Special Issue "Process Modeling in Pyrometallurgical Engineering"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Other Topics".

Deadline for manuscript submissions: 1 December 2019.

Special Issue Editors

Guest Editor
Prof. Henrik Saxen Website E-Mail
Thermal and Flow Engineering Laboratory, Abo Akademi University, 20500 Turku, Finland
Interests: process systems engineering; modeling; optimization; energy systems; iron- and steelmaking
Guest Editor
Prof. Dr. Marco A. Ramírez-Argáez Website E-Mail
Departamento de Metalurgia, Facultad de Química, Universidad Nacional Autónoma de México (UNAM), Edificio “D” Circuito de los Institutos s/n, Cd. Universitaria, Del. Coyoacán, 04510 México D.F., México
Interests: mathematical and physical modeling of materials processing; CFD analysis; optimization techniques; steelmaking; aluminum degasification; sand casting
Guest Editor
Prof. Dr. Alberto N. Conejo Website E-Mail
University of Science and Technology Beijing (USTB), 30 Xueyuan Rd, Haidian Qu, Beijing Shi, 100083, China
Interests: slag/metal reactions; fluid flow phenomena in ladles; EAF steelmaking; gas/solid reactions
Guest Editor
Dr. Abhishek Dutta Website E-Mail
KU Leuven, Campus Groep T, Andreas Vesaliusstraat 13, 3000 Leuven, Belgium
KU Leuven, Departement Materiaalkunde (MTM), Kasteelpark Arenberg 44 - box 2450, 3001 Leuven, Belgium
Interests: metal–slag kinetics; gas/metal/slag interaction; population balances; reactor modeling; process intensification

Special Issue Information

Dear Colleagues,

The metallurgical industry today faces several main challenges. The most pressing ones are using less virgin raw materials and lowering the environmental impacts of the processes, in particular, reducing CO2 emissions. With a growing world population and standard of living, it is not possible to base production on recycled materials only. Therefore, existing processes must be improved and optimized in order to minimize material demand, losses, and emissions, while simultaneously keeping them profitable. A key issue for achieving these goals is to intensify and improve the processes by innovative use of mathematical simulations and optimizations.    

Pyrometallurgical processes involve complex interactions of phenomena distributed in space and time, at high temperatures, and in a hostile environment, which makes measurements of the conditions difficult or sometimes impossible. Still, deep knowledge of the behavior is needed in order to master and control the conditions appropriately. The coexistence and interaction of many phases is characteristic of the unit processes in this industry. The solid phases are often particulate, such as the raw materials used in pelletizing, sintering, and in the blast furnace, exibiting a complex flow behavior. In steelmaking, hydrodynamic and kinetic interactions between dispersed bubbles and droplets of metal or slag are of fundamental importance. Insight into the phenomena is a prerequisite for process intensification and better metal recovery through a systematic phase separation, but the mechanisms are complicated and coupled. The behavior of metallic droplets in molten slag is linked with slag–metal reaction kinetics, but also with heat transfer and turbulent mixing.

A quantitative estimation of the efficiency of any process and improvement can be made by a systematic approach, applying experiments and modeling. As an example, physical (water) modelling can be used to scale-down (i.e., simplify the industrial experimental situation), so as to closely observe the process. Combined with proper numerical simulations, for example, based on computational fluid dynamics (CFD), a deeper insight can be gained on the interactions at high temperatures, which is the basis for proposing improvements in process design and operation. Physical and mathematical modeling are the modern tools that are used to explore the reaction mechanisms and process improvements in more detail.

The goal of this Special Issue on “Process Modeling in Metallurgical Engineering” is to highlight the recent advances in the development and application of process modeling in metallurgical engineering, and how modeling and simulation can be applied to improve and intensify the processes in the metallurgical industry. The ultimate goal of the Issue is to receive contributions on the modeling and simulation of the pyrometallurgical processes in order to show the advancements in the field and the tools that may be used to understand, control, and optimize current processes, and to design new ones. Topics to be considered include, but are not limited to, the following:

  • Transport phenomena and modeling unit processes in pyrometallurgy
  • Modeling of slag–metal interaction and related phenomena
  • Multiphase flows in metallurgical processes (e.g., in blast furnace, direct reduction, BOF, EAF, LMF, RH, continuous casting, etc.): experimental and modeling approaches
  • Modelling techniques for studying metallurgical phenomena at elevated temperatures
  • Process modeling, supervision, and control in pyrometallurgy
  • Innovative process developments in the metallurgical industry
  • Development of sustainable pyrometallurgical processes

Prof. Dr. Henrik Saxén
Prof. Dr. Marco A. Ramírez-Argáez
Prof. Dr. Alberto N. Conejo
Dr. Abhishek Dutta
Guest editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Please note that for papers submitted after 31 December 2019 an APC of 1400 CHF applies. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mathematical modeling
  • computational fluid dynamics
  • metal–slag interaction
  • population balance
  • physical modeling
  • process metallurgy
  • process optimization
  • sustainable development

