SHAP) interpretation framework. This method first uses 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 MSVR, 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 hot metal pretreatment desulfurization process in modern converter steelmaking process can improve the efficiency of impurity removal,reduce the burden of converter blowing,and shorten smelting time.It is a necessary process for smelting variety steel and clean steel.The pretreatment process of molten iron undergoes complex high-temperature physical and chemical reactions,which is a black box process,making precise control of the smelting process very difficult.Establishing an accurate process control model is the core of achieving precise control of the hot metal pretreatment process,which is of great significance for enterprises to reduce steel production costs,promote digital and green transformation.This paper summarizes the modeling principles,characteristic and research progress of various models,such as mechanism model,statistical regression model,expert system and machine learning model,established by domestic and foreign researchers for hot metal pretreatment desulfurization process.Based on the different uses of the model,the development process of application in practice and prospects of the hot metal pretreatment desulfurization model were proposed,focusing on the prediction of endpoint sulfur content and desulfurization rate,prediction and optimization of smelting process parameters,and prediction of desulfurization agent consumption and utilization rate.

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.

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 .

Byproduct gas,steam and electricity are important secondary energy source for the iron and steel production process. With the advancement of the "carbon peak" and " carbon neutrality" policies,the iron and steel industry has a growing need for fine-grained management of multi-medium energy systems,including by-product gas,steam and electricity,in order to control production costs and reduce energy consumption. However,due to the complexity and decentralised nature of the generation and consumption of each energy medium,it is vital to establish a comprehensive and rational scheduling model. In addition,in recent years,the development of renewable energy technologies such as photovoltaic and wind power has provided new ways for the iron and steel industry to make a low-carbon energy transition. Firstly,the multi-medium energy system was introduced,involving buffer equipment such as gas holders and boilers. Secondly,the research characteristics and results of each model were analyzed and summarized through the classification of energy scheduling models. Finally, the characteristics of the energy system in the iron and steel industry after the introduction of renewable energy was analyzed,and the concept of multi-energy microgrid in the iron and steel industry was introduced,providing ideas for subsequent research to promote energy-saving and low-carbon development in the iron and steel industry.

At present, a new wave of technological revolution is emerging globally, represented by industrial technologies such as artificial intelligence, big data and industry 4.0. The traditional chemical plant control model can no longer meet the needs of digital manufacturing. Typical problems such as complex production processes and difficulty in collaborative operations in the steel industry have gradually emerged. Enterprises also have uneven information levels and serious information island phenomena. In this regard, the factory and production line based on emerging technologies such as digital twin technology, internet of things, machine learning and video intelligent recognition, accesses video images, sensor monitoring data, system control data, external drawing information and management systems, and the status of production line equipment and materials in real time were established, visual linkage of data information within the production line was displayed, and information islands were eliminated. At the same time, artificial intelligence algorithms were used to merge and splice the complex and scattered video images, and splice the production line monitoring images. For large-scale key areas, real-time monitoring was achieved in the form of "one picture".

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.

The prediction of sulfur content of liquid steel in ladle furnace (LF) is of great significance for the precise control of the composition of refined molten steel and the improvement of product quality.In view of the complex mechanism,multi-variable,and nonlinear characteristics of LF,a prediction method of sulfur content in LF was proposed based on autoencoder-back propagation neural network (AE-BPNN).Firstly,the effects of noise and missing values are eliminated through AE network for data reduction and feature extraction.Then,the BPNN is used for predicting the sulfur content of liquid steel in LF.The actual on-site data validation show that the E_RMS,E_MA and the correlation coefficient R² are respectively up to 1.403,1.083 and 0.824,which has good prediction performance.

The converter heat loss rate is one of the important parameters that affect the prediction accuracy of material consumption.Based on the calculation of heat loss rate,the historical production data of 1 900 heats of a 150 t converter in a steel plant was applied to accurately predict the converter heat loss rate using machine learning algorithm.The prediction results show that light gradient boosting machine (LightGBM) algorithm has the highest prediction accuracy compared with support vector regression (SVR) and random forest (RF) algorithms.Considering the influence of the last furnace,after adding the final smelting temperature variable of the last furnace,the determination coefficient R² of LightGBM algorithm increases from 0.89 to 0.93,and within the range of ±0.005 and ±0.01,the prediction hit rate of heat loss rate increased from 85%,89% to 90%,93% respectively.In addition,the prediction accuracy of the model can be further improved by optimizing the internal parameters of the algorithm.For the LightGBM algorithm,the determination coefficient R² and root mean square error (RMSE) further reach 0.94 and 0.009,and the prediction hit rate of the heat loss rate further increases to 91% and 94% within the range of ±0.005 and ±0.01.Based on historical data of converter smelting,intelligent algorithms can be used to predict the heat loss rate of the converter,which can provide support for the prediction of the material consumption of the converter.

