{"title":"提高电弧炉炼钢效率:使用非支配排序遗传算法 II 的多目标优化方法","authors":"Xiaoyu Yi, Qiang Yue, Zhihe Dou, Qingcai Bu","doi":"10.1002/srin.202400370","DOIUrl":null,"url":null,"abstract":"To realize the overall optimization of electric arc furnace (EAF) steelmaking system, a multi‐objective optimization model including smelting cost, energy consumption per ton of steel, and carbon emission per ton of steel is established. The model is optimized by multi‐objective genetic algorithm to improve the charging structure. At the same time, the data in the optimal solution set are used to analyze the influence of the change of scrap ratio on smelting cost, carbon emission per ton of steel, and smelting cycle. According to the actual working conditions and the demand of steel plant, the optimized results are selected. Compared with the actual production data, the proportion of scrap steel increases to 50.9%, the ratio of molten iron decreases to 38.8%, the smelting cost per ton of steel decreases by 12 Yuan, the energy consumption per ton of steel decreases by 4%, the carbon emission per ton of steel significantly decreases by 13%, and the smelting cycle is shortened by 2 min, but at the cost of increasing the power consumption per ton of steel. The optimized results and the analysis of the change of scrap ratio provide reference for the optimization of EAF steelmaking system.","PeriodicalId":21929,"journal":{"name":"steel research international","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing Efficiency in Electric Arc Furnace Steelmaking: A Multi‐Objective Optimization Approach Using the Non‐Dominated Sorting Genetic Algorithm II\",\"authors\":\"Xiaoyu Yi, Qiang Yue, Zhihe Dou, Qingcai Bu\",\"doi\":\"10.1002/srin.202400370\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To realize the overall optimization of electric arc furnace (EAF) steelmaking system, a multi‐objective optimization model including smelting cost, energy consumption per ton of steel, and carbon emission per ton of steel is established. The model is optimized by multi‐objective genetic algorithm to improve the charging structure. At the same time, the data in the optimal solution set are used to analyze the influence of the change of scrap ratio on smelting cost, carbon emission per ton of steel, and smelting cycle. According to the actual working conditions and the demand of steel plant, the optimized results are selected. Compared with the actual production data, the proportion of scrap steel increases to 50.9%, the ratio of molten iron decreases to 38.8%, the smelting cost per ton of steel decreases by 12 Yuan, the energy consumption per ton of steel decreases by 4%, the carbon emission per ton of steel significantly decreases by 13%, and the smelting cycle is shortened by 2 min, but at the cost of increasing the power consumption per ton of steel. The optimized results and the analysis of the change of scrap ratio provide reference for the optimization of EAF steelmaking system.\",\"PeriodicalId\":21929,\"journal\":{\"name\":\"steel research international\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"steel research international\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1002/srin.202400370\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"METALLURGY & METALLURGICAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"steel research international","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1002/srin.202400370","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METALLURGY & METALLURGICAL ENGINEERING","Score":null,"Total":0}
Enhancing Efficiency in Electric Arc Furnace Steelmaking: A Multi‐Objective Optimization Approach Using the Non‐Dominated Sorting Genetic Algorithm II
To realize the overall optimization of electric arc furnace (EAF) steelmaking system, a multi‐objective optimization model including smelting cost, energy consumption per ton of steel, and carbon emission per ton of steel is established. The model is optimized by multi‐objective genetic algorithm to improve the charging structure. At the same time, the data in the optimal solution set are used to analyze the influence of the change of scrap ratio on smelting cost, carbon emission per ton of steel, and smelting cycle. According to the actual working conditions and the demand of steel plant, the optimized results are selected. Compared with the actual production data, the proportion of scrap steel increases to 50.9%, the ratio of molten iron decreases to 38.8%, the smelting cost per ton of steel decreases by 12 Yuan, the energy consumption per ton of steel decreases by 4%, the carbon emission per ton of steel significantly decreases by 13%, and the smelting cycle is shortened by 2 min, but at the cost of increasing the power consumption per ton of steel. The optimized results and the analysis of the change of scrap ratio provide reference for the optimization of EAF steelmaking system.
期刊介绍:
steel research international is a journal providing a forum for the publication of high-quality manuscripts in areas ranging from process metallurgy and metal forming to materials engineering as well as process control and testing. The emphasis is on steel and on materials involved in steelmaking and the processing of steel, such as refractories and slags.
steel research international welcomes manuscripts describing basic scientific research as well as industrial research. The journal received a further increased, record-high Impact Factor of 1.522 (2018 Journal Impact Factor, Journal Citation Reports (Clarivate Analytics, 2019)).
The journal was formerly well known as "Archiv für das Eisenhüttenwesen" and "steel research"; with effect from January 1, 2006, the former "Scandinavian Journal of Metallurgy" merged with Steel Research International.
Hot Topics:
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