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基于遺傳算法的電爐載能值綜合優化

EAF carrying energy optimization based on the genetic algorithm

  • 摘要: 引入載能體的方法,統籌電爐爐料結構、電爐供氧、配碳和供電等多種因素,建立了電爐載能值綜合優化模型.模型約束復雜,采用線性規劃難以求解,本文采用遺傳算法進行求解.對BH1H、BHDDQ和SUS304鋼種進行能值計算.結果表明:在保證電爐出鋼鋼水化學成分、溫度和渣的堿度等指標符合要求的前提下,每爐鋼水能值分別降低了22.8%、21.4%和23.6%.

     

    Abstract: A carrying energy optimization model of an electric arc furnace(EAF) was proposed by introducing an energy carrier and considering the factors of EAF charging structures,oxygen supply,carbon addition and power supply.Due to the complexity of its constraints,the model could not be solved by linear programming.In this paper the model was solved by a genetic algorithm.The energy values of BH1H,BHDDQ and SUS304 steels were calculated by the model.The results showed that under the premise of ensuring the indicators of EAF molten steel such as chemical composition,temperature and slag basicity to meet the requirements,the energy values of the molten steel in each furnace were reduced by 22.8%,21.4% and 23.6% for BH1H,BHDDQ and SUS304 steels,respectively.

     

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