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Metallurgical Process: Optimization and Control
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Metallurgical Process: Optimization and Control

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (20 May 2024) | Viewed by 7292

Special Issue Editors

Dr. Shengchao Duan

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Guest Editor
School of Material Science and Chemical Engineering, Hanyang University, Ansan 15588, Republic of Korea
Interests: electroslag remelting; homogeneous control of alloying elements; cleanliness; nonmetallic inclusions; secondary refining process
Prof. Dr. Hanjie Guo

E-Mail Website
Guest Editor
School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China
Interests: electroslag remelting; cleanliness steel; metallurgical physiochemistry
Dr. Jae Hong Shin

E-Mail Website
Guest Editor
Korea Institute of Industrial Technology, Incheon 21655, Republic of Korea
Interests: steelmaking; secondary refining; inclusion; pyrometallurgy; recovery of valuable metal; computational thermodynamics; computational simulation of metallurgical process
Special Issues, Collections and Topics in MDPI journals
Dr. Yong Wang

E-Mail Website
Guest Editor
The State Key Laboratory of Refractories and Metallurgy, Wuhan University of Science and Technology, Wuhan 430081, China
Interests: clean steel; thermodynamics and kinetics of metallurgy; solidification; ferroalloys
Dr. Changyong Chen

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hongkong 541002, China
Interests: additive manufacturing; NiTi alloy; ultra-clean steel; non-metallic inclusions; micro-alloying steel
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Pyrometallurgy processes, especially in the secondary refining processes, play an important role in improving the cleanliness and mechanical properties of final products by removing non-metallic inclusions and impurity elements. However, metallurgical processes have various complex physical and chemical reactions at high temperatures; thus, several variables may affect the metallurgy process, including (but not limited to) the properties of slag and refractory materials for the ferrous metallurgy, the remelting rate and fill ratio for electro slag remelting (ESR), etc. Therefore, the optimization of metallurgical processes using experimental and theoretical simulation methods is indispensable to making the metallurgy process smooth and efficient. Except for iron-based alloys, the development of the refining technology of the other alloy systems at high temperatures in the form of a liquid state is also accepted. 

Dr. Shengchao Duan
Prof. Dr. Hanjie Guo
Dr. Jae Hong Shin
Dr. Yong Wang
Dr. Changyong Chen
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • secondary refining process
  • nonmetallic inclusions
  • solidification structure
  • computational thermodynamics and kinetics

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Published Papers (5 papers)

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16 pages, 6383 KiB  
Article
The Microstructure, Mechanical Properties, and Precipitation Behavior of 1000 MPa Grade GEN3 Steel after Various Quenching Processes
by Angang Ning, Rui Gao, Stephen Yue and Timothy Skszek
Processes 2024, 12(9), 2039; https://doi.org/10.3390/pr12092039 - 21 Sep 2024
Viewed by 854
Abstract
This study examines the microstructure, mechanical properties, and precipitation behavior of 1000 MPa grade GEN3 steel when subjected to various quenching processes, with a focus on the quench and partition (Q&P) technique. The Q&P-treated samples achieved 1300 MPa tensile strength and demonstrated superior [...] Read more.
This study examines the microstructure, mechanical properties, and precipitation behavior of 1000 MPa grade GEN3 steel when subjected to various quenching processes, with a focus on the quench and partition (Q&P) technique. The Q&P-treated samples achieved 1300 MPa tensile strength and demonstrated superior yield strength, attributed to their refined substructure and their large amounts of precipitates. The quenched samples exhibited the thinnest martensite laths due to the highest martensite volume. Despite the as-annealed samples having the smallest grain size, the Q&P treatment resulted in optimal microstructural refinement results and a high dislocation density, reaching 1.15 × 1015 m−2. Analysis of the precipitates revealed the presence of V8C7, M7C3, M2C, and Ti(C, N) across various heat treatments. The application of the McCall–Boyd method and the Ashby–Orowan correction model indicated that quench and tempered (Q&T) samples contained the largest volume of fine precipitates, contributing to their high yield strengths. These findings offer valuable insights for optimizing heat treatment processes to develop advanced high-strength steels for industrial applications. Full article
(This article belongs to the Special Issue Metallurgical Process: Optimization and Control)
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Figure 1

