The Experts below are selected from a list of 159 Experts worldwide ranked by ideXlab platform
K S Raju - One of the best experts on this subject based on the ideXlab platform.
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predicting the performance of Submerged Arc Furnace with varied raw material combinations using artificial neural network
Journal of Materials Processing Technology, 2007Co-Authors: Veerendra Singh, Vilas Tathavadkar, Mohan S Rao, K S RajuAbstract:Abstract Raw materials play a vital role in the ferrochrome production using Submerged Arc Furnace route. Optimized combination of different raw materials can improve the performance of Furnace and minimize the power consumption. This process carries numerous process complexities as well as feed variation, which make it difficult to model mathematically. Artificial neural network known as a black box approach is attempted to predict the effect of various raw materials (pellets, briquettes, hard lumps, friable lumps, coke and quartzite) on the performance of Submerged Arc Furnace by incorporating a production capability index (PCI). Production capability index is a ratio of the daily production and the maximum production achieved by the Furnace in the ideal conditions. A detailed statistical analysis was carried on plant data to study relationship of raw material and Furnace performance. In the first step of the study, the non-linear relationship between the raw material inputs and PCI is tried to predict by multivariable linear regression. Further feed forward back propagation neural network with three different learning algorithms were tried to improve the prediction accuracy (conjugant gradient decent, Levenberg–Marquardt optimization and resilient back propagation). Radial basis neural networks were also tried but no significant improvement was found in the performance prediction. The correlation coefficient is considered as a accuracy measure, and found that correlation between predicted and actual values were 0.64 for multilinear regression which was improved 0.70, 0.71 for radial basis neural network and feed forward neural network with resilient back propagation learning algorithm. Comparative analysis has been done among statistical analysis, neural network structures and the actual values of production capability index.
Gudrun Saevarsdottir - One of the best experts on this subject based on the ideXlab platform.
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Comparative Study of AC and DC Solvers Based on Current and Power Distributions in a Submerged Arc Furnace
Metallurgical and Materials Transactions B, 2020Co-Authors: Yonatan A. Tesfahunegn, Thordur Magnusson, M. Tangstad, Gudrun SaevarsdottirAbstract:This work discusses 3D models of current distribution in a three-phase Submerged Arc Furnace that contains several components, such as electrodes, central Arcs, craters, crater walls, and side Arcs that connect electrodes and crater walls. A complete modeling approach requires time-dependent modeling of the AC electromagnetic fields and current distribution, while an approximation using a static DC approach enables a significant reduction in computational time. By comparing results for current and power distributions inside an industrial Submerged Arc Furnace from the AC and DC solvers of the ANSYS Maxwell module, the merits and limitations of using the simpler and faster DC approach are estimated. The conclusion is that although effects such as skin effect and proximity are lost with the DC approach, the difference in the location of energy dissipation is within a 6 pct margin. The given inaccuracies introduced with an assumption about Furnace configuration and physical properties are significantly more important for the overall result. Unless inductive effects are of particular interest, DC may often be sufficient.
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The Effect of Frequency on Current Distributions Inside Submerged Arc Furnace
2018 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), 2018Co-Authors: Yonatan A. Tesfahunegn, Gudrun Saevarsdottir, Thordur Magnusson, Merete TangstadAbstract:This work presents computations of current distribution and heat generation inside an industrial Submerged Arc Furnace. A 3D model has been developed in ANSYS Fluent that solves Maxwell’s equations based on scalar and vector potential approach that are treated as transport equations. In this paper, the approach is described in detail and numerical simulations are performed on an industrial three-phase Submerged Arc Furnace. The effect of frequency on the current distributions and heat generations in electrodes are presented. The results show that at lower frequencies nonuniform current distribution is only due to proximity effect. Whereas at higher frequencies the nonuniform current distribution is due to the combined effect of skin and proximity effects, causing higher localized heat generation.
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the effect of pitch circle diameter of electrodes on current distributions in Submerged Arc Furnace
IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, 2018Co-Authors: Yonatan A. Tesfahunegn, Merete Tangstad, Thordur Magnusson, Gudrun SaevarsdottirAbstract:This work presents computations of electric current distributions inside an industrial Submerged Arc Furnace. A 3D model has been developed in ANSYS Fluent that solves Maxwell’s equations based on scalar and vector potential approach that are treated as transport equations. In this paper, the approach is described in detail and numericalsimulations are performed on an industrial three-phase Submerged Arc Furnace. The current distributions within electrodes due to a change in pitch circle diameter of electrodes are presented. The results show that proximity effect decreases as the electrodes apart further.
