基于遗传算法的电炉载能值综合优化  

EAF carrying energy optimization based on the genetic algorithm

作　　者：艾立翔[1] 汪红兵[2] 徐安军[1] 杜曦[3] 

机构地区：[1]北京科技大学冶金与生态工程学院,北京100083 [2]北京科技大学计算机与通信工程学院,北京100083 [3]安徽大学计算机科学与技术学院,合肥230601

出　　处：《北京科技大学学报》2012年第4期450-456,共7页Journal of University of Science and Technology Beijing

基　　金："十一五"国家科技支撑计划重大项目(2006BAE03A07)

摘　　要：引入载能体的方法,统筹电炉炉料结构、电炉供氧、配碳和供电等多种因素,建立了电炉载能值综合优化模型.模型约束复杂,采用线性规划难以求解,本文采用遗传算法进行求解.对BH1H、BHDDQ和SUS304钢种进行能值计算.结果表明:在保证电炉出钢钢水化学成分、温度和渣的碱度等指标符合要求的前提下,每炉钢水能值分别降低了22.8%、21.4%和23.6%.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.

关 键 词：不锈钢 电炉 能量利用 优化 遗传算法 

分 类 号：TF741.5[冶金工程—钢铁冶金] TF34