Figure 1
<p>The heating process and dilatometer results of the GEN3 steel.</p> Full article ">Figure 2
<p>Schematic diagram of Sample 2#, 3#, and 4#.</p> Full article ">Figure 3
<p>The microstructure of steel after different heat treatments.</p> Full article ">Figure 3 Cont.
<p>The microstructure of steel after different heat treatments.</p> Full article ">Figure 4
<p>EBSD results of steel after different heat treatments: (<b>a1</b>–<b>a4</b>) show Inverse Pole Figure (IPF) maps for body-centered cubic (bcc) structures; (<b>b1</b>–<b>b4</b>) illustrate misorientation boundary maps with color codes: red for 2–5°, green for 5–15°, and blue for &gt;15°; (<b>c1</b>–<b>c4</b>) phase distributions (green: fcc; red: bcc); (<b>d1</b>–<b>d4</b>) represents the KAM (kernel average misorientation maps) of the body-centered cubic (bcc) structures.</p> Full article ">Figure 5
<p>Stress–strain and work-hardening curves of steels after heat treatment.</p> Full article ">Figure 6
<p>Change in phase distribution of the steel as a function of temperature.</p> Full article ">Figure 7
<p>Elements in M<sub>7</sub>C<sub>3</sub> and M(C, N).</p> Full article ">Figure 8
<p>M<sub>2</sub>C after quench and temper: (<b>a</b>) Morphology by TEM; (<b>b</b>) SAED (Selected Area Electron Diffraction) analysis; (<b>c</b>) EDS (Energy Dispersive X-ray Spectrum) analysis.</p> Full article ">Figure 9
<p>M<sub>7</sub>C<sub>3</sub> after quench and partition: (<b>a</b>) Morphology; (<b>b</b>) SAED analysis; (<b>c</b>) EDS result.</p> Full article ">Figure 10
<p>V<sub>8</sub>C<sub>7</sub> after quench and partition: (<b>a</b>) Morphology; (<b>b</b>) SAED analysis; (<b>c</b>) EDS result.</p> Full article ">Figure 11
<p>Ti(C, N) after quenching by water: (<b>a</b>) Morphology under STEM mode; (<b>b</b>) EDS result by line scanning.</p> Full article ">Figure 12
<p>The comparison of typical martensite microstructure for Samples 3 and 4: (<b>a</b>) Tempered martensite in Q&amp;T sample; (<b>b</b>) Martensite microstructure after Q&amp;P process.</p> Full article ">
18 pages, 4973 KiB  
Article
Simulation of Solidification Structure in the Vacuum Arc Remelting Process of Titanium Alloy TC4 Based on 3D CAFE Method
by Zhenquan Jing, Rui Liu, Naitao Geng, Ying Wang and Yanhui Sun
Processes 2024, 12(4), 802; https://doi.org/10.3390/pr12040802 - 16 Apr 2024
Cited by 1 | Viewed by 1548
Abstract
Vacuum arc remelting is the main production method of titanium alloy ingots at present. In order to obtain good quality ingots, it is of great significance to study the formation of the solidification structure of ingots via vacuum arc remelting. In order to [...] Read more.
Vacuum arc remelting is the main production method of titanium alloy ingots at present. In order to obtain good quality ingots, it is of great significance to study the formation of the solidification structure of ingots via vacuum arc remelting. In order to select and optimize the nucleation parameters for the solidification microstructure simulation of an ingot, a 3D CAFE model for microstructure evolution during vacuum arc remelting was established, taking into account heat transfer, flow, and solute diffusion. The Gaussian distribution continuous nucleation model and extended KGT model were used to describe the grain nucleation and dendrite tip growth rates, respectively. The multi-point mass source and moving boundary method were used to simulate the ingot growth. The results show that there are three typical crystal regions in the solidification structure of vacuum arc remelting titanium alloy ingots, namely the surface fine crystal region, columnar crystal region, and central equiaxed crystal region. The proportion of the columnar crystal region in the solidification structure of an ingot increases gradually with the increase in the undercooling of the maximum bulk nucleation. With an increase in the maximum bulk nucleation density, the equiaxed grain zone gradually increases, and the grain size gradually decreases. The proportion of the columnar crystal region in the solidification structure of an ingot increases gradually with an increase in the undercooling of the maximum bulk nucleation. The maximum volume nucleation variance has no obvious effect on the change in the solidification structure. When the maximum volume nucleation undercooling is 5.5 K, the maximum volume nucleation standard deviation is 4 K, and the maximum volume nucleation density is 5 × 108. The solidification structure simulation results are in good agreement with the experimental results. Full article
(This article belongs to the Special Issue Metallurgical Process: Optimization and Control)
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Figure 1