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dynamic current distribution in the electrodes of Submerged Arc Furnace using scalar and vector potentials
International Conference on Computational Science, 2018Co-Authors: Yonatan A. Tesfahunegn, Merete Tangstad, Thordur Magnusson, Gudrun SaevarsdottirAbstract:This work presents computations of electric current distributions inside an industrial Submerged Arc Furnace. A 3D model has been developed in ANSYS Fluent that solves Maxwell’s equations based on scalar and vector potentials approach that are treated as transport equations. In this paper, the approach is described in detail and numerical simulations are performed on an industrial three-phase Submerged Arc Furnace. The current distributions within electrodes due to skin and proximity effects are presented. The results show that the proposed method adequately models these phenomena.
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effect of carbide configuration on the current distribution in Submerged Arc Furnaces for silicon production a modelling approach
TMS Annual Meeting & Exhibition, 2018Co-Authors: Yonatan A. Tesfahunegn, Merete Tangstad, Thordur Magnusson, Gudrun SaevarsdottirAbstract:Current distribution is critical for good operation of Submerged Arc Furnaces for silicon production. Control systems do not offer this information as it is not directly measureable, but metallurgists operate Furnaces based on experienced interpretation of available data. A number of recent dig-outs of industrial Furnaces has expanded available information on location dependent charge properties, thus enabling numerical models with reasonably realistic domain configurations. This has the potential to enhance understanding of critical process parameters allowing more accurate Furnace control. This work presents computations of electric current distributions inside an industrial Submerged Arc Furnace for silicon production. A 3D model has been developed in ANSYS Fluent using electric potential solver. Electrode, Arc, crater, crater wall, and side Arc that connects electrode and crater wall are considered for each phase. In this paper the current distributions in electrode, Arc and crater wall for different configurations and thickness of the crater walls are presented. The side-Arcs are modelled as either a single concentrated Arc, or a smeared out Arc, in order to capture extreme cases. The main result is that side Arc configuration is more important for the fraction of the current passing through the crater wall than the carbide thickness. The current fraction bypassing the main Arc through the charge is highly influenced by the ease of contact between electrode and conducting charge material. Qualitatively, the results are in a good agreement with previously published results from literature.
Veerendra Singh - One of the best experts on this subject based on the ideXlab platform.
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beneficiation and agglomeration process to utilize low grade ferruginous manganese ore fines
International Journal of Mineral Processing, 2011Co-Authors: Veerendra Singh, Tamal K Ghosh, Y Ramamurthy, Vilas TathavadkarAbstract:Abstract Characterisation, beneficiation and agglomeration studies were carried out to develop a utilization strategy for typical Indian low grade manganese ore fines. The major mineral phases found are pyrolusite, hematite, goethite, clay, feldspar and quartz. QEMSCAN and Sink–Float studies suggested that 40% of manganese minerals are in liberated form, whereas 30% are locked with iron minerals. Classification followed by two-stage high intensity magnetic separation (1.7 & 1.1 Tesla) process can recover 35–40% material of ferromanganese grade with 47–49% Mn recovery. The recovered material was briquetted adding molasses (7%), cement (3%) and bentonite (1%) to use in the Submerged Arc Furnace for metallurgical applications. A cost effective process flow sheet has been developed to utilize these fines.
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predicting the performance of Submerged Arc Furnace with varied raw material combinations using artificial neural network
Journal of Materials Processing Technology, 2007Co-Authors: Veerendra Singh, Vilas Tathavadkar, Mohan S Rao, K S RajuAbstract:Abstract Raw materials play a vital role in the ferrochrome production using Submerged Arc Furnace route. Optimized combination of different raw materials can improve the performance of Furnace and minimize the power consumption. This process carries numerous process complexities as well as feed variation, which make it difficult to model mathematically. Artificial neural network known as a black box approach is attempted to predict the effect of various raw materials (pellets, briquettes, hard lumps, friable lumps, coke and quartzite) on the performance of Submerged Arc Furnace by incorporating a production capability index (PCI). Production capability index is a ratio of the daily production and the maximum production achieved by the Furnace in the ideal conditions. A detailed statistical analysis was carried on plant data to study relationship of raw material and Furnace performance. In the first step of the study, the non-linear relationship between the raw material inputs and PCI is tried to predict by multivariable linear regression. Further feed forward back propagation neural network with three different learning algorithms were tried to improve the prediction accuracy (conjugant gradient decent, Levenberg–Marquardt optimization and resilient back propagation). Radial basis neural networks were also tried but no significant improvement was found in the performance prediction. The correlation coefficient is considered as a accuracy measure, and found that correlation between predicted and actual values were 0.64 for multilinear regression which was improved 0.70, 0.71 for radial basis neural network and feed forward neural network with resilient back propagation learning algorithm. Comparative analysis has been done among statistical analysis, neural network structures and the actual values of production capability index.
