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18 pages, 5257 KiB

Open AccessFeature PaperArticle

Titanium Nitride (TiN) Germination and Growth during Vacuum Arc Remelting of a Maraging Steel

by Vincent Descotes, Thibault Quatravaux, Jean-Pierre Bellot, Sylvain Witzke and Alain Jardy

Metals 2020, 10(4), 541; https://doi.org/10.3390/met10040541 - 22 Apr 2020

Cited by 13 | Viewed by 3871

Abstract

During the processing of maraging steels, Titanium easily combines with Nitrogen to form nitride inclusions, known to be deleterious for fatigue properties of the alloy. According to thermodynamic calculations, the precipitation occurs during solidification of the vacuum arc remelted (VAR) ingot. A coupled [...] Read more.

During the processing of maraging steels, Titanium easily combines with Nitrogen to form nitride inclusions, known to be deleterious for fatigue properties of the alloy. According to thermodynamic calculations, the precipitation occurs during solidification of the vacuum arc remelted (VAR) ingot. A coupled model of titanium nitride (TiN) inclusion precipitation and vacuum remelting has been set-up to study the inclusion cleanliness of the ingot. The nitrogen content, nuclei numeral density and solidification time appear as the key factors which control the inclusion size. Full article

(This article belongs to the Special Issue Mathematical Modeling and Simulation in Ironmaking and Steelmaking)

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17 pages, 2026 KiB

Open AccessFeature PaperArticle

Multiscale Simulation of Non-Metallic Inclusion Aggregation in a Fully Resolved Bubble Swarm in Liquid Steel

by Jean-Sébastien Kroll-Rabotin, Matthieu Gisselbrecht, Bernhard Ott, Ronja May, Jochen Fröhlich and Jean-Pierre Bellot

Metals 2020, 10(4), 517; https://doi.org/10.3390/met10040517 - 17 Apr 2020

Cited by 12 | Viewed by 2309

Abstract

Removing inclusions from the melt is an important task in metallurgy with critical impact on the quality of the final alloy. Processes employed with this purpose, such as flotation, crucially depend on the particle size. For small inclusions, the aggregation kinetics constitute the [...] Read more.

Removing inclusions from the melt is an important task in metallurgy with critical impact on the quality of the final alloy. Processes employed with this purpose, such as flotation, crucially depend on the particle size. For small inclusions, the aggregation kinetics constitute the bottleneck and, hence, determine the efficiency of the entire process. If particles smaller than all flow scales are considered, the flow can locally be replaced by a plane shear flow. In this contribution, particle interactions in plane shear flow are investigated, computing the fully resolved hydrodynamics at finite Reynolds numbers, using a lattice Boltzmann method with an immersed boundary method. Investigations with various initial conditions, several shear values and several inclusion sizes are conducted to determine collision efficiencies. It is observed that although finite Reynolds hydrodynamics play a significant role in particle collision, statistical collision efficiency barely depends on the Reynolds number. Indeed, the particle size ratio is found to be the prevalent parameter. In a second step, modeled collision dynamics are applied to particles tracked in a fully resolved bubbly flow, and collision frequencies at larger flow scale are derived. Full article

(This article belongs to the Special Issue Mathematical Modeling and Simulation in Ironmaking and Steelmaking)

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16 pages, 6237 KiB

Open AccessArticle

A 2D Multiphase Model of Drop Behavior during Electroslag Remelting

by Jérémy Chaulet, Abdellah Kharicha, Sylvain Charmond, Bernard Dussoubs, Stéphane Hans, Menghuai Wu, Andreas Ludwig and Alain Jardy

Metals 2020, 10(4), 490; https://doi.org/10.3390/met10040490 - 08 Apr 2020

Cited by 4 | Viewed by 2847

Abstract

Electroslag remelting is a process extensively used to produce metallic ingots with high quality standards. During the remelting operation, liquid metal droplets fall from the electrode through the liquid slag before entering the liquid pool of the secondary ingot. To better understand the [...] Read more.

