Development of 2D Steady-State Mathematical Model for Blast Furnace Using OpenFOAM®

Abhale, P.B.; Nag, Samik; Bapat, Y.; Kulkarni, A.; Nurni, Viswanathan; Padmapal,

doi:10.1007/s11663-022-02610-6

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…The predicted temperature of lime discharged from both the shafts are compared with the measured values in Figure 9. The fluctuations in measured temperatures of the cooled solids (lime) as opposed to predicted values are presumably due to thermal channelling phenomena (Wonchala and Wynnyckyj 1987; Abhale et al 2022a, 2022b), which causes the solids leaving the kiln to have non-uniform exit temperatures at a given time. Since, thermal channelling is a 2-dimensional phenomenon, implementation of the same is beyond the scope of the present work, which must be efficient in computation time for real-time implementation.…”

Section: Results and Validationmentioning

confidence: 95%

A mathematical model of a twin-shaft parallel flow regenerative lime kiln

Jagnade,

Nayak,

Korath

et al. 2023

Mineral Processing and Extractive Metallurgy: Transactions of t

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Lime is an important flux used in the iron and steel-making processes. Tata Steel Jamshedpur uses a twin-shaft Parallel Flow Regenerative type kiln to calcine limestone. Previously, the kiln was operated based on the temperature measurement at only three locations. This led to suboptimal kiln performance and difficulty in maintaining lime quality. A thermal digital twin was developed using first principles to address these issues. A set of unsteady-state partial-differential equations representing the thermophysical phenomena is solved numerically using the finite volume method. The model was validated with the measured flue gas temperature and lime temperatures. The model is implemented online with the real-time operating parameters to estimate the temperature profile of solid and gas and the degree of conversion of limestone. This real-time visibility enables the operators to optimize the process and achieve lime conversion up to 94%, resulting in energy savings of 31.8 GJ/ day and stable lime quality.

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Section: Results and Validationmentioning

confidence: 95%

A mathematical model of a twin-shaft parallel flow regenerative lime kiln

Jagnade,

Nayak,

Korath

et al. 2023

Mineral Processing and Extractive Metallurgy: Transactions of t

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“…The original codes were not parallelised and there was also room for improvements in the numerical methods used. These drawbacks were later eliminated in the development of 'BlaSim', a 2-D model based on OpenFOAM (Abhale et al 2019), developed by Tata Steel. The OpenFOAM framework provided efficient solvers and discretization schemes for the solution of the arising PDEs.…”

Section: Comprehensive 2-d Modelsmentioning

confidence: 99%

Numerical modelling of blast furnace – Evolution and recent trends

Abhale

Nurni

Saxén

2020

Mineral Processing and Extractive Metallurgy

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In a journey of centuries, the ironmaking blast furnace has gone through many technological changes, which made it a fit-for-purpose till today. However, for the first time in history, its existence is being challenged by other iron making processes, due to its high carbon footprint compared to others. Many researchers are working on adapting the existing blast furnace process to meet the new challenges. Modelling has played a crucial role in new adaptations of the process. The present review has been dedicated to historical developments of blast furnace modelling. The models are broadly classified into comprehensive, zone-specific, and data-driven models. The comprehensive models are further classified into lumped models, 1-D, 2-D and 3-D steady state and transient models, and CFD-DEM models. For the purpose of brevity, the zone-specific models are exemplified by burden distribution models only. Finally, developments in data-driven models are discussed, before some general conclusions are presented.

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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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Development of 2D Steady-State Mathematical Model for Blast Furnace Using OpenFOAM®

Cited by 8 publications

References 21 publications

A mathematical model of a twin-shaft parallel flow regenerative lime kiln

A mathematical model of a twin-shaft parallel flow regenerative lime kiln

Numerical modelling of blast furnace – Evolution and recent trends

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

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