بهینه‌سازی هوشمند انرژی و کنترل تطبیقی کوره‌های قوس الکتریکی در صنعت فولاد با استفاده از دوقلوی دیجیتال و شبکه‌های عصبی

نوع مقاله : مقاله پژوهشی

نویسنده

مربی دانشکده مهندسی برق و کامپیوتر، دانشگاه تبریز، تبریز، ایران

10.22034/abmir.2025.23151.1128

چکیده

در این پژوهش، یک چارچوب نوین برای بهینه‌سازی مصرف انرژی و کنترل تطبیقی عملکرد کوره‌های قوس الکتریکی در صنعت فولاد ارائه‌شده است که بر پایه تلفیق دوقلوی دیجیتال و شبکه‌های عصبی تطبیقی طراحی گردیده است. هدف اصلی، کاهش مصرف انرژی، افزایش پایداری فرآیند و ارتقاء دقت کنترل در شرایط عملیاتی متغیر است. بدین منظور، ابتدا یک مدل دوقلوی دیجیتال از فرآیند ذوب فولاد توسعه‌یافته و به‌صورت برخط به‌روزرسانی شده است. سپس، از شبکه‌های عصبی تطبیقی برای تخمین غیرخطی دینامیک فرآیند و طراحی کنترل‌کننده بهره گرفته شد. پیاده‌سازی سیستم در محیط MATLAB/Simulink انجام شد و عملکرد آن در سناریوهای عملیاتی گوناگون ارزیابی گردید. نتایج شبیه‌سازی نشان می‌دهد که روش پیشنهادی موجب کاهش متوسط ۷/۸ درصدی مصرف انرژی، بهبود 3/24 درصدی در پاسخ گذرا و افزایش 12/14 درصدی دقت ردیابی توان نسبت به کنترل‌کننده‌های کلاسیک شده است. این دستاوردها حاکی از اثربخشی روش ارائه‌شده در بهبود بهره‌وری انرژی و پایداری فرآیندهای حرارتی در صنعت فولاد است و می‌تواند به‌عنوان الگویی برای پیاده‌سازی سیستم‌های هوشمند مشابه در سایر فرآیندهای صنعتی در نظر گرفته شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

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

نویسنده [English]

  • Sara Mahmoudi Rashid
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Industrial Artificial Intelligence
  • Electric Arc Furnace
  • Energy Consumption Optimization
  • Adaptive Control
  • Learning Neural Network
  • Steel Industry Efficiency
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