冶金自动化
冶金自動化
야금자동화
METALLURGICAL INDUSTRY AUTOMATION
2014年
5期
22-26
,共5页
杨凌志%朱荣%秦云广%宋水根%马国宏%魏光升
楊凌誌%硃榮%秦雲廣%宋水根%馬國宏%魏光升
양릉지%주영%진운엄%송수근%마국굉%위광승
聚类算法%终点碳%电弧炉%脱碳模型
聚類算法%終點碳%電弧爐%脫碳模型
취류산법%종점탄%전호로%탈탄모형
clustering algorithm%end-point carbon%electric arc furnace%decarburization model
为了实现对电弧炉冶炼过程碳质量分数的预测，根据炼钢过程的碳氧反应机理建立电弧炉脱碳模型。在此基础上采用数据挖掘技术中的k-means聚类算法对电弧炉炼钢历史数据进行分析，选取8个影响终点碳质量分数的因素，得出不同冶炼情况下的聚类结果。通过计算当前炉次与聚类结果加权欧氏距离，将相似度高的聚类结果炉次作为当前炉次的预测参考炉次，最终实现对电弧炉终点碳质量分数的预测。仿真结果表明钢水碳质量分数预报的命中率在75%以上，模型具有较高的预报精度。
為瞭實現對電弧爐冶煉過程碳質量分數的預測，根據煉鋼過程的碳氧反應機理建立電弧爐脫碳模型。在此基礎上採用數據挖掘技術中的k-means聚類算法對電弧爐煉鋼歷史數據進行分析，選取8箇影響終點碳質量分數的因素，得齣不同冶煉情況下的聚類結果。通過計算噹前爐次與聚類結果加權歐氏距離，將相似度高的聚類結果爐次作為噹前爐次的預測參攷爐次，最終實現對電弧爐終點碳質量分數的預測。倣真結果錶明鋼水碳質量分數預報的命中率在75%以上，模型具有較高的預報精度。
위료실현대전호로야련과정탄질량분수적예측，근거련강과정적탄양반응궤리건립전호로탈탄모형。재차기출상채용수거알굴기술중적k-means취류산법대전호로련강역사수거진행분석，선취8개영향종점탄질량분수적인소，득출불동야련정황하적취류결과。통과계산당전로차여취류결과가권구씨거리，장상사도고적취류결과로차작위당전로차적예측삼고로차，최종실현대전호로종점탄질량분수적예측。방진결과표명강수탄질량분수예보적명중솔재75%이상，모형구유교고적예보정도。
In order to forecast the carbon content in arc furnace smelting process, decarburization model of EAF is established based on the C-O reaction mechanism in the steelmaking process. The steelmaking history data of EAF are analyzed by using the k-means clustering algorithm,which is be-longing to the data mining technology. Clustering results in different situations are found out by selec-ting eight factors affecting the end-point carbon content. By calculating the weighted Euclidean dis-tance between the current furnace and clustering results, the furnace which clustering results are of high similarity is used as the reference for the current furnace prediction,and the end-point carbon content prediction for EAF is finally realized. Simulation results show that the hit rate of the carbon content prediction for molten steel is above 75%,which means the model has high precision of fore-cast.
