本文選題：氧化鋁　切入點(diǎn)：溶出苛性比值　出處：《輕金屬》2017年09期 　論文類(lèi)型：期刊論文

【摘要】：針對(duì)拜耳法生產(chǎn)氧化鋁的溶出工序中苛性比值難以實(shí)時(shí)獲得的問(wèn)題,提出了一種通過(guò)BP神經(jīng)網(wǎng)絡(luò)模型對(duì)溶出苛性比值進(jìn)行提前預(yù)測(cè)的方法。根據(jù)溶出生產(chǎn)物料輸入輸出關(guān)系,提出了基于物料平衡的溶出苛性比值機(jī)理計(jì)算模型,并對(duì)計(jì)算模型的輸入變量進(jìn)行優(yōu)化,使其能夠滿(mǎn)足BP神經(jīng)網(wǎng)絡(luò)輸入的要求。最終設(shè)計(jì)了一種具有誤差反傳學(xué)習(xí)及歷史數(shù)據(jù)訓(xùn)練功能的BP神經(jīng)網(wǎng)絡(luò),經(jīng)山西某鋁廠(chǎng)實(shí)際數(shù)據(jù)測(cè)試,BP神經(jīng)網(wǎng)絡(luò)能夠較好的預(yù)測(cè)溶出苛性比值。
[Abstract]:In order to solve the problem that it is difficult to obtain the ratio of causticity in the digestion process of alumina production by Bayer process in real time, a BP neural network model is proposed to predict the ratio of dissolution causticity in advance. According to the relation between input and output of dissolved materials, A model for calculating dissolution causticity ratio based on material balance is proposed, and the input variables of the model are optimized. Finally, a BP neural network with the function of error backpropagation learning and historical data training is designed. The BP neural network can predict the ratio of dissolution causticity better by the actual data of an aluminum factory in Shanxi province.
【作者單位】： 沈陽(yáng)鋁鎂設(shè)計(jì)研究院有限公司;
【分類(lèi)號(hào)】：TP183;TQ133.1

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相關(guān)期刊論文 前1條

1 陳肖虎;提高氧化鋁種分率的研究[J];貴州工業(yè)大學(xué)學(xué)報(bào)(自然科學(xué)版);1999年05期

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