Electroslag remelting is a process extensively used to produce metallic ingots with high quality standards. During the remelting operation, liquid metal droplets fall from the electrode through the liquid slag before entering the liquid pool of the secondary ingot. To better understand the process and help to optimize the operating condition choice, a 2D axisymmetric multiphase model of the slag domain has been developed using a two fluid Eulerian approach. During their fall, droplets hydrodynamic interactions are calculated thanks to an appropriate drag law. Influence of droplets on the electromagnetic field and on the slag hydrodynamics is discussed, as well as their heat exchange with the slag. Even with a small volume fraction, the droplets influence is noticeable. The present investigation shows that small droplets have a large influence on the slag hydrodynamics, due to a great momentum exchange. However heat transfer is more influenced by large drops, which are found to be relatively far from the thermal equilibrium with the slag phase. Full article

(This article belongs to the Special Issue Mathematical Modeling and Simulation in Ironmaking and Steelmaking)

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12 pages, 3158 KiB

Open AccessArticle

Carbon Impact Mitigation of the Iron Ore Direct Reduction Process through Computer-Aided Optimization and Design Changes

by Rami Béchara, Hamzeh Hamadeh, Olivier Mirgaux and Fabrice Patisson

Metals 2020, 10(3), 367; https://doi.org/10.3390/met10030367 - 12 Mar 2020

Cited by 17 | Viewed by 8419

Abstract

The steel industry is known to have one of the highest environmental impacts on the industrial sector, especially in terms of CO₂ emissions. The so-called direct reduction route, which makes use of reformed natural gas along with top gas recycling to reduce [...] Read more.

The steel industry is known to have one of the highest environmental impacts on the industrial sector, especially in terms of CO₂ emissions. The so-called direct reduction route, which makes use of reformed natural gas along with top gas recycling to reduce iron oxide pellets with H₂ and CO, is responsible for lower CO₂ emissions than the classic blast furnace route and is currently under development. The present article focuses on the direct reduction process and discusses means to further decrease the CO₂ emission rate. A set of 10 operating parameters were simultaneously changed according to computer-aided optimization. The results provide about 15% improvement over original emissions for comparable output values. Full article

(This article belongs to the Special Issue Mathematical Modeling and Simulation in Ironmaking and Steelmaking)

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13 pages, 10816 KiB

Open AccessArticle

Experimental and Numerical Investigation of Reaction Behavior of Carbon Composite Briquette in Blast Furnace

by Huiqing Tang, Yanjun Sun and Tao Rong

Metals 2020, 10(1), 49; https://doi.org/10.3390/met10010049 - 25 Dec 2019

Cited by 7 | Viewed by 2293

Abstract

The application of carbon composite briquette (CCB) is considered to be an efficient method for achieving low-energy and low-CO₂-emission blast furnace (BF) operations. In this research, a combined experimental and numerical study was conducted on the CCB reaction behavior in BF. [...] Read more.

The application of carbon composite briquette (CCB) is considered to be an efficient method for achieving low-energy and low-CO₂-emission blast furnace (BF) operations. In this research, a combined experimental and numerical study was conducted on the CCB reaction behavior in BF. The CCB used in this study had a composition of 20.10 wt.% carbon, 29.70 wt.% magnetite, 39.70 wt.% wüstite, and 1.57 wt.% metallic iron. Using the prepared CCB samples, isotherm reduction tests under a simulated BF atmosphere (CO-CO₂-N₂) were conducted and a reaction model was developed. Subsequently, the reaction behavior of CCB along the mid-radial solid descending path in an actual BF of 2500 m³ was analyzed by numerical simulations based on the experimental findings and the previous results of comprehensive BF modeling. The results of the experiments showed that the CCB model predictions agreed well with the experimental measurements. With respect to the BF, the results of the numerical simulations indicated that, along the path, before the CCB temperature reached 1000 K, the CCB was reduced by CO in the BF gas; when its temperature was in the range from 1000 to 1130 K, it underwent self-reduction and contributed both CO and CO₂ to the BF gas; when its temperature was above 1130 K, it only presented carbon gasification. Moreover, these results also revealed that the reduction of iron oxide and the gasification of carbon inside the CCB proceeded under an uneven mode. The uneven radial distribution of the local reduction fraction and local carbon conversion were evident in the self-reducing stage of the CCB. Full article

(This article belongs to the Special Issue Mathematical Modeling and Simulation in Ironmaking and Steelmaking)

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53 pages, 1778 KiB

Open AccessArticle

Using Statistical Modeling to Predict the Electrical Energy Consumption of an Electric Arc Furnace Producing Stainless Steel

by Leo S. Carlsson, Peter B. Samuelsson and Pär G. Jönsson

Metals 2020, 10(1), 36; https://doi.org/10.3390/met10010036 - 24 Dec 2019

Cited by 17 | Viewed by 3764

Abstract

The non-linearity of the Electric Arc Furnace (EAF) process and the correlative behavior between the process variables impose challenges that have to be considered if one aims to create a statistical model that is relevant and useful in practice. In this regard, both [...] Read more.