Figure 1
<p>Schematic diagram of VAR.</p> Full article ">Figure 2
<p>Grain nucleation density distribution curves on the ingot surface and inside.</p> Full article ">Figure 3
<p>Physical parameters of the titanium alloy TC4: (<b>a</b>) thermal conductivity, (<b>b</b>) density, (<b>c</b>) enthalpy, (<b>d</b>) viscosity, (<b>e</b>) solid fraction.</p> Full article ">Figure 4
<p>Geometric model and grid division of the ingot: (<b>a</b>) geometric model, (<b>b</b>) grid division.</p> Full article ">Figure 5
<p>Schematic diagram of the boundary conditions.</p> Full article ">Figure 6
<p>Distribution of the temperature field and solid fraction at different moments in the longitudinal section of the model during the smelting process.</p> Full article ">Figure 7
<p>Variation of molten pool depth with time in the melting process.</p> Full article ">Figure 8
<p>Changes in the solidification structure of the ingot at different times in the longitudinal section of the model during melting.</p> Full article ">Figure 9
<p>Comparison of solidification structures at different maximum bulk nucleation undercoolings: (<b>a</b>) 3 K, (<b>b</b>) 5.5 K, (<b>c</b>) 8 K.</p> Full article ">Figure 10
<p>Changes in the grain number and average grain area under different maximum bulk nucleation undercooling levels.</p> Full article ">Figure 11
<p>Comparison of the solidification structures at different maximum bulk nucleation densities: (<b>f</b>) 5 × 10<sup>7</sup>, (<b>b</b>) 5 × 10<sup>8</sup>, (<b>g</b>) 5 × 10<sup>9</sup>.</p> Full article ">Figure 12
<p>The change in the grain number and average grain area with different maximum nucleation densities.</p> Full article ">Figure 13
<p>Comparison of the solidification structures at different maximum bulk standard deviations of body nucleation: (<b>d</b>) 2 K, (<b>b</b>) 4 K, (<b>e</b>) 6 K.</p> Full article ">Figure 14
<p>The change in the grain number and average grain area with different standard deviations of maximum bulk nucleation.</p> Full article ">Figure 15
<p>Comparison of the simulated results and experimental results of the solidification structure.</p> Full article ">
20 pages, 3214 KiB  
Article
Kinetic Investigation of the Deep Desulfurization of 5 wt% Si High-Silicon Austenitic Stainless Steel
by Guanxiong Dou, Hanjie Guo, Jing Guo and Xuecheng Peng
Processes 2024, 12(4), 781; https://doi.org/10.3390/pr12040781 - 12 Apr 2024
Viewed by 1319
Abstract
Given the demand for extremely low sulfur content in 5 wt% Si high-silicon austenitic stainless steel (SS-5Si), smelting utilizes a slag composition of CaF2-CaO-Al2O3-MgO-SiO2 with a basicity of 1 to 3, Al2O3 content [...] Read more.
Given the demand for extremely low sulfur content in 5 wt% Si high-silicon austenitic stainless steel (SS-5Si), smelting utilizes a slag composition of CaF2-CaO-Al2O3-MgO-SiO2 with a basicity of 1 to 3, Al2O3 content ranging from 2.04 to 9.61%, and CaF2 content between 20.8 and 31.62%. Experiments designed to investigate the sulfur content in molten steel at temperatures of 1773 K, 1823 K, and 1873 K over durations of 1, 5, 10, 15, and 30 min, under varying slag compositions, corroborated with a theoretically derived model hypothesizing a “rate-controlling” step in mass transfer, revealed that the mass transfer of sulfur within the molten steel was determined to be the rate-controlling step (RCS) in the (CaO) + [S] = (CaS) + [O] reaction kinetics, and the variability of the mass transfer coefficient of sulfur, kS,m, in the molten steel ranged from 1.04 × 10−5 m∙s−1 to 2.24 × 10−5 m∙s−1. Based on the temperature dependency of kS,m, the apparent activation energy for the desulfurization reaction was estimated to be 96.03 kJ/mol. Considering the slag components, the binary basicity, denoted as R, exerted an overriding influence on the process of desulfurization. At a basicity of 1, the sulfur content within the liquid steel was reduced, from 22 ppm to 11 ppm within a time span of 30 min. In contrast, an increase in the basicity to a value of 3 showed a significant consequence: over an identical temporal duration of 30 min, the sulfur content was drastically reduced to 2.2 ppm. By contrast, an initial surge in desulfurization rates is observed within the first five minutes, attributable to relatively lower concentrations of Al2O3 and higher levels of CaF2. Subsequently, these parameters exert no significant influence on the kinetics of the desulfurization process. Full article
(This article belongs to the Special Issue Metallurgical Process: Optimization and Control)
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Figure 1