Published Papers (7 papers)

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Research

Open AccessFeature PaperArticle
Discrete Element Method (DEM) and Experimental Studies of the Angle of Repose and Porosity Distribution of Pellet Pile
Processes 2019, 7(9), 561; https://doi.org/10.3390/pr7090561 - 23 Aug 2019
Abstract
The lumpy zone in a blast furnace is composed of piles formed naturally during burden charging. The properties of this zone have significant effects on the blast furnace operation, including heat and mass transfer, chemical reactions and gas flow. The properties of the [...] Read more.
The lumpy zone in a blast furnace is composed of piles formed naturally during burden charging. The properties of this zone have significant effects on the blast furnace operation, including heat and mass transfer, chemical reactions and gas flow. The properties of the layers mainly include the angle of repose and porosity distribution. This paper introduces two methods, the Discharging Method and the Lifting Method, to study the influence of the packing method on the angle of repose of the pile. The relationships of the angle of repose and porosity with physical parameters are also investigated. The porosity distribution in the bottom of a pile shows a decreasing trend from the region below the apex to the center. The coordination number of the particles is employed to explain this change. The maximum of the frequency distribution of it was found to show a negative correlation to the static friction coefficient, but becomes insensitive to the parameter as the static friction coefficient increases above 0.6. Full article
(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)
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Open AccessArticle
Numerical Simulation of Effects of Different Operational Parameters on the Carbon Solution Loss Ratio of Coke inside Blast Furnace
Processes 2019, 7(8), 528; https://doi.org/10.3390/pr7080528 - 09 Aug 2019
Abstract
Carbon solution loss reaction of coke gasification is one of the most important reasons for coke deterioration and degradation in a blast furnace. It also affects the permeability of gas and fluids, as well as stable working conditions. In this paper, a three [...] Read more.
Carbon solution loss reaction of coke gasification is one of the most important reasons for coke deterioration and degradation in a blast furnace. It also affects the permeability of gas and fluids, as well as stable working conditions. In this paper, a three dimensional model is established based on the operational parameters of blast furnace B in Bayi Steel. The model is then used to calculate the effects of oxygen enrichment, coke oven gas injection, and steel scrap charging on the carbon solution loss ratio of coke in the blast furnace. Results show that the carbon solution loss ratio of coke gasification for blast furnace B is almost 20% since the results of a model are probably only indicative. The oxygen enrichment and the addition of steel scrap can reduce the carbon solution loss ratio with little effect on the working condition. However, coke oven gas injection increases the carbon solution loss ratio. Therefore, coke oven gas should not be injected into the blast furnace unless the quality of the coke is improved. Full article
(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)
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Open AccessFeature PaperArticle
Principal Component Analysis of Blast Furnace Drainage Patterns
Processes 2019, 7(8), 519; https://doi.org/10.3390/pr7080519 - 07 Aug 2019
Abstract
Monitoring and control of the blast furnace hearth is critical to achieve the required production levels and adequate process operation, as well as to extend the campaign length. Because of the complexity of the draining, the outflows of iron and slag may progress [...] Read more.
Monitoring and control of the blast furnace hearth is critical to achieve the required production levels and adequate process operation, as well as to extend the campaign length. Because of the complexity of the draining, the outflows of iron and slag may progress in different ways during tapping in large blast furnaces. To categorize the hearth draining behavior, principal component analysis (PCA) was applied to two extensive sets of process data from an operating blast furnace with three tapholes in order to develop an interpretation of the outflow patterns. Representing the complex outflow patterns in low dimensions made it possible to study and illustrate the time evolution of the drainage, as well as to detect similarities and differences in the performance of the tapholes. The model was used to explain the observations of other variables and factors that are known to be affected by, or affect, the state of the hearth, such as stoppages, liquid levels, and tap duration. Full article
(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)
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Open AccessArticle
Flow Behavior and Hot Processing Map of GH4698 for Isothermal Compression Process
Processes 2019, 7(8), 491; https://doi.org/10.3390/pr7080491 - 01 Aug 2019
Abstract
An in-depth understanding of the flow behaviors of materials deformed at high temperatures is of paramount significance. However, insufficient research on the nickel-based GH4698 alloy has resulted in inaccurate material flow prediction or even cracking in the practical billet opening of GH4698 large [...] Read more.
An in-depth understanding of the flow behaviors of materials deformed at high temperatures is of paramount significance. However, insufficient research on the nickel-based GH4698 alloy has resulted in inaccurate material flow prediction or even cracking in the practical billet opening of GH4698 large forgings. In this study, hot compressions were performed at 950–1150 °C and 0.001–3 s−1. Single-peaked strain-stress curves were obtained under various conditions, owing to dislocation motions in dynamic recrystallizations. The Arrhenius model was formulated to accurately describe the flow stress evolutions and the mean prediction error of the flow stress was 5.90%. Processing maps were constructed at various hot working conditions. It was found that the hot working ability of GH4698 markedly decreased under lower temperatures (950–1080 °C) and higher strain rates (0.1–3 s−1). Optimal thermal processing parameters were suggested. In sum, this study systematically investigated the flow behaviors and hot working ability of GH4698 in isothermal compressions. Full article
