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.
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.
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 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 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.
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 blast furnace smelting is carried out in a completely closed and high-pressure environment,it is impossible to observe the internal operating conditions of the blast furnace and the shape of the material surface,making it difficult to accurately judge the furnace condition,and the utilization rate of the material surface data resources is not high,which affects the operators adjustment of the distribution system of the furnace top. In order to improve the data utilization rate and improve the quality and accuracy of point cloud data,a double-sided filter was proposed to preprocess the three dimensional point cloud data of the original blast material surface in the paper. Poisson reconstruction algorithm is used to reconstruct the filtered point cloud data,build a multi-scale feature coding network,and repair the missing 3D point cloud material surface. Poisson surface reconstruction can retain the detail characteristics of the surface and smooth the surface,which provides an important basis for quickly judging the type of the surface. By extracting the point cloud feature information of different scales,3D point cloud feature enhancement and multi-level expression are realized. Experiments show that the proposed method has small error in point cloud missing prediction and complete shape of point cloud,which provides a fast,efficient and practical solution for processing point cloud data with missing material surface.
The permeability index of blast furnace is an important parameter that can quickly,intuitively,and comprehensively reflect the condition of the blast furnace. Accurately predicting the permeability index of blast furnaces can detect and avoid abnormal furnace conditions such as pipeline, suspended material,collapse,and gas loss as early as possible (about 10 min in advance). This article proposes a blast furnace permeability index prediction model that combines kernel principal component analysis ( KPCA), convolutional neural network ( CNN), and long short-term memory (LSTM). Firstly,KPCA is used to reduce the dimensionality of the original high-dimensional input variables,followed by CNN to capture the features of the data,and finally,LSTM is used to predict
the permeability index of the blast furnace. The results show that the constructed KPCA-CNN-LSTM blast furnace permeability index prediction model significantly reduces prediction errors and improves prediction accuracy compared to before dimensionality reduction. It is beneficial for blast furnace operators to quickly grasp the instantaneous changes in furnace conditions and take effective measures to restore smooth operation of the blast furnace.
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 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.