Figure 1
<p>Relationship between the basicity and Al<sub>2</sub>O<sub>3</sub> content in the CaF<sub>2</sub>-CaO-Al<sub>2</sub>O<sub>3</sub>-MgO-SiO<sub>2</sub> slag for (<b>a</b>) equilibrium oxygen content, (<b>b</b>) equilibrium oxygen content at 1873 K for SS-5Si.</p> Full article ">Figure 2
<p>Schematic diagram of the double-layer crucible at different stages: (<b>a</b>) melting process, (<b>b</b>) reaction initiation, and (<b>c</b>) slag–metal reaction.</p> Full article ">Figure 3
<p>(<b>a</b>) A macroscopic model of heterogeneous reactions; (<b>b</b>) Schematic of the concentration <math display="inline"><semantics> <mrow> <msubsup> <mi>C</mi> <mi>i</mi> <mi>b</mi> </msubsup> </mrow> </semantics></math> distribution during the heterogeneous reaction and the definition of the thickness of the effective boundary layer <math display="inline"><semantics> <mrow> <msubsup> <mi>δ</mi> <mi>c</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math> proposed by Wagner.</p> Full article ">Figure 4
<p>Determination of the mass transfer coefficient of S for the different slag basicities: (<b>a</b>) S1, (<b>b</b>) S2, (<b>c</b>) S3 at 1873 K (1600 °C).</p> Full article ">Figure 5
<p>Determination of the mass transfer coefficient of S for the different slag compositions: (<b>a</b>) S4, S5, and S6; (<b>b</b>) S7, S8, and S9 at 1873 K (1600 °C).</p> Full article ">Figure 6
<p>The relationship between <span class="html-italic">F</span>(<span class="html-italic">w</span>[S]<sub>%</sub>) and time at temperatures (1772, 1823, and 1873 K) under slag basicity: (<b>a</b>) S1; (<b>b</b>) Localized expansion diagram demarcated by the dotted line in (<b>a</b>); (<b>c</b>) S2.</p> Full article ">Figure 7
<p>Model predictions (lines) and experimental data (points) of change in sulfur content with time for different slag compositions: (<b>a</b>) CaO, (<b>b</b>) Al<sub>2</sub>O<sub>3</sub>, and (<b>c</b>) CaF<sub>2</sub>.</p> Full article ">
17 pages, 6405 KiB  
Article
Numerical Investigation on Solidification Behavior of Slab Ingot during Electroslag Remelting Process
by Xin Geng, Zhou-Hua Jiang and Fu-Bin Liu
Processes 2023, 11(7), 2085; https://doi.org/10.3390/pr11072085 - 13 Jul 2023
Viewed by 1010
Abstract
In the process of electroslag remelting (ESR) for large-sized slab ingots, controlling the surface quality of the slab ingot is challenging due to its relatively high width-to-thickness ratio. In this study, a three-dimensional dynamic mathematical model for single-electrode ESR slab ingots was developed [...] Read more.
In the process of electroslag remelting (ESR) for large-sized slab ingots, controlling the surface quality of the slab ingot is challenging due to its relatively high width-to-thickness ratio. In this study, a three-dimensional dynamic mathematical model for single-electrode ESR slab ingots was developed using dynamic mesh technology, with the aid of the commercial software FLUENT. The model is based on the electromagnetic field equation, flow field equation, and energy equation. A detailed analysis of various physical fields and the distribution law of the metal pool shape was conducted. According to the calculation results, the maximum flow velocity of the molten slag was found below the consumable electrode, with the range of maximum velocity at different time points varying between 4.35 × 10−2 and 4.88 × 10−2 m/s. The range of the maximum temperature for the slag bath at different time points was between 2118 and 2122 K. As the remelting continued, the impact of the forced cooling of the bottom plate on the temperature of the metal pool weakened. Consequently, the temperature gradient of the electroslag ingot gradually decreased, the depth of the metal pool increased, and the height of the metal liquid head in the metal pool rose. The effects of different voltages, filling ratios, and mold chamfers on the shape of the metal pool were investigated using the established mathematical model. Based on the research findings from the mathematical model, the technical processes for ESR J80 large-sized slab ingots were improved, providing solutions to improve the surface quality of the ESR large-sized slab ingots. Full article
(This article belongs to the Special Issue Metallurgical Process: Optimization and Control)
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Figure 1

