本文關鍵詞： 鐵礦預配料 有限下料槽 Canopy-Kmeans算法 組合優(yōu)化 智能調(diào)度　出處：《控制理論與應用》2017年07期 　論文類型：期刊論文

【摘要】：鐵礦預配料的原料種類繁多、化學成分差異較大,且下料槽個數(shù)有限、生產(chǎn)約束多,原料下料次序難以確定.針對該配料調(diào)度難題,本文提出了一種基于聚類算法和組合優(yōu)化的鐵礦混勻過程預配料智能調(diào)度方法.分別根據(jù)原料成分中SiO_2,TFe含量的差異,采用Canopy-Kmeans聚類方法進行兩次聚類,然后綜合考慮各項約束條件,利用融合專家規(guī)則的組合優(yōu)化和小范圍窮舉思想對聚類結果進行組合與排序,得到原料共槽方案與共槽下料次序,以保證在有限下料槽的情況下配完所有原料,且配得的混勻料化學元素含量始終盡可能穩(wěn)定.經(jīng)我國某鋼鐵廠實際生產(chǎn)數(shù)據(jù)驗證,所提方法與現(xiàn)有人工計算方法相比,大幅縮減了運算時間,且礦物化學元素指標的波動小,具有實用價值.
[Abstract]:There are many kinds of raw materials for iron ore pre-proportioning, big difference in chemical composition, limited number of feeding tanks, many production constraints, and difficult to determine the order of raw materials. This paper presents an intelligent scheduling method for iron ore blending process based on clustering algorithm and combination optimization. According to the difference of SiO2TFE content in raw material composition, Canopy-Kmeans clustering method is used to cluster twice. Then considering the constraint conditions synthetically, using the combination optimization of fusion expert rules and the idea of small range exhaustive, the clustering results are combined and sorted, and the scheme of common trough and the order of cutting stock in common trough are obtained. In order to ensure that all the raw materials are finished under the condition of limited blanking tank, and the chemical element content of the mixed material is as stable as possible, the method is verified by the actual production data of a steel and iron plant in our country, and compared with the existing manual calculation method. The calculation time is greatly reduced, and the fluctuation of mineral chemical element index is small, so it is of practical value.
【作者單位】： 中南大學信息科學與工程學院;
【基金】：國家自然科學基金重大項目課題(61590921);國家自然科學基金資助項目(61273187) 中南大學中央高校基本科研業(yè)務費專項資金(2017zzts135)資助~~
【分類號】：TD921.6;TP301.6

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