OPTIMIZATION OF WALL MATERIAL FOR MINIMUM HEAT LOSSES FOR INDUCTION FURNACE BY FEA

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Mr. Nitin B. Bhujbal, Aditya H. Jadhav, Ms. Pratima C. Bhosale, Ms. Asha K. Dudhal

Abstract

Induction furnaces are commonly employed for melting metals, and to prevent losses, silica ramming mass is typically used as a refractory material. Consequently, it is crucial to optimize the thickness of the refractory material. This research paper focuses on optimizing the wall thickness and material to minimize heat losses during the iron melting process. Calculations involve determining thermal properties and physical parameters for heat loss calculations. Theoretical heat loss and temperature distribution calculations are then compared to actual measurements obtained from an existing furnace, which experiences significant losses. To perform the optimization, ANSYS Software was utilized, providing more accurate results in a shorter timeframe. The problem was approached in an axi-symmetric manner since it exhibits symmetry about the Y-Axis. Ultimately, it was concluded that optimizing the thickness of the refractory material could reduce losses by 35%, while optimizing the thermal properties could lead to a reduction of 73% in losses. By employing the proper thickness and material properties for the refractory material in the induction furnace, losses can be ultimately reduced by 70%.


 

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How to Cite
Mr. Nitin B. Bhujbal, Aditya H. Jadhav, Ms. Pratima C. Bhosale, Ms. Asha K. Dudhal. (2023). OPTIMIZATION OF WALL MATERIAL FOR MINIMUM HEAT LOSSES FOR INDUCTION FURNACE BY FEA. International Journal of Innovations in Engineering Research and Technology, 70-81. https://repo.ijiert.org/index.php/ijiert/article/view/3717
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How to Cite

Mr. Nitin B. Bhujbal, Aditya H. Jadhav, Ms. Pratima C. Bhosale, Ms. Asha K. Dudhal. (2023). OPTIMIZATION OF WALL MATERIAL FOR MINIMUM HEAT LOSSES FOR INDUCTION FURNACE BY FEA. International Journal of Innovations in Engineering Research and Technology, 70-81. https://repo.ijiert.org/index.php/ijiert/article/view/3717