Mathematical modeling of electrode motion control in an electric arc furnace based on fuzzy logic

Relevance. Electric arc furnaces are among the most energy-intensive units in the technological chain of metallurgical enterprises. Approximately 50% of the total electricity consumption is attributed to processes carried out in electric arc furnaces. The steady global increase in demand for steel products, the limited availability of energy resources, and the low energy efficiency of existing electric arc furnaces highlight the need for innovative management solutions in this field. Aim. Improvement of electrode movement control in an electric arc furnace, development of an intelligent control system based on fuzzy logic, and enhancement of control efficiency using this system, taking into account the inconsistency and high dynamics inherent in the technological process. Methods. Mathematical modeling based on the Mamdani fuzzy logic model. The main technological parameters were fuzzified, and corresponding membership functions (Gaussian and triangular) were defined. Decision-making was automated, and defuzzification was performed using the center of gravity method. Additionally, visual analysis was conducted using 3D graphs and heat maps. Results. The research successfully enabled effective control of the electrode movement speed in the EAF-30 within the range of –4 to +3 cm/s (in this case, positive values indicate electrode movement toward the molten metal, whereas negative values indicate movement in the opposite direction). It was established that when the arc current exceeds 37 kA, electrodes should be lifted at a speed of –4 cm/s. With the help of the constructed decision table and the mechanism of fuzzy logic the electrode movement speed could be determined based on arc current and voltage. As a result, arc parameters were maintained within normal ranges, ensuring process stability. Overall technological efficiency improved due to a 5,1% reduction in energy consumption and a decrease in electrode failures, demonstrating the practical value of implementing digital intelligent control systems in electric arc furnaces.