The non-linearity of the Electric Arc Furnace (EAF) process and the correlative behavior between the process variables impose challenges that have to be considered if one aims to create a statistical model that is relevant and useful in practice. In this regard, both the statistical modeling framework and the statistical tools used in the modeling pipeline must be selected with the aim of handling these challenges. To achieve this, a non-linear statistical modeling framework known as Artificial Neural Networks (ANN) has been used to predict the Electrical Energy (EE) consumption of an EAF producing stainless steel. The statistical tools Feature Importance (FI), Distance Correlation (dCor) and Kolmogorov–Smirnov (KS) tests are applied to investigate the most influencing input variables as well as reasons behind model performance differences when predicting the EE consumption on future heats. The performance, measured as kWh per heat, of the best model was comparable to the performance of the best model reported in the literature while requiring substantially fewer input variables. Full article

(This article belongs to the Special Issue Mathematical Modeling and Simulation in Ironmaking and Steelmaking)

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16 pages, 5427 KiB

Open AccessArticle

Prediction of Central Carbon Segregation in Continuous Casting Billet Using A Regularized Extreme Learning Machine Model

by Leilei Zou, Jiangshan Zhang, Qing Liu, Fanzheng Zeng, Jun Chen and Min Guan

Metals 2019, 9(12), 1312; https://doi.org/10.3390/met9121312 - 05 Dec 2019

Cited by 14 | Viewed by 3232

Abstract

Central carbon segregation is a typical internal defect of continuous cast steel billets. Real-time and accurate carbon segregation prediction is of great significance for lean control of the production quality in continuous casting processes. In this paper, a data-driven regularized extreme learning machine [...] Read more.

Central carbon segregation is a typical internal defect of continuous cast steel billets. Real-time and accurate carbon segregation prediction is of great significance for lean control of the production quality in continuous casting processes. In this paper, a data-driven regularized extreme learning machine (R-ELM) model is proposed for the prediction of carbon segregation index (CSI). To improve model performance, outliers in industrial data were eliminated by means of boxplot tool. Besides, Pearson correlation combined with grey relational analysis (GRA) was conducted to avoid multicollinearity and redundancy in input variables. The new model shows potential to evaluate online quality of steel billets. When predictive errors were within ±0.03 and ±0.025, the prediction accuracy of the R-ELM model was 94% and 89%, respectively, which was higher than that of the multiple linear regression (MLR) model and ELM model. Moreover, the effects of several key continuous casting process parameters on CSI were investigated based on the predictions of the R-ELM model via response surface analysis. The conclusions are consistent with the metallurgical mechanism, and the predictive values of the R-ELM model agree well with experimental values, which further verifies the correctness and generalization ability of the R-ELM model. Full article

(This article belongs to the Special Issue Mathematical Modeling and Simulation in Ironmaking and Steelmaking)

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15 pages, 4550 KiB

Open AccessArticle

Initial Transfer Behavior and Solidification Structure Evolution in a Large Continuously Cast Bloom with a Combination of Nozzle Injection Mode and M-EMS

by Pu Wang, Zhuang Zhang, Zhan-peng Tie, Meng Qi, Peng Lan, Shao-xiang Li, Zhan-bing Yang and Jia-quan Zhang

Metals 2019, 9(10), 1083; https://doi.org/10.3390/met9101083 - 08 Oct 2019

Cited by 15 | Viewed by 2030

Abstract

A three-dimensional numerical model combining electromagnetic field, fluid flow, heat transfer, and solidification has been established to study the effect of nozzle injection mode and mold electromagnetic stirring (M-EMS) on the internal quality of a continuously cast bloom. The model is validated by [...] Read more.

