( 1. 中南大學(xué) 冶金科學(xué)與工程學(xué)院，長(zhǎng)沙 410083；
2. 中南大學(xué) 信息科學(xué)與工程學(xué)院， 長(zhǎng)沙 410083)
摘 要: 針對(duì)燒結(jié)法氧化鋁生產(chǎn)過程中生料漿配料工藝的特點(diǎn)， 根據(jù)物料平衡的原理建立機(jī)理模型， 作為生料漿質(zhì)量預(yù)測(cè)的主規(guī)律模型； 針對(duì)堿液成分波動(dòng)大且難以實(shí)時(shí)檢測(cè)的問題， 對(duì)堿液成分含量建立了神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型， 并和機(jī)理模型進(jìn)行嵌套集成； 利用灰色理論對(duì)機(jī)理模型的偏差數(shù)據(jù)進(jìn)行信息挖掘， 建立了GM(1, 1)補(bǔ)償模型， 并與機(jī)理模型進(jìn)行并聯(lián)集成， 獲得生料漿質(zhì)量預(yù)測(cè)模型。 驗(yàn)證結(jié)果表明， 該質(zhì)量預(yù)測(cè)模型能獲得較理想的生料漿質(zhì)量預(yù)測(cè)精度， 其應(yīng)用可使生料漿質(zhì)量得到顯著的提高。
關(guān)鍵字: 生料漿； 神經(jīng)網(wǎng)絡(luò)； 機(jī)理模型； 預(yù)測(cè)模型； 灰色理論
（ 1. School of Metallurgical Science and Engineering, Central South University, Changsha 410083, China;
2. School of Information Science and Engineering, Central South University, Changsha 410083, China）
Abstract: Based on the analysis of the characteristics of the raw mix slurry preparing process in alumina sintering production process, firstly, a mechanism model based on material balance principle was established as the master-rule model for the quality prediction; secondly, considering the problem that the alkali liquor composition was unstable and its real-time measurement was difficult, a NN (neural networks) prediction model for the prediction of the alkali liquor composition was set up and nesting-integrated with the mechanism model; finally, using the gray theory for the information mining from the errors of the mechanism model, a GM(1, 1) compensation model was put forward and parallel-connection-integrated with the mechanism model, achieving a raw mix slurry quality prediction model. Verification results show that the quality prediction model is with satisfactory prediction accuracy, and its industrial application will benefit to improving the quality of raw mix slurry.
Key words: raw mix slurry; neural network; mechanism model; prediction model; gray theory