Figure 1
<p>Schematic diagram of boundary conditions (Z<sub>1</sub>—electroslag ingot bottom; Z<sub>2</sub>—slag–metal interface; Z<sub>3</sub>—electrode bottom; and Z<sub>4</sub>—free slag surface).</p> Full article ">Figure 2
<p>Computational domain of ESR slab ingot process.</p> Full article ">Figure 3
<p>Velocity field distribution of slag bath in ESR of slab ingots at different time points: (<b>a</b>–<b>c</b>) <span class="html-italic">t</span> = 70 s; (<b>d</b>–<b>f</b>) <span class="html-italic">t</span> = 400 s; (<b>b</b>,<b>e</b>) <span class="html-italic">y</span> = 0 m section; and (<b>c</b>,<b>f</b>) <span class="html-italic">x</span> = 0 m section.</p> Full article ">Figure 4
<p>Temperature field distribution in ESR of slab ingots at different time points: (<b>a</b>–<b>c</b>) <span class="html-italic">t</span> = 70 s; (<b>d</b>–<b>f</b>) <span class="html-italic">t</span> = 400 s; (<b>b</b>,<b>e</b>) <span class="html-italic">y</span> = 0 m section; and (<b>c</b>,<b>f</b>) <span class="html-italic">x</span> = 0 m section.</p> Full article ">Figure 5
<p>Shape of metal pool in ESR of slab ingots at different time points: (<b>a</b>) <span class="html-italic">t</span> = 70 s; (<b>b</b>) <span class="html-italic">t</span> = 136 s; (<b>c</b>) <span class="html-italic">t</span> = 202 s; (<b>d</b>) <span class="html-italic">t</span> = 268 s; (<b>e</b>) <span class="html-italic">t</span> = 334 s; and (<b>f</b>) <span class="html-italic">t</span> = 400 s.</p> Full article ">Figure 6
<p>Shape of metal pool at various sections in ESR of slab ingots at different time points: (<b>a</b>,<b>b</b>) <span class="html-italic">t</span> = 70 s; (<b>c</b>,<b>d</b>) <span class="html-italic">t</span> = 400 s; (<b>a</b>,<b>c</b>) <span class="html-italic">y</span> = 0 m section; and (<b>b</b>,<b>d</b>) <span class="html-italic">x</span> = 0 m section.</p> Full article ">Figure 7
<p>Shape of the metal pool at the <span class="html-italic">y</span> = 0 m section (<b>a</b>) and the <span class="html-italic">x</span> = 0.106 m section (<b>b</b>) when <span class="html-italic">t</span> = 400 s.</p> Full article ">Figure 8
<p>Shape of metal pool when <span class="html-italic">t</span> = 70 s and <span class="html-italic">t</span> = 400 s: (<b>a</b>,<b>d</b>) 30 V; (<b>b</b>,<b>e</b>) 32 V; and (<b>c</b>,<b>f</b>) 35 V.</p> Full article ">Figure 9
<p>Depth of metal pool at the <span class="html-italic">y</span> = 0 m section (<b>a</b>) and the <span class="html-italic">y</span> = 0.106 m section (<b>b</b>) under different voltages when <span class="html-italic">t</span> = 400 s.</p> Full article ">Figure 10
<p>Shape of metal pool when <span class="html-italic">t</span> = 70 s and <span class="html-italic">t</span> = 400 s: (<b>a</b>,<b>d</b>) S = 0.48; (<b>b</b>,<b>e</b>) S = 0.54; and (<b>c</b>,<b>f</b>) S = 0.60.</p> Full article ">Figure 11
<p>Depth of metal pool at the <span class="html-italic">y</span> = 0 m section (<b>a</b>) and the <span class="html-italic">y</span> = 0.106 m section (<b>b</b>) under the condition of different filling ratios when <span class="html-italic">t</span> = 400 s.</p> Full article ">Figure 12
<p>Schematic diagram for the addition of the chamfer to the mold.</p> Full article ">Figure 13
