With the advancement of carbon peak and carbon neutrality policy,higher demands have been placed on the blast furnace ironmaking process,which constitutes a primary energy consumption segment within the iron and steel industry. Achieving intelligent sensing of key indicators,diagnosing furnace conditions,and optimizing control of operational parameters in the blast furnace ironmaking process is of paramount significance for promoting its safe,green,and low-carbon development. Firstly,taking intelligent sensing and prediction of key state indicators in blast furnaces as a starting point,providing a comprehensive review of sensing and prediction methods for three critical indicators:gas utilization rate,molten iron silicon content,and permeability index. Secondly,an analysis of the current research status of blast furnace condition monitoring and diagnosis is conducted from two perspectives:expert system and data-driven approaches. Subsequently,advancements in optimization and control of blast furnace operation parameters are reviewed from three angles:expert system and expert experience extraction,multi-objective optimization,and data-driven predictive control. Finally, by analyzing the strengths and weaknesses of various models and algorithms,the current challenges and development directions for intelligent sensing,furnace condition diagnosis,and operation optimization of blast furnaces are proposed.

In the context of the sintering blending process in steel production,challenges arise due to significant fluctuations in iron ore powder prices,the complexity of sintering raw material information, and the impact of various factors on sintering ore blending. Traditional genetic algorithm (GA) can easily fall into local optima. To address this issue,this study proposed a mathematical model based on an improved GA aimed at optimizing the sintering ore blending process to tackle the challenges posed by these influences on the cost of sintering materials. The model automatically adjusts the size of operators during the operational process based on the specific problem environment,effectively avoiding the premature convergence issue encountered by traditional GA. This ensures that the algorithm ultimately outputs a globally optimal solution when optimizing the sintering modeling. Starting with iron ore powder,the system utilizes technologies such as Python,MySQL and PyQt5 to construct an integrated sintering ore blending model. Through analysis and processing of backend data,the system ultimately generates optimized sintering ore blending solutions.

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.

By analyzing the problems existing in the existing single technology of converter smelting detection,the online quantitative evaluation technology of slag foam degree and the online prediction technology of process feature points of flue gas analysis are developed on the basis of summary to solve the problem of converter black box and realize converter smelting visualization. Based on the identification results of the detection technology, the intelligent intervention technology of powder spraying and slag suppression at the tapping port and the intelligent intervention technology of the oxygen gun are used to achieve stable control of the converter blowing process. After the application of technology,the risk of slag overflow during the converter smelting process is significantly reduced, preventing explosive splashing in the middle and later stages,and reducing the loss of metal materials. After the control of the converter smelting process is stabilized,it is beneficial to improve the end-point temperature and carbon prediction hit rate.

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.

A disk pelletizing automatic control system based on flow rate target was proposed to address the difficulties in accurately controlling pellet size and outdated production methods in the production process of pellet ore. Firstly,relying on image recognition algorithms,basic information such as particle size,distribution,and number of pellets are extracted from actual production process pellet images. Secondly,the system analyzes and calculates the received pellet information to obtain the current production status and particle size change trend of the pellets. Finally,the system establishes a flow target setting value based on the trend of pellet particle size change and production status,and adjusts the valve opening adjustment cycle appropriately according to the pellet production status to adapt to production. The system was actually applied to the control of a disk pelletizer in a domestic steel plant. The practical results show that in terms of the difference between the average particle size and the target particle size,the automatic control mode reduces the error by 32. 54% compared to traditional manual control mode. Moreover,under different pelletizing conditions,the proposed control system exhibits stable fluctuations in flow during actual production. Compared to manual control mode,the root mean square error of pellet size is reduced by 6. 59% ,which has good stability. It has a positive effect on improving the production efficiency and qualification rate of pellets,and reducing manual labor intensity.

The smooth condition of the blast furnace is important for the production and quality of the hot metal. The stability of the slag crust indicates the stability of the conditions in the blast furnace, and the temperature in the cooling stave can describe the stability of the slag crust. For predicting the conditions of the blast furnace according to the dynamic features of the temperature in the cooling stave,this study presented an intelligent method for predicting the conditions of the blast furnace based on the information granules of the temperature in the cooling stave. Firstly,the Spearman correlation analysis method is employed to select the parameters that affect the dynamic features of the temperature in the cooling stave. Secondly,the information granulation method is used to extract the dynamic features of the selected parameters and represent the data in a granular form. Then,the prediction model of the information granule of temperature in the cooling stave is built based on the support vector regression with the inputs of information granules of the selected parameters,realizing the prediction of the temperature in the cooling stave. Finally,based on the predicted information granules,the conditions of the blast furnace can be recognized using a condition prediction method. Experiments conducted using actual steel enterprise data show that the presented method can predict the conditions in the blast furnace and provides powerful guidance for operators to make a proper burden distribution decision-making strategy.

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.

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.

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.

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 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.

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.

Blast furnace permeability index is an important index to reflect the indirect reduction degree of charge and furnace condition,which is affected by blast furnace operations in multiple time scales. The existing research analysis,modeling and prediction of the development trend of the permeability index are mostly based on the same time-scale and the prediction step is short,so the prediction results are difficult to guide the on-site judgment. Therefore,this paper proposed a multi-step prediction model of blast furnace permeability index based on multi-time scale. Firstly,the time domain characteristics of the influence of blast furnace operations on the permeability index on multi-time scale are calculated through mechanism and data analysis,and the multi-time scale effects of different operations on the permeability index in different time scales are analyzed in combination with the frequency domain characteristics. Then,according to the characteristics of blast furnace operation affecting the development of permeability index in different time scales,a single-step prediction model based on support vector machine is established. Finally,a multi-step prediction model of permeability index based on recursive strategy is established on the basis of single-step prediction model. The experimental results show that this method can effectively predict the future development trend of permeability index and is convenient for on-site decision-making.

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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