本文關(guān)鍵詞： 紅柱石 莫來石化 抗侵蝕性能 BP神經(jīng)網(wǎng)絡(luò)　出處：《西安建筑科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：冶金工業(yè)用熔銅爐爐襯材料所面對(duì)的苛刻條件和熔煉工藝要求,使耐火材料必須具備優(yōu)異的理化性能,其中最重要的就是抗侵蝕性能,它直接決定了爐襯的使用壽命。近年來,紅柱石以其優(yōu)良的抗侵蝕性能和體積穩(wěn)定性等優(yōu)點(diǎn)受到廣泛關(guān)注。因此,探究紅柱石基耐火材料對(duì)改善爐襯材料抗侵蝕性能的機(jī)理,具有重要意義。在此基礎(chǔ)上,構(gòu)造人工神經(jīng)網(wǎng)絡(luò)中的BP網(wǎng)絡(luò)結(jié)構(gòu),建立原始礦物成分和生產(chǎn)工藝參數(shù)與產(chǎn)品抗侵蝕性能之間的關(guān)系,將促進(jìn)材料的研發(fā),使得研究過程高效而有針對(duì)性。本文以礬土、紅柱石、棕剛玉、活性?-Al2O3微粉、SiO2微粉為原料,硼酸為助熔劑,糊精為黏合劑,研究分別加入不同含量(8%、16%、25%、33%、42%、51%)的紅柱石顆粒對(duì)化銅感應(yīng)爐爐襯耐火材料抗侵蝕性能的影響。利用X射線衍射(XRD)檢測(cè)加入紅柱石前后試樣的物相組成變化,掃描電子顯微鏡(SEM)觀察和分析試樣燒成后的晶體形貌。并在此基礎(chǔ)上,通過神經(jīng)網(wǎng)絡(luò)的BP算法,建立預(yù)測(cè)紅柱石基耐火材料抗銅液侵蝕性能的模型。結(jié)果表明:(1)隨著紅柱石含量的增加,耐壓強(qiáng)度和體積密度的變化趨勢(shì)相似,都顯現(xiàn)出兩個(gè)峰值。顯氣孔率和燒后線變化率的變化趨勢(shì)則恰好相反。當(dāng)紅柱石的添加量為16%時(shí),剛玉和玻璃相的共同作用,使得材料結(jié)構(gòu)變得致密。當(dāng)紅柱石的添加量為51%時(shí),紅柱石含量的增加使得一次莫來石化和二次莫來石化生成大量的莫來石,莫來石的網(wǎng)狀結(jié)構(gòu)得以加強(qiáng)。(2)當(dāng)紅柱石添加量為25%時(shí),試樣的抗侵蝕性能最好。紅柱石的一次莫來石化過程形成網(wǎng)狀結(jié)構(gòu),大部分SiO2液相填充氣孔,排擠到晶體表面的剩余SiO2液相繼續(xù)與Al2O3進(jìn)行二次莫來石化反應(yīng),完善莫來石網(wǎng)狀結(jié)構(gòu)。(3)耐火材料的制造工藝及其特性決定了耐火材料的性能參數(shù)只能是一種統(tǒng)計(jì)分布的數(shù)據(jù),因此,很難使用解析式精確預(yù)測(cè)。本文中的紅柱石基耐火材料抗銅液侵蝕性能BP網(wǎng)絡(luò)模型說明了神經(jīng)網(wǎng)絡(luò)對(duì)于耐火材料性能預(yù)測(cè)方面的適用性。由此模型得到的化學(xué)成分與性能關(guān)系也完全符合實(shí)際理論。這些結(jié)果表明,ANN方法對(duì)組成和性能間呈復(fù)雜非線性關(guān)系的紅柱石基耐火材料而言是一種有效的分析工具。
[Abstract]:Requirements of metallurgical industry lining materials of copper smelting furnace in the face of harsh conditions and melting process, the refractory materials must have excellent physical and chemical properties, of which the most important is the anti erosion performance, which directly determines the life of the lining. In recent years, andalusite has received widespread attention because of its excellent corrosion resistance and volume stability and other advantages. Therefore, research of andalusite based refractory to improve the anti erosion mechanism performance of lining material, which is of great significance. Based on the BP network structure of artificial neural network, establish the relationship between the original minerals and production process parameters and product properties of anti erosion, will promote the development of material. The research process of efficient and targeted. In this paper, bauxite, andalusite, corundum, activity? -Al2O3 powder, SiO2 powder as raw material, boric acid as additive, dextrin as adhesive, study respectively The different contents (8%, 16%, 25%, 33%, 42%, 51%) the influence of andalusite particle erosion resistance of copper induction furnace lining refractory. By using X ray diffraction (XRD) detection of samples before and after adding andalusite phase composition changes, scanning electron microscopy (SEM) crystal morphology observation and analysis the sample after sintering. And on this basis, through the BP neural network algorithm, establish the prediction of andalusite based refractory copper liquid anti erosion performance model. The results showed that: (1) with the increase of andalusite content, compressive strength and bulk density change trend is similar, showing two peaks. The change trend of linear change rate porosity and burn after adding to the contrary. Popular pillar is 16%, corundum and glass phase interaction, the material became compact. Adding popular pillar is 51%, the increase of andalusite content makes a mullite And the two time Mullitization of mullite formed a lot of reticular structure of mullite can be strengthened. (2) when the andalusite addition is 25%, the sample is best. The anti erosion properties of andalusite a Mullitization process of the formation of network structure, most of the SiO2 liquid filled pores, exclusion to the remaining SiO2 the surface of the liquid crystal phase to two Mullization reaction with Al2O3, improve the mullite network structure. (3) the manufacturing process of refractory material and its characteristics determine the performance parameters of only refractory material is a kind of statistical distribution of the data, therefore, it is difficult to use analytic accurate prediction of andalusite based refractory copper resistance. In this paper, the liquid material erosion performance of the BP network model to illustrate the applicability of neural network for the prediction of performance of refractory materials. The relationship between chemical composition and properties of this model are also accord with the actual theory. These results show that ANN The method is an effective analytical tool for the complex nonlinear relationship between the composition and the properties of the andalusite based refractory.

【學(xué)位授予單位】：西安建筑科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TQ175.1

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