<p>Temperature field distribution when <span class="html-italic">t</span> = 70 s and <span class="html-italic">t</span> = 400 s: (<b>a</b>,<b>c</b>) with the chamfer and (<b>b</b>,<b>d</b>) without the chamfer.</p> Full article ">Figure 14
<p>Depth of metal pool with and without the chamfer at the <span class="html-italic">y</span> = 0 m section when <span class="html-italic">t</span> = 400 s.</p> Full article ">Figure 15
<p>Electroslag ingots with a dimension of 620 mm × 2060 mm × 3630 mm produced before the improved process.</p> Full article ">Figure 16
<p>Electroslag ingots with a dimension of 620 mm × 2060 mm × 3630 mm produced after the improved process.</p> Full article ">Figure 17
<p>Macrograph of the narrow side at a distance of 450 mm from the bottom of the ingot.</p> Full article ">
21 pages, 8847 KiB  
Article
Research on the Factors Affecting the Formation of Ore-Free Zone at Blast Furnace Throat Based on DEM
by Hao Xu, Yici Wang, Chuanhui Li, Hongwei Guo and Bingji Yan
Processes 2023, 11(3), 967; https://doi.org/10.3390/pr11030967 - 22 Mar 2023
Cited by 1 | Viewed by 1700
Abstract
The ore-free zone in the center of the blast furnace throat is a major feature of the charging system, ensuring the permeability of the center. Factors that influence the formation of the ore-free zone need to be researched to increase the control precision. [...] Read more.
The ore-free zone in the center of the blast furnace throat is a major feature of the charging system, ensuring the permeability of the center. Factors that influence the formation of the ore-free zone need to be researched to increase the control precision. In this paper, on the basis of a 1:1 3D model of a blast furnace, the formation of an ore-free zone at the burden surface at the throat was simulated by using the discrete element method (DEM). The effects of burden line depth, batch weight, and distribution angle on the formation of ore-free zones were investigated. The results showed that with increasing burden line depth, the width of the ore-free zone increased, the thickness decreased, the ore-to-coke ratio decreased, and the central airflow developed. Only changing the ore batch weight affected the thickness and width of the ore-free zone and had a greater impact on the permeability of the ore-free zone. The greater the ore batch weight was, the worse the permeability, while changing the batch weight of both coke and ore mainly affected the thickness of the ore-free zone. The greater the batch weight of coke and ore was, the greater the thickness of the ore-free zone. In the case of changing only the angle of ore in the matrix, with the angle increasing, the ore-to-coke ratio around the ore-free zone decreased, the ore-to-coke ratio around the furnace wall increased, and the edge airflow was suppressed. In the case of changing the angle of coke and ore at the same time, with the simultaneous increase in both angles, the ore-free area was compressed in the direction of smaller charge segregation, the area with better permeability in the center of the furnace throat was reduced, and the central airflow was suppressed. Full article
(This article belongs to the Special Issue Metallurgical Process: Optimization and Control)
►▼ Show Figures

