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Three-dimensional computational fluid dynamics analysis of an electric submerged arc furnace.

ABSTRACT: A computational fluid dynamics (CFD) method is proposed to analyze the operation of a submerged electric arc furnace (SAF) used in ferronickel production. A three-dimensional mathematical model was used for the time-dependent solution of the fluid flow, heat transfer and electromagnetic phenomena. The slag's physical properties, which play a crucial role in the SAF operation, were previously determined using classical molecular dynamics simulations and empirical relationships. The analysis revealed that the main slag properties affecting SAF operation are density, viscosity and electrical conductivity-the latter two being mutually dependent. The high electrical conductivity values of the slag favor melting via the high Joule heat produced within the slag region. Calculation of the dimensionless Péclet and Reynolds numbers revealed that the slag velocities play a decisive role in heat transfer and further indicate that the slag flow is laminar. The average slag velocity calculated 0.0001 m/s with maxima in the vicinity of the electrodes.

SUBMITTER: Karalis K

PROVIDER: S-EPMC8417227 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Three-dimensional computational fluid dynamics analysis of an electric submerged arc furnace.

Karalis K K Karalis N N Karkalos N N Ntallis Ν Ν Antipas G S E GSE Xenidis A A

Scientific reports 20210903 1

A computational fluid dynamics (CFD) method is proposed to analyze the operation of a submerged electric arc furnace (SAF) used in ferronickel production. A three-dimensional mathematical model was used for the time-dependent solution of the fluid flow, heat transfer and electromagnetic phenomena. The slag's physical properties, which play a crucial role in the SAF operation, were previously determined using classical molecular dynamics simulations and empirical relationships. The analysis revea ...[more]

PMID: 34480043

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Project description:This study presents a data-driven assisted real-time optimization model which is an innovative approach to address the challenges posed by integrating Submerged Arc Furnace (SAF) systems with renewable energy sources, specifically photovoltaic (PV) and wind power, with modern intelligent energy terminals. Specifically, the proposed method is divided into two stages. The first stage is related to data-driven prediction for addressing local time-varying renewable energy and electricity market prices with predicted information, and the second stage uses an optimization model for real-time SAF dispatch. Connections between intelligent energy terminals, demand-side devices, and load management systems are established to enhance local renewable resource utilization. Additionally, mathematical formulations of the operating resistance in SAF are explored, and deep neuron networks are employed and modified for dynamic uncertainty prediction. The proposed approach is validated through a case study involving an intelligent energy terminal with a 12.5 MVA SAF system and 12 MW capacity renewable generators in an electricity market with fluctuating prices. The findings of this research underscore the efficacy of the proposed optimization model in reducing operational costs and enhancing the utilization of localized renewable energy generation. By integrating four distinct dissatisfaction coefficients into the optimization framework, we demonstrate the model's adaptability and efficiency. The application of the optimization strategy delineated herein results in the SAF system's profitability oscillating between $111 and $416 across various time intervals, contingent upon the coefficient settings. Remarkably, an aggregate daily loss recovery amounting to $1,906.84 can be realized during the optimization period. Such outcomes not only signify considerable economic advantages but also contribute to grid stability and the diminution of renewable energy curtailment, thereby underscoring the dual benefits of economic efficiency and sustainability in energy management practices.

Dataset Information

Three-dimensional computational fluid dynamics analysis of an electric submerged arc furnace.

Publications

Three-dimensional computational fluid dynamics analysis of an electric submerged arc furnace.

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