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Big Data of Steel and Low Carbon Intelligent Smelting

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A special issue of Metals (ISSN 2075-4701).

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 10469

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Dr. Jie Li

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Guest Editor

College of Metallurgy and Energy, North China University of Science and Technology, Caofeidian, Tangshan 063200, China
Interests: mineral-phase feature identification and extraction; CO emission reduction and pollutant treatment of sintering flue gas; metallurgical energy saving and resource optimization
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Dr. Zhenggen Liu

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Guest Editor

School of Metallurgy, Northeastern University, Shenyang, China
Interests: ironmaking raw materials; low-carbon ironmaking; comprehensive utilization of metallurgical resources

Dr. Hangyu Zhu

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Guest Editor

School of Materials and Metallurgy, Wuhan University of Science and Technology, Wuhan, China
Interests: non-metallic inclusion; clean-steel smelting theory and technology; low-density steel product development

Dr. Hao Liu

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Guest Editor

School of Metallurgy and Material Engineering, Chongqing University of Science and Technology, Chongqing, China
Interests: new technology for sintering pellets; new technology for low-carbon iron making; metallurgical resources value-added processing

Prof. Dr. Chunying Zhang

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Guest Editor

School of Science, North China University of Science and Technology, Tangshan 063000, China
Interests: big data of steel industry; complex network big data; machine learning; artificial intelligence
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Prof. Dr. Aimin Yang

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Guest Editor

College of Science, North China University of Science and Technology, Tangshan 063000, China
Interests: artificial intelligence; network security; big data modeling; numerical calculation; green metallurgy; precision medicine
Special Issues, Collections and Topics in MDPI journals

Dr. Weixing Liu

E-Mail Website
Guest Editor

North China University of Science and Technology, Tangshan, China
Interests: efficient utilization of metallurgical resources；intelligent manufacturing of sinter pellets；quantitative characterization of mineral structure characteristics；big data of steel

Special Issue Information

Dear Colleagues,

This Special Issue focus on the analysis of state data and image/video streams in the metallurgical reaction process using intelligent algorithms to extract characteristic data and explores the best practice to obtain useful information from the data to strengthen the metallurgical reaction process. With the help of machine vision and other means, research on the metallurgical reaction process has gradually shifted its focus from qualitative, descriptive and local research to precision, quantification and integration. This Special Issue’s scope not only includes studies on molecular, atomic and microscopic mineral phase analysis, but also those on the overall development law of the metallurgical reaction process. Theoretical breakthroughs or new ideas for low-carbon metallurgy driven by dual carbon goals are of particular interest. We also welcome research on: 1. intelligent low-carbon ore blending of iron ore powder; 2. integrated treatment of multi-pollutants in sintering flue gas; 3. intelligent ore blending driven by sintering big data; 4. the evolution law of iron ore mineral phase characteristics; 5. extraction of characteristic parameters of iron ore microstructure; 6. liquid-phase formation and crystallization behavior during metallurgical reaction; 7. the dissolution behavior of flux in high-temperature molten pools; 8. intelligent control of furnace temperature based on deep mining of big data in the blast furnace smelting process; 9. steelmaking end-point prediction model based on deep mining of flue gas big data; 10. comprehensive utilization of metallurgical solid-waste resources; 11. hydrogen metallurgy technology; and 12. non-blast furnace ironmaking technology.

Dr. Jie Li
Dr. Zhenggen Liu
Dr. Hangyu Zhu
Dr. Hao Liu
Prof. Dr. Chunying Zhang
Prof. Dr. Aimin Yang
Dr. Weixing Liu
Guest Editors

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Keywords

sintering flue gas
intelligent ore matching
big data of steel
study on mineral facies characteristics
blast furnace temperature
quality prediction of sinter
metallurgical solid waste
hydrogen metallurgy
non-blast furnace ironmaking technology
energy saving and resource optimization in metallurgy

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Published Papers (10 papers)

