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. 

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.

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 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.

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.

Hoist is the key equipment of dry quenching coke charging system,which has large load,frequent start and stop,unstable working condition,complex gear box structure and large reduction ratio.Therefore,the continuity and reliability of the hoist has always been the focus of production and equipment managers.An artificial intelligence algorithm based on multi-dimensional perception and neural network was proposed by the paper to identify the instantaneous fault of hoist equipment in real-time and monitor the hoist life cycle.By extracting and identifying the acoustic and vibration characteristics of the hoist gear box,combined with convolutional neural network (CNN) and long-short term memory (LSTM) neural network algorithm,extracting vibration space characteristics and time operating characteristics,judge and predict the operating state of equipment,so as to achieve real-time monitoring of equipment operating state,fault diagnosis and predictive maintenance.Engineering application practice shows that the method can effectively achieve 95.3% accuracy of fault identification,and can quantify the specific stage of the hoist′s operation life cycle,so as to remind maintenance personnel to respond to equipment faults in time,achieve predictive maintenance of hoist,reduce equipment failure rate,reduce maintenance costs,achieve continuous safe production,promote equipment professional management and smart factory construction.

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.

In order to ensure the effective use of rolling equipment capacity,taking the mill motor load data of a bar production line in a steel plant as the object of study,PyTorch was used to build a prediction model based on long short term memory ( LSTM) network,define the initial grid structure parameters of the model,and select the cell structure activation function. For the problem of model hyper-parameters selection,the adaptive moment estimation ( Adam) algorithm was used to optimize the parameters,iteratively reduce the loss value and improve the prediction accuracy of the model.Through the experimental design,the mill load data for two bar sizes were used to verify,and that the mean square error SME is reduced by 3. 28 and 1. 76,respectively,compared with the unoptimized load prediction models. The results show that the established models have better prediction effect and higher stability.

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 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 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.

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.

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.

In iron and steel enterprises,refined energy management and intelligent scheduling operations are important technical means to achieve efficient energy utilization. Based on a detailed analysis of the integrated energy system of iron and steel enterprises and the integration of advanced technologies from other industries,the design of an intelligent implementation path for the scheduling and operation of the integrated energy system was completed. Based on the theory of metallurgical process network,a networked modeling of integrated energy systems was conducted. A hybrid reasoning method based on serialized case sections was proposed,including the definition,management,search,and design of hybrid reasoning engines. A practical intelligent driving engine was constructed. Finally,according to the actual needs of a steel enterprise's integrated energy system scheduling operation,an intelligent scheduling decision support system was designed and developed,which achieved good economic benefits in practical applications and provided beneficial practice for the development of intelligent energy in iron and steel enterprises. 

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.

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. 

In the hot rolling strip process,the coiling temperature is an important process parameter and the main control objective,which can to some extent determine the strip steel microstructure,and affect the mechanical properties and usability of the product.To improve the accuracy of hot strip coiling temperature,based on the actual production data of a hot strip rolling line,a data driven coiling temperature prediction model for hot strip rolling was established using random forest (RF) algorithm.The Bayesian optimization algorithm is used to determine the optimal hyper-parameter of the RF model,and the grid search is used to determine the Bayesian algorithm hyper-parameters.At the same time,an decision tree model (DT) optimized by Bayesian optimization algorithm,support vector regression model (SVR) and mechanism model based on classical heat transfer theory are used for comparison and verification.The model testing results show that over 97% of the prediction results of the RF model have a prediction error within -10-10 ℃ for sample points.Compared to on-site models,it can predict the coiling temperature and further improve the control accuracy of coiling temperature.

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.

In the process of blast furnace smelting,under the influence of dynamic changes of working conditions and complex factors at the production site,the fluctuation of differential pressure has a certain time lag,and it is still difficult to realize the accurate forecast of differential pressure based on real-time online data. To address this problem,based on the actual smelting process of the blast furnace,which has the characteristics of multivariate and time-dependent time series data,the volatility analysis and decision tree feature importance analysis methods that can effectively reflect the degree of fluctuation of the production process parameters are adopted,and different subsets of the model input features are selected,so as to establish the temporal pressure difference prediction model based on the long short-term memory (LSTM). The comparison results of the two methods show that the LSTM prediction model based on volatility analysis to determine the input features has an improved hit rate of 0. 761% within the prediction error range[ - 5, + 5] kPa. The feature selection method based on the volatility analysis of production parameters can effectively improve the prediction accuracy of the LSTM model,and verify the validity of the input feature selection method of the temporal differential pressure prediction model under the condition of oxygen-enriched blast furnace.