Vilas Tathavadkar - One of the best experts on this subject based on the ideXlab platform.
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development of cold bonded chromite pellets for ferrochrome production in Submerged Arc Furnace
Isij International, 2013Co-Authors: Srinivas Dwarapudi, Vilas Tathavadkar, Chenna B Rao, T Sandeep K Kumar, Tamal K Ghosh, Mark B DenysAbstract:Pelletizing of Indian chromite ores is more challenging due to their high refractory nature. High Cr/Fe ratio and high MgO content in these ores demand high firing temperatures and longer firing cy ...
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beneficiation and agglomeration process to utilize low grade ferruginous manganese ore fines
International Journal of Mineral Processing, 2011Co-Authors: Veerendra Singh, Tamal K Ghosh, Y Ramamurthy, Vilas TathavadkarAbstract:Abstract Characterisation, beneficiation and agglomeration studies were carried out to develop a utilization strategy for typical Indian low grade manganese ore fines. The major mineral phases found are pyrolusite, hematite, goethite, clay, feldspar and quartz. QEMSCAN and Sink–Float studies suggested that 40% of manganese minerals are in liberated form, whereas 30% are locked with iron minerals. Classification followed by two-stage high intensity magnetic separation (1.7 & 1.1 Tesla) process can recover 35–40% material of ferromanganese grade with 47–49% Mn recovery. The recovered material was briquetted adding molasses (7%), cement (3%) and bentonite (1%) to use in the Submerged Arc Furnace for metallurgical applications. A cost effective process flow sheet has been developed to utilize these fines.
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predicting the performance of Submerged Arc Furnace with varied raw material combinations using artificial neural network
Journal of Materials Processing Technology, 2007Co-Authors: Veerendra Singh, Vilas Tathavadkar, Mohan S Rao, K S RajuAbstract:Abstract Raw materials play a vital role in the ferrochrome production using Submerged Arc Furnace route. Optimized combination of different raw materials can improve the performance of Furnace and minimize the power consumption. This process carries numerous process complexities as well as feed variation, which make it difficult to model mathematically. Artificial neural network known as a black box approach is attempted to predict the effect of various raw materials (pellets, briquettes, hard lumps, friable lumps, coke and quartzite) on the performance of Submerged Arc Furnace by incorporating a production capability index (PCI). Production capability index is a ratio of the daily production and the maximum production achieved by the Furnace in the ideal conditions. A detailed statistical analysis was carried on plant data to study relationship of raw material and Furnace performance. In the first step of the study, the non-linear relationship between the raw material inputs and PCI is tried to predict by multivariable linear regression. Further feed forward back propagation neural network with three different learning algorithms were tried to improve the prediction accuracy (conjugant gradient decent, Levenberg–Marquardt optimization and resilient back propagation). Radial basis neural networks were also tried but no significant improvement was found in the performance prediction. The correlation coefficient is considered as a accuracy measure, and found that correlation between predicted and actual values were 0.64 for multilinear regression which was improved 0.70, 0.71 for radial basis neural network and feed forward neural network with resilient back propagation learning algorithm. Comparative analysis has been done among statistical analysis, neural network structures and the actual values of production capability index.
Yonatan A. Tesfahunegn - One of the best experts on this subject based on the ideXlab platform.