The traditional techniques of microstructure and properties prediction and process optimization ( physical metallurgy model and neural network model) can not give attention to the high precision of steel material properties prediction and the basic laws of physical metallurgy,so the practical guidance for steel production is limited. Digital twin technology is applied to predict microstructure and mechanical properties as well as optimization of hot rolling process of steels after industrial big data are related and cleaned. Under condition of physical metallurgy regular correctly,high accuracy prediction of mechanical properties is realized through machine learning of effective industrial data.Taking rolling force prediction as an example,the prediction method based on physical metallurgy and machine learning was introduced. Smart hot rolling is realized through targeted design of production process using artificial intelligence algorithm. Smart hot rolling is mainly reflected in the customized production of hot rolled steel,the design of reducing alloy,the merging of steel grades,and the stability control of mechanical properties,etc. Finally,the achievements in the field of microstructure and mechanical properties prediction and optimization of hot rolling process of steels were introduced.

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. 

Continuous casting slabs are raw materials of steel production.The defect of slab will lead to quality defect of final steel product.The low and high frequency data collected from continuous casting process on site were studied.Cleaning methods of complex process industrial data and feature extraction methods of high frequency industrial data were proposed.Based on machine learning theory,four kinds of slab surface defect prediction models,namely classification and regression tree (CART),AdaBoost,random forest (RF),and optimal classification tree (OCT) were established.Feature selection were carried out using Relief and RF model.The prediction accuracy of different models was compared and analyzed through a large number of experiments.The experimental results show that the RF model gives the best prediction accuracy.The top 10 features,such as liquidus temperature,tundish (TD) lower limit temperature and TD target temperature,which play a key role in slab surface defects are found out.The method in this paper can be extended to industrial data analysis and utilization modeling in other scenarios,which has important reference value for using industrial data to improve product quality.

Based on the continuous development of industrial smart manufacturing and the wide application of cloud-edge-end and digital twin technologies,the digital twin structure and key technologies for hot rolling production based on cloud-edge-end were proposed. Among them,the three-layer architecture in the digital twin structure realizes the digital management and intelligent control of hot rolling production and equipment. The key technology of hot rolling digital twin is the data communication between equipment based on 5G + edge computing technology. At the same time,the cloud-edge-end technology of data processing,storage and computing modeling is combined to solve the problems of low speed and poor privacy security of direct communication between physical entity and cloud. Finally,the hot rolling digital twin system is gradually constructed.

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.

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.

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

 

The content of FeO in the sintering process is an important reference index affecting the performance of sintering ore.Real-time observation and monitoring of changes in FeO content can reduce sintering energy consumption and improve sintering effect.Aiming at the current situation that the means of real-time observation of FeO content in enterprises is relatively single,the prediction model of FeO content in sinter based on bi-directional long short-term memory (BiLSTM) neural network algorithm was studied.The source of the data is partial process data generated in 2021 by the six-type sintering machine of Panzhihua Iron and Steel Co.,Ltd.After filtering,optimizing and other data processing,the BiLSTM neural network is selected for training,parameter adjustment,and combined with the on-site sintering process of the enterprise,which improves the prediction accuracy of the model.The accuracy rate basically realized the prediction of sintered FeO.The test results show that within the allowable range of enterprise error,the accuracy rate reaches 90.2%,so it can give　effective opinions on sinter production in the enterprise.

With the development of information technology,big data,internet of things and computing power,the third wave of artificial intelligence has begun to appear in academic research and industrial applications. The iron and steel industry is a complex process industry,where the internal production process is complex with many influencing factors,thus artificial intelligence in the iron and steel industry has a high application value. With the promotion and implementation of the national policy of integration of informatization and industrialization,China's iron and steel industry has gradually improved its informatization degree and level. This lays a solid foundation for the implementation of artificial intelligence technology in iron and steel industry. This paper first explored the research fields of artificial intelligence technology,including expert system,neural network,intelligent robot,machine learning,intelligent optimization,etc.,and then studied the main application scenarios and research results of these technologies in the field of iron and steel. Finally,this paper looked forward to the artificial intelligence technology from production optimization to strategic management to help the high-quality development of iron and steel industry.

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 .

To address the problems of large subjective deviations and permeability attenuation in traditional argon blowing control, the relationship between the dynamic characteristics of the exposed area of the steel liquid surface and the argon stirring intensity was analyzed. An indirect quantification method of argon intensity based on machine vision was studied. This method captures the dynamic characteristics of the steel liquid surface in real time, calculates the liquid surface exposure rate (SER) using artificial intelligence large model technology, and establishes an argon blowing automatic control strategy based on the relationship between the real-time SER and the ideal SER, thus realizing the precise regulation of the argon flow rate. The research results show that this method overcomes the limitations of traditional flow preset and manual adjustment, effectively improves the control accuracy of the stirring intensity, and was applied and verified in the actual VD refining production system, proving its industrial feasibility and engineering promotion value, and providing an engineering solution for the intelligent upgrade of the refining process.