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

It is a big dream of ironmaking workers to achieve the intelligent control of blast furnace. In recent 30 years,a large number of developments and practices have been implemented in many blast furnaces. Due to the complexity of blast furnace process control,it is difficult to obtain a good intelligent control system for realizing stable performance of blast furnace in despite of good performance for intelligent control system with single function. Some problems,such as range of study,contents,operational requirement, key issues to be addressed, have been deliberated based on the requirements of high efficiency smelting in blast furnace. Some proposals,such as emphasizing on the research of blast furnace with features of " multivariate, large time delay, nonlinear, full time and space" ,building high level data platform,using the simplest and most practical technologies,paying attention to the development and use of platform tools,constructing reasonable intelligent control development mode,simulating blast furnace experts,accurate positioning and priority to the easy,setting up a special software development team for blast furnace whole campaign optimization,have been proposed,a good object for upgrading one generation in 3 - 5 years will be achieved.

The preparation of wheel production plans is one of the core tasks in the management of transit materials production plans. A reasonable production plan helps regulate the production pace of each order,ensuring smooth and orderly wheel production. Based on the scheduling of transit materials wheel production plans,taking into account factors such as rolling,heat treatment,outsourcing
rough machining,precision machining,and inspection capabilities,as well as the logistics cycle between insulation covers, tool switching, and sequential operations. By establishing a mathematical model and employing a multi-objective combinatorial optimization algorithm (MOCOA),intelligent scheduling of wheel plans is achieved,effectively addressing the production planning and scheduling issues arising from small batch sizes,multiple specifications and personalized requirements in Masteels transit materials wheel production plans. The results of the system operation demonstrate that by repeatedly balancing the contractual delivery dates of wheel orders,unit capacity,material inventory,process paths,logistics cycles,equipment utilization rate,availability time of production lines and contract manufactory capabilities throughout the entire factory. The system generates interlinked production plans for each process unit,implementing a single plan creation process,which was previously taking two days,has been shortened to within 2 h,effectively enhancing the efficiency of wheel production scheduling. The reduction has led to a 20% decrease in wheel manufacturing lead times and a 20% reduction in inventory occupancy,aligning with the smart factory production requirements aimed at cost reduction and efficiency enhancement.

During the continuous casting process,the instantaneous abnormal mold level fluctuation has great detrimental effects on slab quality,thus the mold level fluctuation is a key parameter for continuous casting process of high-quality steel.In the present study,the slab continuous casting process data for low carbon steel,medium carbon steel,hypo-peritectic steel and peritectic steel were collected,the fast Fourier transform (FFT) and continuous wavelet transform (CWT) were used to analyze the data characteristics,and then the influence of process parameters on the instantaneous abnormal mold level fluctuation was studied.The results of FFT analysis indicate that bulging has no significant effect on the instantaneous abnormal mold level fluctuation.The time-frequency characteristics of instantaneous abnormal mold level fluctuation and stopper-rod position were analyzed by CWT,and the results show that under different steel grades and casting speeds,before the instantaneous abnormal mold level fluctuation occurrence,the CWT coefficients in high-frequency region of stopper-rod position shows a linear increase trend.Therefore,by conducting CWT analysis of the high-frequency region of the stopper-rod position,it is possible to predict the instantaneous abnormal　mold level fluctuation.

Iron and steel industry plays an important role in the national industrial economy. As an important link in steel production,BOF steelmaking directly determines the quality of steel production. BOF steelmaking is a multi-element,multi-phase and high-temperature physical and chemical reaction process with many influencing factors and complicated process. It is always a difficult problem to be solved in metallurgical industry to realize accurate control of BOF steelmaking endpoint.The goal is to improve the hit rate of carbon content and temperature at the endpoint of BOF steelmaking. Aiming at the lack of prediction performance of the traditional global model,it is difficult to solve the problem of multiple working conditions,a local model integration online monitoring method based on just-in-time learning ( JITL) is proposed. Under the framework of JITL, using similarity measurement methods under different criteria,select corresponding sub-sample sets,build local regression models respectively,and finally output the prediction results of each local model through ensemble learning. In the verification of actual converter steelmaking data,using the proposed method,the prediction accuracy of endpoint temperature in ± 15 ℃ is 92. 7% and the prediction accuracy of endpoint carbon mass fraction in ± 0. 02% is 95. 7% ,which can provide reference for endpoint control and other operations in actual production process.

