25 July 2024, Volume 48 Issue 4

    

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  Preface

  LIUWenzhong

  Metallurgical Industry Automation. 2024, 48(4): 1-1.

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Special column on industrial software for iron and steel industry
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   Research on intelligent equipment software platform for steel industry based on machine vision

  WU Kunpeng, YANG Chaolin, LI Zhiyou, SHI Jie, DENG Nenghui

  Metallurgical Industry Automation. 2024, 48(4): 2-8. https://doi.org/10. 3969/ j. issn. 1000-7059. 2024. 04. 001

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  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.
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  Metadata based construction of data space for steel rolling process

  DONG Jie, KANG Yongyi, ZHANG Hongjun, PENG Kaixiang

  Metallurgical Industry Automation. 2024, 48(4): 9-20. https://doi.org/10. 3969/ j. issn. 1000-7059. 2024. 04. 002

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  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.
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  Mechanical properties optimization method of hot strip based on SHAP framework

  SONG Jun, GAO Lei, WANG Kuiyue, CAO Zhonghua, MA Chiyu, MA Xiaoguo

  Metallurgical Industry Automation. 2024, 48(4): 21-32. https://doi.org/10. 3969/ j. issn. 1000-7059. 2024. 04. 003

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  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.
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  Optimization strategy of bending force and rolling shift for hot rolling process of silicon steel based on stochastic configuration network-dung beetle optimization algorithm

  DU Haozhan, DING Jingguo, SUN Jianhong, CAO Guoyu, ZHAO Jian, LI Xu, ZHANG Dianhua

  Metallurgical Industry Automation. 2024, 48(4): 33-45. https://doi.org/10. 3969/ j. issn. 1000-7059. 2024. 04. 004

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  Hot rolled silicon steel plate shape has significant genetic effect on the cold rolled plate shape and edge drop,and reducing the hot rolled silicon steel products transverse with the same plate difference can effectively improve the quality of cold rolled products. However,in the process of hot rolling of silicon steel,changing specifications or steel grades can lead to inaccurate pre-set values of bending force and rolling shift,which leads to a poor plate shape control effect. To address this problem,this paper proposes a prediction model for silicon steel crown based on stochastic configuration network (SCN). To improve the model's capacity to fit the data,the number of hidden layers in the model was increased (DeepSCN),and incorporated the manifold regularization term in the SCN modeling process (RSC). Guided by the data-driven model's prediction results,the dung beetle optimization (DBO) algorithm was used to optimize the bending force and rolling shift. The results show that this method can control the fluctuation of the proportional crown within a small range,and the data of the finishing mill exit plate crown deviation within ±5 μm can be improved to 92. 2% . This not only effectively improves the quality of plate shape of silicon steel,but also provides a new research direction and technical method for the control of plate shape in silicon steel.
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  Deviation prediction of exit thickness of finished first rolled steel coil based on improved BP algorithm of genetic algorithm

  XU Xinkai, CHEN Xuejiao, SONG Xiangrong, QI Zheng, LU Guangzhou, SUN Chenxi

  Metallurgical Industry Automation. 2024, 48(4): 46-52. https://doi.org/10. 3969/ j. issn. 1000-7059. 2024. 04. 005

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  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.
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   Production total factor analysis system based on hierarchical causal scoring model

  QIAN Weidong, YU Jiayi

  Metallurgical Industry Automation. 2024, 48(4): 53-63. https://doi.org/10. 3969/ j. issn. 1000-7059. 2024. 04. 006

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  In order to comprehensively improve product quality,open up multilevel IT system,optimize production planning,and realize tracking and tracing of the whole life cycle of production line, a production total factor analysis system was proposed,which processes real-time asset data through a rule engine to form feature input containing business logic,uses feature input and diagnostic prior knowledge to build a hierarchical causal score graph,and constructs a hierarchical score model at the node level of the graph to obtain inference results from low level to high level of processes,equipment,materials,personnel and other nodes. Through layer upon layer reasoning of the directed graph, a score result bound with the production ID number is finally obtained. In this process,the node location causing the score anomaly can be quickly and accurately located. Map abstract asset information to field production problems,allowing managers to respond quickly. In the field application,the joint analysis of the same batch of different materials can extend the monitoring of the whole production factors such as quality,yield and energy consumption of a single material to the entire production line. The construction of the model is completed through the visual operation page. Due to the excellent expansion performance of the model,a rich production analysis model library is built and the precipitation of expert knowledge is realized,which has strong promotion value.
Frontier technology and review
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   Research progress of intelligent sintering prediction technology based on big data