The accurate prediction of the Linz Donawitz converter gas (LDG) holder level in iron and steel enterprises can provide an important basis for gas system scheduling.Given the impact of LDG recovery,scheduling workers are particularly concerned about the problem of exceeding the upper limit of the holder level.Based on a large amount of on-site actual data,a cost-sensitive learning-based support vector machine (SVM) method for predicting the level of LDG holder was proposed,which can improve the prediction accuracy of the situation when the level exceeds the upper limit.This method takes the recovery and consumption flow of LDG as inputs,and the future holder level value as output.By using the KKT equation,the original constraint conditions are transformed into equation constraints,different costs are set for the gas holder level exceeding the limit and false alarms.Finally,by minimizing the model′s false alarm error,the original prediction problem is transformed into a series of linear equations and solved.A simulation experiment was conducted on data　from a domestic steel plant,and the results showed that the proposed method can effectively reduce the false alarm rate of converter gas holder level exceeding the limit to 0.16%,providing more rapid and effective guidance for scheduling workers to develop reasonable scheduling strategies.

It is of great significance to clarify the phase distribution and lining erosion characteristics of blast furnace by the damage investigation,and the information on various phase parameters of the damage-investigation samples can be obtained rapidly and cost-effectively through the image-processing technology.The coke information of the residual iron section in the hearth,the three-dimensional structure of slag-iron-coke and the damage degree of the refractories were characterized quantitatively based on the damage-investigation work of a large blast furnace in China combining Photoshop (PS),Image-Pro Plus (IPP) software and industrial CT equipment.The process of image acquisition and binarization processing was firstly introduced,as well as "calculation method of pixel proportion" and the trend distribution of deadman voidage by grid partitioning were then characterized.At the same time,the correlation between two and three dimensional voidage was verified through detecting　the three-dimensional phase distribution and voxel ratio of slag-iron-coke in the deadman.Furthermore,the correction coefficient C was introduced based on the basis of "coke size calculated by equivalent circle" which reduced the influence of coke irregular morphology on particle size.The distribution,size,and depth of the refractory material′s cracks in surface macroscopic and internal microscopic were intuitively characterized by the binary images,and the number,volume,and porosity of pores in refractory materials can be also characterized by the three-dimensional structure,then determined the damage situation of refractory materials in the hearth quantitatively.

The precise control of the end-point carbon content and temperature of Consteel electric arc furnace is extremely important to ensure the high-quality liquid steel. Different machine learning models including prediction models and hyper-parameter optimization models were used to fit the actual production data from the smelting process of Consteel electric arc furnace in a domestic steel plant. Based on the prediction results of machine learning models,the end-point carbon content and temperature prediction model of the Consteel electric arc furnace based on Bayesian optimization algorithm (BOA) and gradient boosting decision tree (GBDT) was established. Through the verification by using the collected data,this model achieves better prediction performance for the end-point carbon content and temperature of Consteel electric arc furnace smelting,which can provide certain guidance for the actual production process.

Under the dual influence of environmental protection and de-capacity,Chinese steel has begun to transform into a high-quality,intelligent and green production model. The traditional concept of blast furnace smelting with high energy consumption and high pollution is no longer applicable to the development of the " 14th Five-Year Plan" direction. With the rise of big data and artificial intelligence technology,the new generation of iron and steel industry is moving towards green manufacturing under the impetus of intelligent manufacturing,and it has become a major trend to establish various prediction models by analyzing the data accumulated by iron and steel enterprises for many years. This paper firstly took the blast furnace intelligent transformation as the research background,and the prediction model of smelting index and monitoring system of smelting process of the current blast furnace were introduced in a simple to complex manner. Then,the importance of data processing and expert decision optimization strategy was analyzed,and the current construction of blast furnace big data cloud platforms in various enterprises was briefly expounded. Finally,the corresponding conclusions and prospects were made for the intelligent transformation of blast furnace.

In order to increase the production level of intelligence at the material yard of domestic steel companies,the function and application in the material yard based on the laser scanning was analyzed. The 3D digitalized material yard system was developed. According to the position of the device,the laser scanning was used to obtain the point cloud data of stack in real time and the 3D reconstruction and rendering was made. It was developed the function of automatic division and the inventory. The real-time flow control and edge detection of stacker-reclaimer was implemented by laser scanning. With the application of the system in the projects,the functions of the system were verified and remarkable application effect was obtained.

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.

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.