The blast furnace gas flow can characterize the operating state of a blast furnace,while the cross-temperature measurement reflects the distribution state of the blast furnace gas flow. This paper proposed a dynamic modeling method for multivariate correlation of blast furnace cross-temperature measurement based on periodic registration and seasonal and trend decomposition using loess (STL),which can improve the accurate estimation of gas flow. Firstly,the periodic partitioning and periodic registration among multiple variables are performed by sliding window,which is helpful to achieve precise multivariate correlation. Next,the RobustSTL is introduced to retain key information, extract global changes,and enhance the accuracy of the online estimation model. Then,the gated recurrent unit (GRU) is employed to establish a multi-step prediction model for the multivariate correlation of cross-temperature measurement. Finally, experimental verification is conducted using the cross-temperature measurement dataset, and the results show that the proposed predictive model achieves significant performance improvement.

Steel manufacturing is a typical process industry. Multiple uncertainties in its complicated production process bring huge challenges for production scheduling. Facing the disturbances caused by uncertain events,how to design dynamic scheduling strategies to achieve fast response is a key and practical issue to be solved in iron and steel enterprises. Existing work proposes many dynamic scheduling methods based on Petri net,heuristic algorithms,mathematical programming,dynamic constraint satisfaction,human-computer interaction, case-based reasoning,robust and fuzzy optimization to solve various dynamic scheduling problems in iron and steel production scheduling scenarios,such as ironmaking,steelmaking-continuous casting, hot rolling, and steelmaking-continuous casting-hot rolling processes. An overview of dynamic scheduling in iron and steel production systems is given. The main process of iron and steel production and its dynamic disturbance factors are introduced. Existing research on dynamic scheduling problems and their solution methods in iron and steel production systems are summarized. The desired demands of the iron and steel industry and the limitations of existing work are analyzed. Finally,the key scientific issues to be solved and future research directions to be explored are indicated, which include knowledge and data-driven high-performance dynamic scheduling algorithms, multi-process collaboration dynamic scheduling problems and their solution methods,and configurable decision support systems.

The production process of BF involves complex gas,solid and liquid multiphase reactions and is affected by the interaction of multiple factors such as raw fuel conditions,operating smelting parameters and BF忆s state. It is difficult to accurately control two control targets,namely rate of decline of blast furnace charge and pig iron忆s silicon content,which have important influence on the stability and technical indexes of smelting process. In order to comprehensively quantify the complex relationship between many factors that affect its control and form the standardization and process of operation regulation,and to solve the common problems of disunity of judgment and regulation standards and low control accuracy caused by individual differences of blast furnace three shift production. Based on ironmaking theory,combined with practical experience and aided by computer information means. An operation model was innovatively designed to control the silicon and rate of decline of BF忆s charge,which can quantify and modular accurately control the rate,silicon content of pig iron and slag iron composition in the smelting process,and has the functions of data collection,quantitative evaluation,check and optimization. The operational model effectively improves the uniformity and systematization of "oxygen control rate,coal control temperature" regulation method in blast furnace
operation,and meets the requirements of immediacy,procession and convenience in daily BF忆s production. The practice of BF applied to specific volume and raw fuel condition shows that the target value of rate of decline of BF忆s charge and silicon content of molten iron can be achieved in 0-3 times of oxygen adjustment and 0-3 times of coal adjustment in each shift in the smelting process. The deviation rate between the daily pre-controlled silicon content and the actual silicon content is only 0.006% points. Calculated based on material speed,the daily deviation rate between theoretical and actual production is only 0. 133% . The model lays a foundation for improving furnace condition and reducing fuel ratio and smelting cost.

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.

Given the complex production process of traditional converter steelmaking,the difficulty of high-temperature molten steel detection and manual operation. An multi-task parallel architecture converter steelmaking process control system is developed to improve the production efficiency of converter steelmaking. The system includes six functional modules,namely process tracking module,human machine interface (HMI) module,data communication module,model calculation module,data management module and data validity judgment module. It realizes the automatic steelmaking process without human intervention in the converter steelmaking process. The problems of large error in data detection and excessive dependence on manual experience are solved. The system adopts multi-process structure,and the new mode of one task one process is used in the process,which reduces the
coupling between the function modules. The practical application shows that the system is simple in operation,strong in stability,and the interaction is good.

Aiming at the rescheduling problem after the failure of a smelting equipment in the steelmaking-continuous casting production process,in order to ensure the stability of production and reduce the variation of the rescheduling scheme compared with the initial scheduling scheme,a rescheduling model was proposed with the objective of minimizing the weighted variance of the start time,operation cycle and equipment assignment,which was established by mathematical planning method.By analyzing the production operation mode and production process of a steel plant,a rescheduling algorithm was proposed,which consists of an equipment assignment algorithm based on the "furnace-caster coordinating" scheduling strategy rule and a time adjustment algorithm based on the process flexible buffer control strategy rule.Taking the converter equipment failures or refining furnace equipment failures that often occur during the actual production of steelmaking and continuous casting in a large domestic steel plant as simulation samples,the experimental results indicate that the total weighted differences between the start time,operation cycle and equipment assignment before and after scheduling were 0.29,1.43,and 1.21,respectively,which can effectively maintain the consistency between the rescheduling plan and the initial scheduling scheme,and ensure the stability of production.And the solution time was less than 0.6 s,which can quickly provide better solutions to the rescheduling problem after smelting equipment failures.In the actual production process with frequent failures of smelting equipment,the orderly,stable,and efficient operation of steelmaking-continuous casting can be ensured.

