چکیده مقاله
Electric Arc Furnaces EAFs are indispensable in modern steelmaking due to their efficiency and ability to recycle scrap materials However, their high and fluctuating power consumption poses critical challenges to energy management and grid stability, especially under strict power constraints This paper proposes a novel multi objective optimization framework based on the Non dominated Sorting Genetic Algorithm II NSGA II to optimize EAF scheduling and address these challenges The framework explicitly considers three conflicting objectives: maximizing steel production, minimizing power fluctuations, and reducing furnace idle time Using a simulated steel plant with six EAFS operating under a 300 MW power limit over a 24 hour horizon, the study showcases the effectiveness of the proposed framework The simulation results demonstrate the framework's effectiveness, yielding a Pareto front that quantifies the trade offs between objectives For instance, an optimized solution achieved a maximum steel production of 8,100 tons with a power fluctuation of 15 7 MW standard deviation , while another solution significantly reduced fluctuation to 13 5 MW standard deviation at 7,200 tons production These findings provide a robust, adaptable, and efficient solution for energy intensive industries, enabling informed decision making to balance operational efficiency and grid stability under power limitations
کلیدواژهها
نویسندگان
Ali Parsai Kia
PhD Student in Mechanical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
Ali-Reza Naeimifard
Faculty Member, Department of Mechanical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
شیوه ارجاع
Parsai Kia, Ali and Naeimifard, Ali-Reza,1404,Multi-Objective Optimization of Electric Arc Furnace Scheduling for Enhanced Steel Production and Grid Stability using NSGA-II,The First National Conference on Applied Technologies in Mechanical Engineering (ATME2025),Ahvaz
ارائهشده در
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