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Research

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16 pages, 13498 KiB

Open AccessArticle

Effect of V₂O₅ on Consolidation, Reduction, and Softening-Melting Behavior of High-Cr Vanadium Titanomagnetite

by Jianxing Liu, Zhenxing Xing, Xuyang Wang, Gongjin Cheng and Xiangxin Xue

Metals 2023, 13(7), 1171; https://doi.org/10.3390/met13071171 - 23 Jun 2023

Cited by 2 | Viewed by 1165

Abstract

Vanadium titanomagnetite is an important mineral resource. It is a raw material for ironmaking, vanadium extraction, strategic metal titanium production, and titanium dioxide production. In this study, high chromium vanadium titanomagnetite (High-Cr VTM) and ordinary iron ore were used as raw materials for pelletizing. The effect of V₂O₅ on the preparation and properties of High-Cr VTM pellets was studied. The influence of V₂O₅ on the properties of the green pellets, the compressive strength of oxidized pellets, the reduction swelling index and reduction degree, softening-melting behavior, and the migration law of Fe, Ti, and Cr in the reduction process were studied by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The results show that with the increase in V₂O₅ content, the properties of the green pellets basically showed a trend of first decreasing and then increasing but all met the basic requirements of pelletizing. When the added amount of V₂O₅ in the pellet was 6%, the compressive strength of the oxidized pellet was the lowest at only 2565 N/pellet but it still met the quality requirements for pellets in blast furnace production. As the dosage of V₂O₅ increased, the reduction swelling index and reduction degree of the pellets showed a trend of first increasing and then decreasing. The addition of V₂O₅ can increase the softening initial temperature, softening final temperature, melting start temperature, and dripping temperature of the High-Cr VTM pellets, narrowing the softening interval, and expanding the melting dripping interval. The experimental results provided a data reference for revealing the influence of V₂O₅ on High-Cr VTM pellets during the blast furnace smelting process. Full article

(This article belongs to the Special Issue Big Data of Steel and Low Carbon Intelligent Smelting)

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13 pages, 2310 KiB

Open AccessArticle

Study on Load-Bearing Characteristics of Converter Trunnion Bearing

by Liwen Chen, Bingyan Cui, Xiaochen Wu, Chenxu Yin and Jianhua Zhao

Metals 2023, 13(3), 549; https://doi.org/10.3390/met13030549 - 9 Mar 2023

Viewed by 2204

Abstract

In order to improve the load-bearing performance of converter trunnion bearing, this paper proposes magnetic-hydraulic bearing which is a new coupled support technology that combines electromagnetic support and hydrostatic support. It can be realized the active control of electromagnetic and hydraulic pressure, and is conducive to extending the bearing replacement period. Based on the magnetic-hydraulic coupling, a mathematical model of the initial state of the radial support system is established, the calculation formula of bearing capacity of radial magnetic fluid bearing is deduced, and Matlab software is used to analyze the variation trend of bearing capacity and stiffness with rotor displacement. The optimization of structural parameters was carried out according to analysis of bearing characteristics, and it was concluded that the bearing performance was the best when the diameter of the bearing cavity was 10 mm, and the bearing capacity increased significantly when the thickness of the oil film was 70 μm. It provides a theoretical basis for the innovative design of converter trunnion support system. Full article

(This article belongs to the Special Issue Big Data of Steel and Low Carbon Intelligent Smelting)

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14 pages, 1174 KiB

Open AccessArticle

Research on Multi-Decision Sinter Composition Optimization Based on OLS Algorithm

by Shilong Feng, Bin Wang, Zixing Zhou, Tao Xue, Aimin Yang and Yifan Li

Metals 2023, 13(3), 548; https://doi.org/10.3390/met13030548 - 9 Mar 2023

Cited by 5 | Viewed by 1913

Abstract

The adjustment of sintering raw materials has a decisive influence on the composition of blast furnace slag and the properties of sinter. In order to smelt high-quality molten iron in the blast furnace, the composition of the sinter must be properly adjusted so that the composition of the blast furnace slag and the metallurgical properties of the sinter are optimal for the quality of the iron and are conducive to the smooth operation of the blast furnace. In view of the huge difference in the quality and price of sintering raw materials, this paper proposes an automatic sintering ore blending model to quickly configure sintering raw materials according to the requirements of the production line. This method is based on the calculation process of blast furnace charge, combined with the constraints of process composition and cost performance, to establish a multi-decision sintering ore blending model based on the OLS(Ordinary least squares) algorithm to automatically screen from available raw materials and give the sinter that meets the requirements of the furnace. The plan finally makes TFe, CaO, MgO, SiO₂, TiO₂, Al₂O₃, P, Mn, Na₂O, K₂O, Zn, and other components meet the requirements of the production line, and meet the cost performance requirements of the enterprise for sinter. The model can complete the screening and proportioning of 43 kinds of raw materials within 10 s, and its performance can meet the requirements of the production of variable materials. Combined with an example, a comparative analysis experiment is carried out on the accuracy and practicability of the designed sintering and ore blending model. The experimental results show that the accuracy and efficiency of the method proposed in this paper are higher than those of the current ore blending scheme designed by enterprise engineers. This method can provide an effective reference for the stable operation of the sintering production line. Full article