Figure 1

Figure 1
<p>Blast furnace burden surface measured diagram: (<b>a</b>) coke burden surface shape; (<b>b</b>) ore burden surface shape.</p> Full article ">Figure 2
<p>Phenomenon of the charge being blown up by the gas flow at the center of the furnace throat.</p> Full article ">Figure 3
<p>Schematic diagram of the parallel-hopper bell-less top.</p> Full article ">Figure 4
<p>Process of particles from generation to falling to the burden surface: (<b>a</b>) burden generation model; (<b>b</b>) charging system.</p> Full article ">Figure 5
<p>Coke burden surface and ore burden surface under base conditions: (<b>a</b>) coke simulated burden surface; (<b>b</b>) coke measured burden surface; (<b>c</b>) ore simulated burden surface; (<b>d</b>) ore measured burden surface.</p> Full article ">Figure 6
<p>The sampling area diagram of data analysis.</p> Full article ">Figure 7
<p>Shape of the burden surface of the ore-free zone at different burden line depths.</p> Full article ">Figure 8
<p>Distribution of burden in sampling area under different burden line depths.</p> Full article ">Figure 9
<p>Schematic diagram of the delineated sampling area.</p> Full article ">Figure 10
<p>Effect of different burden line depths on the ore-to-coke ratio within the ore-free zone.</p> Full article ">Figure 11
<p>Shape of the ore-free zone with different ore batch weights.</p> Full article ">Figure 12
<p>Burden distribution in the sampling area under different ore batch weights.</p> Full article ">Figure 13
<p>Shape of the ore-free zone with different coke and ore batch weights.</p> Full article ">Figure 14
<p>Burden distribution in the sampling area under different ore and coke batch weights.</p> Full article ">Figure 15
<p>Effect of the charge batch weight on the ore–coke ratio within the ore-free zone: (<b>a</b>) change in ore batch weight only; (<b>b</b>) change in coke and ore batch weight at the same time.</p> Full article ">Figure 16
<p>Effect of changing only the angle of the ore in the matrix on the shape of the ore-free zone.</p> Full article ">Figure 17
<p>Distribution of charge within the sampling zone in different fabric matrices under the condition that only the angle of the ore in the matrix is changed.</p> Full article ">Figure 18
<p>Effect of simultaneously changing the coke and ore angles in the matrix on the shape of the ore-free zone.</p> Full article ">Figure 19
<p>Distribution of charge in the sampling area under different burden matrices with simultaneous changes in coke and ore angles in the matrix.</p> Full article ">Figure 20
<p>Segregation phenomenon under different cloth matrix by changing the angle of coke and ore in the matrix: (<b>a</b>) movement of the charge in the chute with different burden matrices; (<b>b</b>) contours of the edge of the ore-free zone with different burden matrices.</p> Full article ">Figure 21
<p>Effect of the distribution angle on the ore-to-coke ratio within the ore-free zone: (<b>a</b>) only changing the ore angle; (<b>b</b>) changing both the coke and ore angles.</p> Full article ">
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