The optimization of industrial processes is an interesting strategy to mitigate the environmental impact of productive activities. It involves applying multidisciplinary knowledge to improve the efficiency of industrial processes, thereby reducing the consumption of natural resources, pollutant emissions, and material waste. Exergy analysis has been extensively addressed in the literature, with numerous studies focused on this topic. The most prevalent research areas include analyzing industrial processes, optimizing energy generation and distribution systems, and assessing energy efficiency in transportation systems and buildings. The literature provides various exergy analysis studies conducted for electric arc furnaces (EAF) used in steel production. However, there is a need for more research focused on applying exergy analysis to the ladle furnace (LF) refining process, which represents the second stage of secondary steel refining in the industry. A Python program using Jupyter Notebook was developed to calculate steel refinement s mass, energy, and exergy balances in a ladle furnace. Thermodynamic data were obtained from the SSUB3 database of Thermo-Calc. A semi-integrated steel mill provided the actual data used, ensuring the results relevance and validity. The energy and exergy efficiencies found were 92.9 percent and 90.1 percent, respectively. These efficiency values are surprising, as they reveal that the furnace under analysis is already optimized. Optimizations were conducted to examine variations in argon s mass and inlet temperature, as well as electrode active power. The parameters variations revealed minor impacts on process efficiency, providing insights for effective improvements.