(This article belongs to the Special Issue Big Data of Steel and Low Carbon Intelligent Smelting)

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16 pages, 5822 KiB

Open AccessArticle

Overlapping Pellet Size Detection Method Based on Marker Watershed and GMM Image Segmentation

by Weining Ma, Lijing Wang, Tianyu Jiang, Aimin Yang and Yuzhu Zhang

Metals 2023, 13(2), 327; https://doi.org/10.3390/met13020327 - 6 Feb 2023

Cited by 3 | Viewed by 2164

Abstract

The particle size of pellets is an important parameter in steel big data, and the high density and high overlap rate of pellets bring a great challenge to particle size detection. To address this problem, a particle size intelligent detection algorithm with an improved watershed and a Gaussian mixture model (GMM) is proposed. First, the initial segmentation of the pellets and background is achieved by using adaptive binary segmentation, and then the secondary fine segmentation of the pellets and background is achieved by combining morphological operations such as skeleton extraction and marked watershed segmentation; then, the contour of the connected domain of pellets is calculated, and the non-overlapping pellets in the foreground and the overlapping pellets are filtered according to the roundness of their contours. Finally, the number of overlapping pellets is predicted by Gaussian reconstruction of the grayscale image of the overlapping pellets, and the number and granularity of the overlapping pellets are predicted by the Gaussian reconstruction of the overlapping pellets. The experimental results showed that the algorithm achieved a 91.98% segmentation accuracy in the experimental images. Compared with other algorithms, the algorithm can also effectively suppress the over-segmentation and under-segmentation problems, and it can effectively realize the pellet size detection of dense, overlapping pellets such as those on a pelletizing disk, which provides an effective technical means for the metallurgical performance analysis of pellet ore and intelligent pellet-making driven by big data. Full article

(This article belongs to the Special Issue Big Data of Steel and Low Carbon Intelligent Smelting)

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13 pages, 3905 KiB

Open AccessArticle

Effect of Return Fines Embedding on the Sintering Behaviour of Vanadium Titanium Magnetite Concentrates

by Shihong Peng, Hao Liu, Huangjie Hua, Zezheng Sun, Yuelin Qin, Fei Meng, Weiqiang Liu and Guang Wang

Metals 2023, 13(1), 62; https://doi.org/10.3390/met13010062 - 26 Dec 2022

Cited by 4 | Viewed by 2028

Abstract

To improve the permeability of sinter packed bed for achieving the efficient utilization of low-grade iron bearing minerals, the effect of the returned fines embedding on productivity, yield, flame front speed (FFS) in the vanadium titanium magnetite (VTM) sintering process, tumble index (TI) of sinter, and permeability of the sinter packed bed was clarified. Results indicate that the productivity, yield, flame front speed, and tumble index of the vanadium titanium magnetite sintering process are all increased to a certain extent after embedding different sizes of returned fines, and the optimal sintering indices occur when the particle size of return fines for embedding is 3~5 mm. The optimal mass ratio of return fines for embedding was confirmed at 80%, and a continued increase in the mass ratio results in a decrease in flame front speed, yield, productivity, and tumble strength. Among the five different possible locations of embedded return fine layer, the middle-lower layer corresponds to the highest flame front speed. As the mass ratio of return fines for embedding is enhanced from 0% to 50%, the permeability of the sinter packed bed is improved at each stage of sintering. Full article

(This article belongs to the Special Issue Big Data of Steel and Low Carbon Intelligent Smelting)