A three-dimensional numerical model combining electromagnetic field, fluid flow, heat transfer, and solidification has been established to study the effect of nozzle injection mode and mold electromagnetic stirring (M-EMS) on the internal quality of a continuously cast bloom. The model is validated by measured data of the magnetic flux density along the stirrer center line. According to the simulation and experimental results, M-EMS can introduce a horizontal swirling flow ahead of the solidification front, promoting the superheat dissipation of molten steel and columnar to equiaxed transition (CET). As the stirring current increases from 0 to 800 A, the superheat at the mold exit in the bloom center decreases by 1.9 K for the single-port nozzle case and 3.8 K for the five-port nozzle case. The resulting increase in the equiaxed crystal ratio is about 5.65% and 4.06%, respectively. In comparison, the injection mode shows a more significant influence on the heat transfer and solidification structure in the bloom under the present casting conditions. The superheat at the mold exit in the bloom center decreases by 5.1–7.7 K as the injection mode changes from a single-port nozzle to a five-port nozzle, and the increase in the equiaxed crystal ratio ranges between 14.8% and 17%. It is found that the flow velocity of the molten steel in front of the solidification interface for the five-port nozzle is higher than that for the single-port nozzle regardless of the M-EMS power. The washing effect here reinforces both the heat exchange through the solidification interface and the dendrite re-melting or fragmenting, stimulating the formation of an equiaxed crystal at the bloom center. In addition, it is observed that both the central shrinkage and carbon segregation have decreased with the five-port nozzle plus M-EMS. This suggests that the combined application of a five-port nozzle and M-EMS can effectively improve the internal quality of large bloom castings. Full article

(This article belongs to the Special Issue Mathematical Modeling and Simulation in Ironmaking and Steelmaking)

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17 pages, 9506 KiB

Open AccessArticle

Fine Description of Multi-Process Operation Behavior in Steelmaking-Continuous Casting Process by a Simulation Model with Crane Non-Collision Constraint

by Jianping Yang, Jiangshan Zhang, Min Guan, Yujie Hong, Shan Gao, Weida Guo and Qing Liu

Metals 2019, 9(10), 1078; https://doi.org/10.3390/met9101078 - 05 Oct 2019

Cited by 12 | Viewed by 2911

Abstract

The fine description of multi-process operation behavior in steelmaking-continuous casting process is an important foundation for the improvement of production scheduling in steel plants. With sufficient consideration on non-collision movements among cranes, a dynamic simulation model is established by Plant Simulation software to [...] Read more.

The fine description of multi-process operation behavior in steelmaking-continuous casting process is an important foundation for the improvement of production scheduling in steel plants. With sufficient consideration on non-collision movements among cranes, a dynamic simulation model is established by Plant Simulation software to describe the operation behavior of multi-process in the steelmaking-continuous casting process of lacking refining span. The design and implement of simulation are illustrated based on a typical workshop layout of “one converter-one refining furnace-one caster”. The method to avoid the collisions between adjacent cranes is represented in detail. To validate the availability of this model, an actual steel plant without refining span is studied, and simulation experiments are conducted by introducing actual production plans as simulation instances. The simulated findings agree well with the actual results of interest, including the total completed times of simulation instances, the turnover number of ladles, and the transfer times of heats among different processes. Hence, the proposed model can reliably simulate the multi-process operation behavior in steelmaking-continuous casting process. Full article

(This article belongs to the Special Issue Mathematical Modeling and Simulation in Ironmaking and Steelmaking)

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18 pages, 4661 KiB

Open AccessArticle

Modelling of Mass and Thermal Balance and Simulation of Iron Sintering Process with Biomass

by Jaroslav Legemza, Mária Fröhlichová, Róbert Findorák and Martina Džupková

Metals 2019, 9(9), 1010; https://doi.org/10.3390/met9091010 - 16 Sep 2019

Cited by 6 | Viewed by 7798

Abstract

This paper specifies the mathematical and physical modelling of the iron sintering process in laboratory conditions. The aim is to get the simplest approach (using thermodynamic software “HSC Chemistry”, version 9, Outokumpu Research Oy, Pori, Finland) that allows one to predict the output [...] Read more.

