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.

The interference of the surface medium of seamless steel pipes and the high leakage rate of manual inspection result in the accuracy of surface defect detection of steel pipes not meeting the requirements of online detection. The insufficient depth of field and uneven grayscale of imaging detected by two-dimensional cameras result in low defect recognition rate,false detection of defects,and missed detection. Therefore,a 3D point cloud based steel pipe outer surface detection system was proposed to solve the problems of small imaging depth,low defect recognition rate,and interference defects on the steel pipe surface. Deep learning algorithms and 3D size measurement technology are applied to defect detection of seamless steel pipes. This defect detection system is used to quantify the size of defects and detect them more accurately. The on site defect recognition rate of this system can reach over 90% ,and the detection speed is fast. In addition,this system has multiple functions such as periodic defect alarm,steel pipe surface defect statistical report printing,defect grading,etc. ,making the surface defect detection system multifunctional and intelligent,which has positive significance in reducing manual labor intensity and improving the quality of seamless steel pipes.

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.

To address the challenge posed by the contradiction between the large-scale mass production in steelmaking and the demand for variety and specification diversity in subsequent processes,a study on the steelmaking flow balance problem was conducted. An optimization method based on intelligent decision-making was proposed to enhance the overall production process忆s continuity and stability. The approach involved analyzing the process rules and technical indicators that needed optimization to achieve flow balance in steelmaking. To accurately represent the problem structure,a mixed integer programming model was developed. Recognizing the need for efficient solutions,especially for large-scale problems,an enhanced tabu search algorithm was introduced. This algorithm incorporates multiple neighborhood structures and disturbance factors to prevent it from getting trapped in local optima. Numerical experiments were conducted on practical instances of varying scales,and the results indicate significant improvements in solution quality and convergence of the proposed enhanced tabusearch algorithm compared to the standard solver CPLEX and conventional tabu search algorithms. Furthermore,it outperforms manual decision method and can basically meet the needs of flow balance intelligent decision-making.

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.

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.

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.

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 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.

To address the challenges of low model accuracy and poor robustness in traditional single models for blast furnace molten iron temperature prediction,a combined model was proposed. The model integrates the intrinsic computing expressive empirical mode decomposition with adaptive noise(ICEEMDAN), kernel principal component analysis ( KPCA) and relevance vector machine
(RVM) to achieve precise and stable predictions of molten iron temperature. Firstly,ICEEMDAN is employed to decompose the time series of molten iron temperature,yielding several intrinsic mode functions. Secondly,KPCA is applied to reduce the dimensionality of the multidimensional key variables in the steel production process and extract the main features of these key variables. Lastly,RVM is utilized to predict the molten iron temperature time series based on the reduced variables,resulting in the cumulative prediction results of molten iron temperature. The results demonstrate that compared to the traditional complete ensemble empirical mode decomposition with adaptive noise ( CEEMDAN) model,the new model exhibits a 13. 0% reduction in root mean square error (RMSE) and a 10. 9% increase in training speed,enabling a better understanding of the dynamic changes in molten
iron temperature. Compared to traditional single models like RVM,the new model reduces the mean absolute error (MAE) by 2. 47 and the training time by 0. 463 s,which has the advantages of higher model accuracy and faster speed. Thus,the proposed model provides theoretical support for real-time control of blast furnace temperature,which is of practical significance in ensuring the stability of blast furnace smelting and accelerating the metallurgical process towards intelligent automation.

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.

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.

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.

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.

The final judgment of hot rolled product quality is carried out in the manufacturing execution system (MES),and the sampling method is used for inspection,which is difficult to meet the needs of customers for production process control. In order to accurately characterize the influence of process parameters on product quality,the quality process judgment and grading method provided in this paper adds the head and tail removal rule on the basis of the traditional judgment based on manual empirical rules,and then comprehensively evaluates the judgment results after sub item grading.In the actual application of the field,compared with the previous method based on manual empirical rules,this method provides more comprehensive,refined and accurate data support for MES quality final judgment,reduces the outflow rate of quality defected coiles,improves customer satisfaction,and enhances enterprise competitiveness.

During the traditional hot strip rolling production process,due to the rapid oxidation of the strip steel,the significant measurement deviation arises in the temperature measurement at finishing rolling entrances due to the obstruction and interference of the oxidized iron scale on the surface of the strip steel,which becomes an important influencing factor for the calculation and setting of disturbance rolling parameters and model self-learning regulation. A finishing rolling entry temperature prediction model was established based on machine learning neural networks,which integrates range analysis methods to determine data features and screens data based on mechanism and equipment conditions. The model predicts the rough rolling outlet temperature and calculates the temperature drop of the mechanism model to obtain the predicted value of rolling temperature,which aims at correcting the disturbance of finishing rolling entry temperature measurement caused by iron oxide scale on the surface of strip steel. Through continuous production data analysis and comparison,the temperature deviation decreases from ±9. 15 ℃ to ±5. 33 ℃. The model evaluation indicator R² has been increased from 0. 41 to 0. 84. The average value of the finishing rolling entry temperature difference of samples with sharp pits in the strip steel temperature decreases from 48. 45 to 11. 02. After performance evaluation,it's believed that the prediction model has high accuracy and strong generalization.