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18 pages, 1916 KiB

Open AccessArticle

Prediction of Green Properties of Flux Pellets Based on Improved Generalized Regression Neural Network

by Zhenghan Xu, Zijing Wang, Xiwei Qi, Bin Bai and Jianming Zhi

Metals 2022, 12(11), 1840; https://doi.org/10.3390/met12111840 - 28 Oct 2022

Cited by 3 | Viewed by 1737

Abstract

In order to improve the quality of magnesia flux pellets and meet the production needs of the iron and steel industry, a pellet formation experiment was carried out. The effects of alkalinity R, SiO₂ mass fraction, MgO mass fraction on the green pellets’ burst temperature, compressive strength, and falling strength were studied. The results showed that with the increase in alkalinity, the bursting temperature of green pellets decreases, but has no obvious effect on the compressive strength or drop strength; with the increase in SiO₂ content, the bursting temperature of green pellets decreases gradually, and the green pellets’ strength also decreases slightly; with the increase in MgO content, the compressive strength of green pellets shows an upward trend, while the falling strength gradually decreases, and the burst temperature of green pellets shows a trend of rising first and then decreasing. The change trend is coupled with the software test data amplification method algorithm, based on the search algorithm of longicorn (MBAS), to expand a small amount of experimental data. Through data analysis and algorithm comparison, an improved generalized regression neural network (CFA-GRNN), based on culture firefly, was proposed to establish an optimization model for green pellet performance prediction. CFA uses the weights in the input layer and hidden layer of GRNN, the weights in the hidden layer and output layer, the threshold of the hidden layer and the threshold of the output layer as codes for optimization. The evolutionary goal is to obtain the most appropriate and optimal neural network structure. The results show that the MBAS algorithm, combined with the experimental research, can expand the effective data to 1000 pieces. Secondly, the green pellets’ burst temperature, compressive strength and falling strength predicted by the improved generalized regression neural network are in good agreement with the real values, and the average relative errors were 1.88%, 3.18% and 3.62%, respectively. The error analysis shows that the improved model algorithm has higher accuracy, meets the classification of pellets, and can be used to guide the production of pellets. Full article

(This article belongs to the Special Issue Big Data of Steel and Low Carbon Intelligent Smelting)

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13 pages, 3142 KiB

Open AccessArticle

Improved SVM Model for Predicting Pellet Metallurgical Properties Based on Textural Characteristics

by Yang Han, Lijing Wang, Wei Wang, Tao Xue and Yuzhu Zhang

Metals 2022, 12(10), 1662; https://doi.org/10.3390/met12101662 - 2 Oct 2022

Cited by 3 | Viewed by 1956

Abstract

From the point of view that pellet microstructure determines its metallurgical properties, an improved support vector machine (SVM) model for pellet metallurgical properties forecast is studied based on the mineral phase characteristics, in order to improve the evaluation efficiency of pellet metallurgical properties. The forecast model is composed of a SVM with self-adaptive selection of kernel parameters and a SVM with self-adaptive compounding of kernel types. This not only guarantees the super interpolation ability of the forecast model, but also takes into account its good generalization performance. Based on 200 sets of original sample information, the quantitative relationship between the main characteristics of mineral phase and the grade labels of pellet metallurgical properties (reduction expansion index RSI, reduction index RI, low temperature reduction and pulverization index RDI) was determined by the improved SVM model. With the simulation results of RSI, RI, and RDI with the accuracy of 100%, 98%, and 100% respectively, the precise forecast of pellet metallurgical properties based on mineral phase is realized. Full article

(This article belongs to the Special Issue Big Data of Steel and Low Carbon Intelligent Smelting)

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18 pages, 3688 KiB

Open AccessArticle

Dynamic Prediction Model of Silicon Content in Molten Iron Based on Comprehensive Characterization of Furnace Temperature