This paper specifies the mathematical and physical modelling of the iron sintering process in laboratory conditions. The aim is to get the simplest approach (using thermodynamic software “HSC Chemistry”, version 9, Outokumpu Research Oy, Pori, Finland) that allows one to predict the output parameters based on the initial composition analysis. As a part of the application of mathematical modelling, a mass and thermal balance of combustion of carbonaceous fuels (including biomass) and a mass and thermal balance of high-temperature sintering of an agglomeration charge were determined. The objective of the paper was to point out the advantages of modelling using thermodynamic software and apply the results into a simulation of the sintering process. The outcome of mathematical modelling correlates to the outcome of physical modelling for fuel combustion and the agglomerate production in a laboratory sintering pan. The energy required to reach the desired sintering temperatures and acquire the standard quality of agglomerate was calculated using 4.97% of coke breeze. In a real experiment with the laboratory sintering pan, 4.35% of coke was used. When a biomass fuel with a lower calorific value (lignin) is used in the agglomeration charge, the amount of fuel has to be increased to 5.52% (with 20% substitution of coke). This paper also aimed at predicting methodological tools and defining thermodynamic conditions for creating an interactive simulation. In addition, kinetics should be considered to improve the predicting capabilities of the current model and therefore in further research it will be required to optimise the computational program pursuant to the results of the kinetics experiments. Full article

(This article belongs to the Special Issue Mathematical Modeling and Simulation in Ironmaking and Steelmaking)

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23 pages, 637 KiB

Open AccessArticle

Radar Detection-Based Modeling in a Blast Furnace: A Prediction Model of Burden Surface Descent Speed

by Jiuzhou Tian, Akira Tanaka, Qingwen Hou and Xianzhong Chen

Metals 2019, 9(5), 609; https://doi.org/10.3390/met9050609 - 25 May 2019

Cited by 6 | Viewed by 2826

Abstract

The distribution of burden layers is a vital factor that affects the production of a blast furnace. Radars are advanced instruments that can provide the detection results of the burden surface shape inside a blast furnace in real time. To better estimate the [...] Read more.

The distribution of burden layers is a vital factor that affects the production of a blast furnace. Radars are advanced instruments that can provide the detection results of the burden surface shape inside a blast furnace in real time. To better estimate the burden layer thicknesses through improving the prediction accuracy of the burden descent during charging periods, an innovative data-driven model for predicting the distribution of the burden surface descent speed is proposed. The data adopted were from the detection results of an operating blast furnace, collected using a mechanical swing radar system. Under a kinematic continuum modeling mechanism, the proposed model adopts a linear combination of Gaussian radial basis functions to approximate the equivalent field of burden descent speed along the burden surface radius. A proof of the existence and uniqueness of the prediction solution is given to guarantee that the predicted radial profile of the burden surface can always be calculated numerically. Compared with the plain data-driven descriptive model, the proposed model has the ability to better characterize the variability in the radial distribution of burden descent speed. In addition, the proposed model provides prediction results of higher accuracy for both the future surface shape and descent speed distribution. Full article

(This article belongs to the Special Issue Mathematical Modeling and Simulation in Ironmaking and Steelmaking)

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Review

Jump to: Research

33 pages, 380 KiB

Open AccessReview

Predicting the Electrical Energy Consumption of Electric Arc Furnaces Using Statistical Modeling

by Leo S. Carlsson, Peter B. Samuelsson and Pär G. Jönsson

Metals 2019, 9(9), 959; https://doi.org/10.3390/met9090959 - 01 Sep 2019

Cited by 25 | Viewed by 5216

Abstract

Statistical modeling, also known as machine learning, has gained increased attention in part due to the Industry 4.0 development. However, a review of the statistical models within the scope of steel processes has not previously been conducted. This paper reviews available statistical models [...] Read more.

Statistical modeling, also known as machine learning, has gained increased attention in part due to the Industry 4.0 development. However, a review of the statistical models within the scope of steel processes has not previously been conducted. This paper reviews available statistical models in the literature predicting the Electrical Energy (EE) consumption of the Electric Arc Furnace (EAF). The aim was to structure published data and to bring clarity to the subject in light of challenges and considerations that are imposed by statistical models. These include data complexity and data treatment, model validation and error reporting, choice of input variables, and model transparency with respect to process metallurgy. A majority of the models are never tested on future heats, which essentially renders the models useless in a practical industrial setting. In addition, nonlinear models outperform linear models but lack transparency with regards to which input variables are influencing the EE consumption prediction. Some input variables that heavily influence the EE consumption are rarely used in the models. The scrap composition and additive materials are two such examples. These observed shortcomings have to be correctly addressed in future research applying statistical modeling on steel processes. Lastly, the paper provides three key recommendations for future research applying statistical modeling on steel processes. Full article

(This article belongs to the Special Issue Mathematical Modeling and Simulation in Ironmaking and Steelmaking)

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