發(fā)布時(shí)間：2018-04-15 14:08

  本文選題：X射線衍射 + X射線熒光光譜　； 參考：《光譜學(xué)與光譜分析》2017年08期

【摘要】：以不同處理工藝鋼渣,即轉(zhuǎn)爐熱潑渣、轉(zhuǎn)爐滾筒渣、鑄余渣、鐵水脫硫渣、電爐熱潑渣和電爐滾筒渣作為研究對(duì)象。利用X射線衍射儀和X射線熒光光譜分析儀研究鋼渣的化學(xué)成分與物相組成。利用BP神經(jīng)網(wǎng)絡(luò)模型建立鋼渣活性指數(shù)預(yù)測(cè)模型,研究化學(xué)成分、物相組成與活性的關(guān)系。結(jié)果表明:鋼渣的處理工藝不同,其化學(xué)成分與物相組成存在明顯差異;鋼渣活性指數(shù)預(yù)測(cè)模型具有良好的符合性,即相對(duì)誤差為2.42%和-2.54%,能充分反映輸入層與輸出層之間的映射關(guān)系。
[Abstract]:The research object is the steel slag of different treatment processes, that is, converter hot throwing slag, converter drum slag, cast residue slag, molten iron desulphurization slag, electric furnace hot cast slag and electric furnace drum slag.The chemical composition and phase composition of steel slag were studied by X ray diffractometer and X ray fluorescence spectrometer.The prediction model of steel slag activity index was established by BP neural network model, and the relationship between chemical composition, phase composition and activity was studied.The results show that the chemical composition and phase composition of steel slag are different with different treatment processes, and the prediction model of steel slag activity index has good conformance.The relative error is 2.42% and -2.54, which can fully reflect the mapping relationship between the input layer and the output layer.
【作者單位】： 西安建筑科技大學(xué)材料與礦資學(xué)院;中冶寶鋼技術(shù)服務(wù)有限公司;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(50872105)資助
【分類號(hào)】：O657.34;X757

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本文編號(hào)：1754445