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Comparative Study of AC and DC Solvers Based on Current and Power Distributions in a Submerged Arc Furnace
Metallurgical and Materials Transactions B, 2020Co-Authors: Yonatan A. Tesfahunegn, Thordur Magnusson, M. Tangstad, Gudrun SaevarsdottirAbstract:This work discusses 3D models of current distribution in a three-phase Submerged Arc Furnace that contains several components, such as electrodes, central Arcs, craters, crater walls, and side Arcs that connect electrodes and crater walls. A complete modeling approach requires time-dependent modeling of the AC electromagnetic fields and current distribution, while an approximation using a static DC approach enables a significant reduction in computational time. By comparing results for current and power distributions inside an industrial Submerged Arc Furnace from the AC and DC solvers of the ANSYS Maxwell module, the merits and limitations of using the simpler and faster DC approach are estimated. The conclusion is that although effects such as skin effect and proximity are lost with the DC approach, the difference in the location of energy dissipation is within a 6 pct margin. The given inaccuracies introduced with an assumption about Furnace configuration and physical properties are significantly more important for the overall result. Unless inductive effects are of particular interest, DC may often be sufficient.
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The Effect of Frequency on Current Distributions Inside Submerged Arc Furnace
2018 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), 2018Co-Authors: Yonatan A. Tesfahunegn, Gudrun Saevarsdottir, Thordur Magnusson, Merete TangstadAbstract:This work presents computations of current distribution and heat generation inside an industrial Submerged Arc Furnace. A 3D model has been developed in ANSYS Fluent that solves Maxwell’s equations based on scalar and vector potential approach that are treated as transport equations. In this paper, the approach is described in detail and numerical simulations are performed on an industrial three-phase Submerged Arc Furnace. The effect of frequency on the current distributions and heat generations in electrodes are presented. The results show that at lower frequencies nonuniform current distribution is only due to proximity effect. Whereas at higher frequencies the nonuniform current distribution is due to the combined effect of skin and proximity effects, causing higher localized heat generation.
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the effect of pitch circle diameter of electrodes on current distributions in Submerged Arc Furnace
IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, 2018Co-Authors: Yonatan A. Tesfahunegn, Merete Tangstad, Thordur Magnusson, Gudrun SaevarsdottirAbstract:This work presents computations of electric current distributions inside an industrial Submerged Arc Furnace. A 3D model has been developed in ANSYS Fluent that solves Maxwell’s equations based on scalar and vector potential approach that are treated as transport equations. In this paper, the approach is described in detail and numericalsimulations are performed on an industrial three-phase Submerged Arc Furnace. The current distributions within electrodes due to a change in pitch circle diameter of electrodes are presented. The results show that proximity effect decreases as the electrodes apart further.
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dynamic current distribution in the electrodes of Submerged Arc Furnace using scalar and vector potentials
International Conference on Computational Science, 2018Co-Authors: Yonatan A. Tesfahunegn, Merete Tangstad, Thordur Magnusson, Gudrun SaevarsdottirAbstract:This work presents computations of electric current distributions inside an industrial Submerged Arc Furnace. A 3D model has been developed in ANSYS Fluent that solves Maxwell’s equations based on scalar and vector potentials approach that are treated as transport equations. In this paper, the approach is described in detail and numerical simulations are performed on an industrial three-phase Submerged Arc Furnace. The current distributions within electrodes due to skin and proximity effects are presented. The results show that the proposed method adequately models these phenomena.
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effect of carbide configuration on the current distribution in Submerged Arc Furnaces for silicon production a modelling approach
TMS Annual Meeting & Exhibition, 2018Co-Authors: Yonatan A. Tesfahunegn, Merete Tangstad, Thordur Magnusson, Gudrun SaevarsdottirAbstract:Current distribution is critical for good operation of Submerged Arc Furnaces for silicon production. Control systems do not offer this information as it is not directly measureable, but metallurgists operate Furnaces based on experienced interpretation of available data. A number of recent dig-outs of industrial Furnaces has expanded available information on location dependent charge properties, thus enabling numerical models with reasonably realistic domain configurations. This has the potential to enhance understanding of critical process parameters allowing more accurate Furnace control. This work presents computations of electric current distributions inside an industrial Submerged Arc Furnace for silicon production. A 3D model has been developed in ANSYS Fluent using electric potential solver. Electrode, Arc, crater, crater wall, and side Arc that connects electrode and crater wall are considered for each phase. In this paper the current distributions in electrode, Arc and crater wall for different configurations and thickness of the crater walls are presented. The side-Arcs are modelled as either a single concentrated Arc, or a smeared out Arc, in order to capture extreme cases. The main result is that side Arc configuration is more important for the fraction of the current passing through the crater wall than the carbide thickness. The current fraction bypassing the main Arc through the charge is highly influenced by the ease of contact between electrode and conducting charge material. Qualitatively, the results are in a good agreement with previously published results from literature.