(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)
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Open AccessArticle
Experimental Study on Precipitation Behavior of Spinels in Stainless Steel-Making Slag under Heating Treatment
Processes 2019, 7(8), 487; https://doi.org/10.3390/pr7080487 - 01 Aug 2019
Cited by 1
Abstract
The stability of chromium in stainless steel slag has a positive correlation with spinel particle size and a negative correlation with the calcium content of the spinel. The effect of heating time on the precipitation of spinel crystals in the CaO-SiO2-MgO-Al [...] Read more.
The stability of chromium in stainless steel slag has a positive correlation with spinel particle size and a negative correlation with the calcium content of the spinel. The effect of heating time on the precipitation of spinel crystals in the CaO-SiO2-MgO-Al2O3-Cr2O3-FeO system was investigated in the laboratory. Scanning electron microscopy with energy-dispersive and X-ray diffraction were adopted to observe the microstructure, test the chemical composition, and determine the mineral phases of synthetic slags, and FactSage7.1 was applied to calculate the crystallization process of the molten slag. The results showed that the particle size of the spinel crystals increased from 9.42 to 10.73 μm, the calcium content in the spinel crystals decreased from 1.38 at% to 0.78 at%, and the content of chromium in the spinel crystal increased from 16.55 at% to 22.78 at% with an increase in the heating time from 0 min to 120 min at 1450 °C. Furthermore, the species of spinel minerals remained constant. Therefore, an extension in the heating time is beneficial for improving the stability of chromium in stainless steel slag. Full article
(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)
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Open AccessArticle
Physical Simulation of Molten Steel Homogenization and Slag Entrapment in Argon Blown Ladle
Processes 2019, 7(8), 479; https://doi.org/10.3390/pr7080479 - 24 Jul 2019
Abstract
Argon stirring is one of the most widely used metallurgical methods in the secondary refining process as it is economical and easy, and also an important refining method in clean steel production. Aiming at the issue of poor homogeneity of composition and temperature [...] Read more.
Argon stirring is one of the most widely used metallurgical methods in the secondary refining process as it is economical and easy, and also an important refining method in clean steel production. Aiming at the issue of poor homogeneity of composition and temperature of a bottom argon blowing ladle molten steel in a Chinese steel mill, a 1:5 water model for 110 t ladle was established, and the mixing time and interface slag entrainment under the different conditions of injection modes, flow rates and top slag thicknesses were investigated. The flow dynamics of argon plume in steel ladle was also discussed. The results show that, as the bottom blowing argon flow rate increases, the mixing time of ladle decreases; the depth of slag entrapment increases with the argon flow rate and slag thickness; the area of slag eyes decreases with the decrease of the argon flow rate and increase of slag thickness. The optimum argon flow rate is between 36–42 m3/h, and the double porous plugs injection mode should be adopted at this time. Full article
(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)
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Open AccessArticle
Gaussian Process-Based Hybrid Model for Predicting Oxygen Consumption in the Converter Steelmaking Process
Processes 2019, 7(6), 352; https://doi.org/10.3390/pr7060352 - 08 Jun 2019
Abstract
Oxygen is one of the most important energies used in converter steelmaking processes of integrated iron and steel works. Precisely forecasting oxygen consumption before processing can benefit process control and energy optimization. This paper assumes there is a linear relationship between the oxygen [...] Read more.
Oxygen is one of the most important energies used in converter steelmaking processes of integrated iron and steel works. Precisely forecasting oxygen consumption before processing can benefit process control and energy optimization. This paper assumes there is a linear relationship between the oxygen consumption and input materials, and random noises are caused by other unmeasurable materials and unobserved reactions. Then, a novel hybrid prediction model integrating multiple linear regression (MLR) and Gaussian process regression (GPR) is introduced. In the hybrid model, the MLR method is developed to figure the global trend of the oxygen consumption, and the GPR method is applied to explore the local fluctuation caused by noise. Additionally, to accelerate the computational speed on the practical data set, a K-means clustering method is devised to respectively train a number of GPR models. The proposed hybrid model is validated with the actual data collected from an integrated iron and steel work in China, and compared with benchmark prediction models including MLR, artificial neural network, support vector machine and standard GPR. The forecasting results indicate that the suggested model is able to not only produce satisfactory point forecasts, but also estimate accurate probabilistic intervals. Full article
(This article belongs to the Special Issue Process Modeling in Pyrometallurgical Engineering)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1. Prof. Ari Jokilaakso from  Aalto University

2. Dr. Ville-Valtteri Visuri from University of Oulu

3.  Dr. L. Zhang from Swansea University

 

 

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