  ZHANG Xuefeng, TANG Jingjing, HUANG Liusong, WEN Yixin

  Metallurgical Industry Automation. 2024, 48(4): 64-76. https://doi.org/10. 3969/ j. issn. 1000-7059. 2024. 04. 007

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  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.
Exploration and practice of intelligent manufacturing
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  Intelligent manufacturing model based on steel process analysis and reengineering

  WANG Gang, XU Can, HE Maocheng, XIE Hao, HE Haixi

  Metallurgical Industry Automation. 2024, 48(4): 77-83. https://doi.org/10. 3969/ j. issn. 1000-7059. 2024. 04. 008

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  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.
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   Application of intelligent crane control system in bottom filtration process of blast furnace

  SU Liwei, GAO Da, ZHOU Fan, HAN Zhimeng

  Metallurgical Industry Automation. 2024, 48(4): 84-89. https://doi.org/10. 3969/ j. issn. 1000-7059. 2024. 04. 009

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  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.
Artificial intelligence technique
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  Intelligent diagnosis model of BF hanging based on RFE-GBDT

  LI Fumin, YANG Liu, LIU Xiaojie, MENG Lingru, LI Hongyang, Lv Qing

  Metallurgical Industry Automation. 2024, 48(4): 90-100. https://doi.org/10. 3969/ j. issn. 1000-7059. 2024. 04. 010

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  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.
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   Online thickness prediction of hot rolling strip based on IBA-LightGBM

  HUANG Shuo, ZHANG Fei, WANG Lijun, GUO Qiang, XIAO Xiong

  Metallurgical Industry Automation. 2024, 48(4): 101-109. https://doi.org/10. 3969/ j. issn. 1000-7059. 2024. 04. 011

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  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.
Process control theory and technique
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  Development and application of sealing process operation model in blast furnace

  LIN Anchuan, QIU Guibao, LIU Xiaolan, JIANG Yubo, XU Jianyu

  Metallurgical Industry Automation. 2024, 48(4): 110-124. https://doi.org/10. 3969/ j. issn. 1000-7059. 2024. 04. 012

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  The sealing of blast furnace is a complicated and special operation in ironmaking. In order to fully quantify the complex relationship between many factors affecting its control and form the standardization and process of sealing operation. Based on ironmaking theory,combined with practical experience and supplemented by computer information means,a process operation model of BF furnace sealing was innovatively designed and developed that can quantify modularize and accurately control the charge batching,discharge and comprehensive smelting parameter control and check in the process of furnace sealing,and have the functions of data collection,quantitative evaluation and optimization. It not only improves the accuracy of the calculation of batching and discharging of the blast furnace,the empty volume in the upper part of the furnace and the position of the sealing charge,and the control of the sealing parameters,but also the calculation methods of the important operation nodes such as the last iron drawing,stopping the feeding,closing the furnace and stopping the coal and oxygen are given. The practice applied to the blast furnace with specific volume and raw fuel condition shows that,in the process of furnace sealing,the deviation rate between the planned vacated volume and the calculated value according to the theoretical material speed is-0. 184% . The deviation rate between the planned filled volume and the actual value of the sealed charge is-0. 72%,and the deviation value between the planned filling line and the actual value is 0. 15 m. The difference between the coke ratio calculated according to theoretical rate of drop of blast furnace charge and the coke ratio calculated by the plan to vacate volume batching is 0. 81% . The parameters of hot metal in the first furnace are all within the defined range of the calculation of charging and discharging of sealed charge. The smelting parameters of blast furnace returned to normal after 6 natural shifts. The model lays a foundation for improving precision of sealing process,shortening the smelting process and reducing the cost.