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.

Plate hot rolling is a typical process industry,which goes through continuous casting,heating,descaling,rolling,cooling,coiling and other technological processes in turn. Because of the large range and fast speed of temperature change in the cooling process,the cooling process has the greatest influence on the microstructure and properties of steel plate,and the final cooling temperature is a
key control parameter in the cooling process. In order to improve the accuracy of the final cooling temperature prediction,the LightGBM (light gradient boosting machine) model was used for regression prediction of the final cooling temperature. The size of plate,chemical composition and upstream and downstream process parameters are used as inputs of the model,and the final cooling temperature is used as output of the model. Bayesian optimization method is used to complete the super-parameter optimization of the model. In addition,Shapley additive explanation (SHAP) method is used to test the influence of input parameters on the predicted parameters. The results show that Bayesian optimized LightGBM (BO-LightGBM) model achieves lower error in both training set and test set,95% of the absolute error of predicted data is controlled at ±10 ℃,and the time consumption of the model is reduced by 97% compared with other ensemble learning models,the prediction accuracy and prediction efficiency of hot rolling process temperature of plate are improved.

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.

Belt conveyor is one of the most important equipment in the blast furnace delivery system.The failures of belt conveyor can lead to production losses and even safety accidents in severe cases.In order to discover the abnormalities of belt conveyor system in time,a fault monitoring system is designed for key components of the conveyor,including the motor,the reducer,the bearing of the balance weigh忆s bend pulley,the bearing of the tail pulley,etc. The hardware architecture addresses the challenges of wide distribution,numerous measuring points,and long distances in the star layout of the belt conveyor monitoring points. The use of integrated electronics piezo-electric (IEPE) interface sensors effectively reduces wiring costs while enhancing signal transmission distance and reliability.On the software side,an innovative approach is employed,involving decompose the feature of raw training data through variational mode decomposition before training the neural network. Experimental results show that,without increasing the complexity of the neural network,the software忆s accuracy in judgment has increased from 96. 5% to 99.3% ,while the false negative rate has decreased from 3.5% to 0. 7% . Additionally,training errors can converge rapidly,leading to a significant improvement in performance.

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.

Reversible cold strip rolling mills are the exclusive equipment to produce strip steel products,and maintain its constant tension and ensure the steady-state accuracy of rolling speed are effective means to solve the quality problem of strip steel shape and thickness. To improve the tracking control precision of the speed and tension system of cold strip rolling mill,a extended state observers(ESOs)-based fixed-time prescribed performance control method was proposed. The ESOs are constructed to estimate the mismatched uncertainties of the system,and the observation values are introduced into the designed controllers for compensations,which improve the tracking control accuracy of the system effectively. The cold strip rolling mill speed and tension system controllers are designed based on prescribed performance function method and fixed-time control theory,which take into account the indicators of the system such as the convergence rate,overshoot and steady-state accuracy

Sensitive steel grades with billet cracks are prone to cracking at the corners during continuous casting. In order to ensure continuous production and solve product quality problems,a digital model and quality analysis algorithm for the casting billet have been established,forming a reliable and efficient analysis system. System achieves meter level tracking and positioning through online collection of high-frequency time series data and quality data,dynamically calculating the stability of process variables,an improved self organizing map (SOM) algorithm was developed based on random forest (RF) and Fisher忆s linear discrimination analysis (FDA). The algorithm establishes a factor model through variable screening,dimensionality reduction,and stability calculation,achieving high-dimensional data compression while preserving its spatial topology structure and projecting it onto a
two鄄dimensional plane for visualization,achieving dynamic tracking of corner crack risk rating and process production path. The accuracy of model prediction is maintained at over 90% ,achieving process optimization and online monitoring. Since the system was put into use,the incidence of corner cracks in typical steel grades has decreased from 35. 3% to 8. 3% .

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.

The prediction of furnace temperature can ensure the stability of furnace temperature and billet temperature in normal production process and reduce energy consumption,which is of great significance for improving production efficiency and optimizing energy utilization. Aiming at the problems that the coupling parameters of heating furnace temperature control are numerous,the temperature control is affected by various interference factors,the change has complex nonlinear characteristics,the response speed is slow,and the inertia is large,based on the historical production data of the heating furnace system,a projection wavelet weighted twin support vector regression (PWWTSVR) model for predicting heating furnace temperature was constructed. In the process of establishing the prediction model,according to the actual production data collected from a steel mill,950 sets of data are used as the training data of the model,and 50 sets of data are used to test the accuracy of the model. The results show that,within the error tolerance of 依0. 25 益,the prediction accuracy of PWWTSVR model reaches 98% ,which is better than back propagation(BP) model and twin support vector regression (TSVR) model. Therefore,the proposed model can predict the temperature change of heating furnace more accurately,which is convenient for decision makers.

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.

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 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.

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.

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.

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.

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.

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 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.

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.