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.

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.

Blast furnace radar burden line extraction currently commonly used neural network plus energy center of gravity method of two-step extraction of burden line method,there is a mixture of network model and mechanism model step-by-step computation,susceptible to the influence of the special environment of strong noise problem. In this paper,an improved BS-TransUNet algorithm for blast furnace burden line extraction based on semantic segmentation was proposed. Firstly,to address the problems of periodic morphology and particle size variation of blast furnace burden surface and signal to noise ratio attenuation,the atrous spatial pyramid pooling (ASPP) module is introduced between convolution neural network (CNN) and Transformer modules to obtain fine-grained features of the burden surface. Then,the coordinate attention (CA) module is integrated after each up-sampling to filter out the background noise more comprehensively and inhibit the extraction of ineffective high-frequency texture features. Finally,the jump link is replaced with the BiFusion module to further improve the segmentation performance. The experimental results show that the improved algorithm improves the mean intersection over union (MIoU) and F1 scores by 1. 77% and 1. 46% ,respectively,the mean pixel accuracy (MPA) by 1. 97% on the blast furnace radar burden surface dataset, and the F1 score can reach 86. 18% . Compared to the conventional two-step extraction of burden line method,the one-step method with end-to-end split burden line in the harsh environment of a blast furnace provides improved accuracy and stability of burden line acquisition.

The blast furnace smelting is carried out in a completely closed and high-pressure environment,it is impossible to observe the internal operating conditions of the blast furnace and the shape of the material surface,making it difficult to accurately judge the furnace condition,and the utilization rate of the material surface data resources is not high,which affects the operators adjustment of the distribution system of the furnace top. In order to improve the data utilization rate and improve the quality and accuracy of point cloud data,a double-sided filter was proposed to preprocess the three dimensional point cloud data of the original blast material surface in the paper. Poisson reconstruction algorithm is used to reconstruct the filtered point cloud data,build a multi-scale feature coding network,and repair the missing 3D point cloud material surface. Poisson surface reconstruction can retain the detail characteristics of the surface and smooth the surface,which provides an important basis for quickly judging the type of the surface. By extracting the point cloud feature information of different scales,3D point cloud feature enhancement and multi-level expression are realized. Experiments show that the proposed method has small error in point cloud missing prediction and complete shape of point cloud,which provides a fast,efficient and practical solution for processing point cloud data with missing material surface.

The permeability index of blast furnace is an important parameter that can quickly,intuitively,and comprehensively reflect the condition of the blast furnace. Accurately predicting the permeability index of blast furnaces can detect and avoid abnormal furnace conditions such as pipeline, suspended material,collapse,and gas loss as early as possible (about 10 min in advance). This article proposes a blast furnace permeability index prediction model that combines kernel principal component analysis ( KPCA), convolutional neural network ( CNN), and long short-term memory (LSTM). Firstly,KPCA is used to reduce the dimensionality of the original high-dimensional input variables,followed by CNN to capture the features of the data,and finally,LSTM is used to predict
the permeability index of the blast furnace. The results show that the constructed KPCA-CNN-LSTM blast furnace permeability index prediction model significantly reduces prediction errors and improves prediction accuracy compared to before dimensionality reduction. It is beneficial for blast furnace operators to quickly grasp the instantaneous changes in furnace conditions and take effective measures to restore smooth operation of the blast furnace.

With the development of the steel industry,the steel manufacturing mode is gradually shifting from manual production to unmanned and intelligent production. At present,the working intensity of the converter steelmaking process is high,the equipment operation is cumbersome,and the working environment after the furnace is relatively harsh. With the goal of one click steelmaking and safe steelmaking,the converter steelmaking system was improved and optimized to establish an intelligent steelmaking system. For the transformation of the automation system,firstly increase the setting of the steel tapping curve and improve the functional design of the alloy chute. Then,add security chain programs,ladle car detection devices,and converter tilting detection devices to ensure the safety of the steelmaking process. At the same time,machine vision assisted system and L2 models are used to monitor and calibrate the steel tapping process,ensuring that the molten steel and slag do not overflow. Through practical application in two 200 t converters of HBIS Tangsteel Company,it has been verified that the system promotes standardized production of converter steelmaking,reduces labor intensity of workers,and ensures security and stability of tapping process.