Modelling of phenomena affecting blast furnace burden permeability using the Discrete Element Method (DEM) – A review

Roeplal, Raïsa; Pang, Yusong; Adema, Allert T.; Stel, Jan van der; Schott, Dingena L.

doi:10.1016/j.powtec.2022.118161

Cited by 16 publications

(4 citation statements)

References 164 publications

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“…Coke functions not only as a reducing agent but also as a skeleton in the blast furnace [ 2 ]. The blast furnace is filled with furnace burden, molten slag, iron, and coke, which account for 1/3–1/2 of the furnace burden and determines the gas permeability [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Study on the High-Temperature Interaction between Coke and Iron Ores with Different Layer Thicknesses

Wang,

Du,

Diao

et al. 2024

Materials

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Coke plays a key role as the skeleton of the charge column in BF. The gas path formed by the coke layer in the BF has a decisive influence on gas permeability. At high temperatures, the interface between coke and ore undergoes a melting reaction of coke and a reduction reaction of ore. The better the reducibility of the ore, the more conducive it is to the coupling reaction of ore and coke. The melting loss reaction of coke becomes more intense, and the corresponding strength of coke will decrease, which will affect the permeability of the blast furnace and is not conducive to the smooth operation of the blast furnace. Especially with a deterioration in iron ore quality, BF operation faces severe challenges, which makes it necessary to find an effective way to strengthen BF operation. In this study, a melting-dropping furnace was used to develop and clarify the high-temperature interaction between coke and iron ores with different layer thicknesses. The influencing factors were studied by establishing a gas permeability mathematical model and observing the metallographic microscope images of samples after the coke solution loss reaction. The relationships between coke layer thickness, distribution of gas flow, and pressure drop were obtained. The results showed that, under certain conditions, the gas permeability property of a furnace burden has been improved after the coke layer thickness increased. Upon observing the size of coke particles at the interface reaction site, the degree of melting loss reaction can be determined. A smaller particle size indicates more melting loss reaction. A dripping eigenvalue for molten metal was introduced to evaluate the dynamic changes in the comprehensive dripping properties of molten metal of furnace burden, which showed that the dripping eigenvalue for the molten metal could deteriorate because of the unruly thickness and the coke layer thickness should be limited through considering the operational indicators of the blast furnace.

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Section: Introductionmentioning

confidence: 99%

Study on the High-Temperature Interaction between Coke and Iron Ores with Different Layer Thicknesses

Wang,

Du,

Diao

et al. 2024

Materials

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“…For example, CFD/DEM has been used for investigating the solid dynamics in a blast furnace [11,12] or the attrition/breakage in raceway [13]. Kuang et al [14] provides a comprehensive review of the modeling and simulation of the blast furnace raceway with TFM and CFD/DEM, and later Roeplal et al [15] focuses on CFD/DEM.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Analysis of the Forces Involved in a Pilot-Scale Blast Furnace Raceway Formation by Means of CFD/DEM Simulations

Romano,

Izard,

Fede

2024

Processes

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The CFD/DEM approach was used for investigating the forces playing a role in a furnace raceway formation and stability. The configuration is an actual pilot-scale hot blast furnace filled only with coke particles. In such a system, the raceway was unstable, with successively a growing phase and a collapse. The CFD/DEM numerical simulations were coupled with a core-shrinking model to mimic coke particle combustion. However, the kinetic reactions and heat transfers were not numerically predicted. Instead, the characteristic combustion timescale of one coke particle was imposed, and the combustion zone was adjusted to match the global combustion measured in the pilot-scale experiment. The results showed that the standard contact model was not enough to resist the pressure exerted by the granular weight on the raceway. However, the addition of a cohesive force, through the Johnson-Kendall-Roberts (JKR) model, allowed the qualitative reproduction of the gas pressure fluctuations and the collapse cycles in accordance with the experiment. A sensitivity analysis of the flow rate showed that CFD/DEM is able to reproduce quantitatively the time between two collapses, as observed in the experiment. Predicted raceway size and shapes are also in agreement with the experimental observations in the range of investigated parameters.

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“…These models can be broadly categorized into two groupsphysics-based models and data-driven models. In the case of physics-based models (Abhale et al, 2022(Abhale et al, , 2020Austin et al, 1997;Dong et al, 2007;Roeplal et al, 2023;Yu and Shen, 2022), mass, momentum and enthalpy balance equations are solved for the furnace to predict HMSi and other blast furnace performance indicators such as hot metal temperature, furnace permeability, fuel rate and productivity. In the case of data-driven models (Bhattacharya, 2005;Chuanhou Gao et al, 2011;Diniz et al, 2021;Gao et al, 2021;Gaopeng, 2011;Gao-peng et al, 2021bGao-peng et al, , 2021aJian et al, 2015;Li et al, 2018Li et al, , 2017Li et al, , 2013Liu et al, 2007;Nurkkala et al, 2011;Saxén and Pettersson, 2007;Saxén et al, 2016;Shi-hua and Jiu-sun, 2007;Tang et al, 2009;Wang, 2018;Wang et al, 2019Wang et al, , 2015Wang et al, , 2022Wang and Liu, 2011;Zeng et al, 2008;Zhao et al, 2020), an empirical relationship between silicon content and various raw material and process parameters is established using historical operations data of the furnace through statistical, machine learning and deep learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Machine learning based knowledge discovery and modeling of silicon content of molten iron from a blast furnace

Nistala,

Biswas,

Kumar

et al. 2024

Preprint

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It is necessary to maintain the silicon content of hot metal in a stipulated range to improve the productivity and energy consumption of a blast furnace. Significant research therefore went into the development of data-driven models to predict hot metal silicon content in real-time. However, these models use only a small subset of blast furnace variables that are chosen using prior process knowledge. As each blast furnace is unique in its operation, using pre-selected variables would lead to sub-optimal models. To address this, a machine learning based ensemble feature selection and modeling approach is proposed. In this approach, all the available furnace variables are ranked using multiple feature selection techniques based on their impact on silicon content. The individual ranks are combined to obtain an ensemble ranking of variables and the top variables in the ranking are used to build data-driven silicon prediction models. This approach is applied to an industrial blast furnace wherein 374 variables are used to obtain the ensemble ranking. While some of the top 100 variables in the ensemble ranking matched those that are commonly used in silicon predictions models, several new variables have also been identified. Silicon prediction models trained using the top 100 variables resulted in a hot rate of ~90% demonstrating the efficacy of the proposed approach. Real-time predictions from the models will enable blast furnace operators to control the silicon content without having to wait for laboratory results.

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Modelling of phenomena affecting blast furnace burden permeability using the Discrete Element Method (DEM) – A review

Cited by 16 publications

References 164 publications

Study on the High-Temperature Interaction between Coke and Iron Ores with Different Layer Thicknesses

Study on the High-Temperature Interaction between Coke and Iron Ores with Different Layer Thicknesses

Mechanical Analysis of the Forces Involved in a Pilot-Scale Blast Furnace Raceway Formation by Means of CFD/DEM Simulations

Machine learning based knowledge discovery and modeling of silicon content of molten iron from a blast furnace

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