Smart Energy Optimization and Adaptive Control of Electric Arc Furnaces in the Steel Industry using Digital Twin and Neural Networks

Document Type : Original Article

Author

Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

10.22034/abmir.2025.23151.1128

Abstract

This study proposes a novel framework for energy optimization and adaptive control of Electric Arc Furnaces (EAFs) in the steel industry, based on the integration of a digital twin model and adaptive neural networks. The main objective is to reduce energy consumption, enhance process stability, and improve control accuracy under varying operational conditions. To achieve this, a real-time updated digital twin of the steel melting process was first developed. Subsequently, adaptive neural networks were employed to estimate the nonlinear dynamics of the system and design an intelligent controller. The proposed system was implemented in the MATLAB/Simulink environment and evaluated under multiple operational scenarios. Simulation results demonstrate that the proposed approach achieves an average energy consumption reduction of 8.7%, a 24.3% improvement in transient response, and a 14.12% increase in power tracking accuracy compared to conventional controllers. These outcomes highlight the effectiveness of the proposed method in enhancing energy efficiency and operational stability in thermal processes within the steel industry and suggest its potential as a scalable model for smart system implementation in other industrial applications.

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References

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Applied and basic Machine intelligence research
Volume 3, Issue 2
December 2025
Pages 53-71

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