by Zeqian Cui, Aimin Yang, Lijing Wang and Yang Han

Metals 2022, 12(9), 1403; https://doi.org/10.3390/met12091403 - 24 Aug 2022

Cited by 12 | Viewed by 2545

Abstract

The silicon content of the molten iron is an important indicator of the furnace temperature trend in blast furnace smelting. In view of the multi-scale, non-linear, large time lag and strong coupling characteristics of the blast furnace smelting process, a dynamic prediction model for the silicon content of molten iron is established based on the analysis of comprehensive furnace temperature characterization data. The isolated forest algorithm is used to detect anomalies and analyze the causes of the anomalies in conjunction with the blast furnace mechanism. The maximum correlation-minimum redundancy mutual information feature selection method is used to reduce the dimensionality of the furnace temperature characterization data. The grey correlation analysis with balanced proximity is used to obtain the correlation between the furnace temperature characterization parameters and the silicon content of the molten iron at different time lags and to integrate the furnace temperature characterization data set. The GRA-FCM model is used to divide the parameter set of the integrated furnace temperature characterization and construct a parameter directed network from multiple control parameters to multiple state parameters. The GWO-SVR model is used to predict the state parameters of each delay step by step to achieve dynamic prediction of the silicon content of the molten iron. Finally, the control parameters are adjusted backwards according to the prediction results of the state parameters and the silicon content of the molten iron and expert experience to achieve accurate control of the furnace temperature. Starting from the actual production situation of a blast furnace, the characteristic parameters are divided into control parameters and state parameters. This model establishes a multi-step dynamic prediction and closed-loop control model of “control parameters-state parameters-silicon content in hot metal-control parameters”. Full article

(This article belongs to the Special Issue Big Data of Steel and Low Carbon Intelligent Smelting)

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13 pages, 714 KiB

Open AccessArticle

Automatic Ore Blending Optimization Algorithm for Sintering Based on the Cartesian Product

by Xinying Ren, Chaoyi Gao, Hanchen Wang, Shilong Feng, Tao Xue and Aimin Yang

Metals 2022, 12(8), 1351; https://doi.org/10.3390/met12081351 - 15 Aug 2022

Cited by 5 | Viewed by 2432

Abstract

In actual sinter production, batching is a complex metallurgical and mathematical problem. Aiming at the problem of the precising batching of iron ore in the process of sintering batching, an automatic batching algorithm based on a Cartesian product to batch sinter was proposed for the first time. When the algorithm is applied to the sintering batching process, a complete batching scheme can be obtained, which can realize the organic combination with other calculation processes, can effectively save the manpower and material cost of sintering batching, and is of great significance to the comprehensive use of iron ore resources. Taking the actual sintering production batching of a domestic iron and steel plant as an example, according to the batching requirements compared with various ore batching schemes, combined with the actual production situation, the automatic batching optimization algorithm based on a Cartesian product is applied to build a mathematical model of sintering batching. Through the algorithm test, the practicability of the automatic batching algorithm is verified. In addition, the automatic batching algorithm based on a Cartesian product has good performance in other batching fields. Full article

(This article belongs to the Special Issue Big Data of Steel and Low Carbon Intelligent Smelting)

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Review

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13 pages, 1959 KiB

Open AccessReview

Research Progress of Intelligent Ore Blending Model

by Yifan Li, Bin Wang, Zixing Zhou, Aimin Yang and Yunjie Bai

Metals 2023, 13(2), 379; https://doi.org/10.3390/met13020379 - 13 Feb 2023

Cited by 9 | Viewed by 3399

Abstract

The iron and steel industry has made an important contribution to China’s economic development, and sinter accounts for 70–80% of the blast furnace feed charge. However, the average grade of domestic iron ore is low, and imported iron ore is easily affected by transportation and price. The intelligent ore blending model with an intelligent algorithm as the core is studied. It has a decisive influence on the development of China’s steel industry. This paper first analyzes the current situation of iron ore resources, the theory of sintering ore blending, and the difficulties faced by sintering ore blending. Then, the research status of the neural network algorithms, genetic algorithms, and particle swarm optimization algorithms in the intelligent ore blending model is analyzed. On the basis of the neural network algorithm, genetic algorithm and particle swarm algorithm, linear programming method, stepwise regression analysis method, and partial differential equation are adopted. It can optimize the algorithm and make the model achieve better results, but it is difficult to adapt to the current complex situation of sintering ore blending. From the sintering mechanism, sintering foundation characteristics, liquid phase formation capacity of the sinter, and the influencing factors of sinter quality were studied, it can carry out intelligent ore blending more accurately and efficiently. Finally, the research of intelligent sintering ore blending model has been prospected. On the basis of sintering mechanism research, combined with an improved intelligent algorithm. An intelligent ore blending model with raw material parameters, equipment parameters, and operating parameters as input and physical and metallurgical properties of the sinter as output is proposed. Full article

(This article belongs to the Special Issue Big Data of Steel and Low Carbon Intelligent Smelting)

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