Iron and steel production plays an important role in both global and national economies and serves as the cornerstone of industrialization. Steel, primarily composed of iron, is central to this industry. In countries like Turkey, where ore resources are limited, steel production heavily relies on scrap metal melted using Electric Arc Furnaces or Induction Furnaces. Since this process requires intensive energy consumption, it is of great importance to accurately measure and manage energy consumption to improve the efficiency of production processes and support sustainability goals. This study aims to determine the main factors affecting energy consumption by examining the energy consumption in induction furnaces at Bilecik Iron and Steel Factory. Using econometric modelling and one-way analysis of variance (ANOVA), the study uses multiple linear regression analysis to estimate a suitable model. The ANOVA results reveal significant differences in energy consumption among induction furnaces. The main determinants identified include pouring time, tapping temperature, and billet quantity. Panel data analysis models were also used in this study to analyse the relationship between energy consumption and influencing factors across both time and cross-sectional dimensions (e.g., different furnaces). The findings provide actionable insights into reducing energy consumption and emphasize the importance of the study as a methodological example for similar energy estimation analyses in other steel production facilities. This study not only contributes to the optimization of operational efficiency but also highlights the importance of making inferences using statistical modelling to minimize energy use in industrial processes.

Energy consumption, Induction furnaces, Iron and steel, Regression analysis, Panel data analysis, ANOVA.