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Research And Application Of Carbonaceous Reducing Agents For Industrial Silicon Production

Posted on:2018-02-07Degree:DoctorType:Dissertation
Country:ChinaCandidate:Z J ChenFull Text:PDF
GTID:1311330536987237Subject:Metallurgical physical chemistry
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The charcoal is usually used as carbonaceous reductant for the production process of silicon,which mainly sources from a lot of forest and has cause serious damage to the ecological environment.Petroleum coke is a kind of ideal carbonaceous reductant because of its high fixed carbon,low ash and low cost,but its certain graphitization structure,especially graphitization easily happened under the condition of high temperature,leads to decline in reactivity and increase in electrical conductivity of carbon materials,which have certain unfavorable effects on the process of silicon production.In addition,petroleum coke has a high sulfur content that has not only an important influence on the production process of silicon furnace,but has also produced a large number of SO2 gas harmful to the environment.Therefore,there has very important significance to study thereasonsof high costs and low performance in carbonaceous reductant and the exceedingly energy-intensivein the process of silicon smelting production problem.The main research contents are as follows.?1?The effects of the medium?distilled water and anhydrous ethanol as a medium?on CO2 gasification activity of the catalysts-loaded?K2CO3 as additive withmassratioof0.5,0.8,1.0 and 1.5%,respectively?petroleum coke were studied by the thermogravimetric analysis?TGA?and the X-Ray Diffraction?XRD?,and the physical and chemical properties of sample were also analyzed.It have been found that organic medium as a grinded medium has improved effect on the reaction performance of petroleum coke,and the time for reaction completion was reduced by 67%under the condition of same additives.?2?The desulfurizing efficiency under vacuum for the petroleum cokes containing high amounts of sulfur was investigated,and the parameters of particle sizes?350450?m,300350?m,180300?m,150180?m,125150?m and125?m?,feeding layer depth?030mm,3060mm and 6090mm?,heat treatment temperature?600-1500°Cintervalis 100°C?,holding time?0.5h,1.0h,1.5h,2.0h,2.5h and 3.0h?,and vacuum degree(10-2 Pa,1Pa and 100 Pa)were also studied.The results demonstrated that the desulfurizing efficiency increased by 20%under vacuum processes,compared to the normal pressure.According to the analysis of XRD results,the petroleum coke crystal structures were more ordered after the removal of sulfur from coke mainly due to the breakage of C-S bonds and reorganization of the C-C bonds.?3?The effect of different biomass raw materials?with the mass ratio of 0:100,20:80,40:60,60:40,80:20 and 100:0,respectively?on synergistic reaction the petroleum coke was investigated by TGA analysis method,and the synergy effects were also evaluated by cited catalytic index.The results shown that the chemical activation energy of mixing materials decreased with the increase of biomass ratio,and the most significant content is about 50%when the mixture reaction between the same biomass and petroleum coke.?4?The analysis showed that about 45%of total energy for the silicon furnace provided by the power consumption according to 3 months above data accumulated during experiments.Exergy efficiency is the ratio of effective or earnings exergy and pay exergy to total exergy,and it can be expressed as follows:Exergy analyses of industrial silicon furnaces have shown overall exergy efficiencies of30%,where the remaining energy?70%?leaves the process as thermal energy in the cooling water,i.e.,hot off-gas,by radiation and convection from the furnace and from the liquid silicon cooling process.?5?The effect of main impurity Fe2O3,Al2O3,Ca O and trace impurities oxide from the raw materials on the power consumption in the process of silicon production was investigated by the correlation coefficient and the contour method.The results showed that the effects of metal oxides content on SAF power consumption fell into the order Ca O>Fe2O3>Al2O3.And the effects of the remaining trace metal oxides on power consumption fell into the order Ti O2>Cr2O3>Mg O>K2O,while Ni O content has no practical effect on power consumption.?6?The effect of the proximate composition of carbonaceous reductant on the power consumption and the energy efficiency was investigated by statistical analysis method in the process of silicon production.The effect of interaction among the proximate composition of different combination carbonaceous reductant on the power consumption and the energy efficiency was also studied by the contour analysis method.The results indicated that the proximate composition of carbonaceous reductants impact on the power consumption and the exergy efficiency and the order is:fixed carbon>volatile>moisture.Therefore,the extent and effect of fluctuations in the proximate composition of carbon materials on specific electrical energy consumption and exergy efficiency in the production process of silicon furnaces was evaluated using a novel artificial neural network?ANN?approach,and the proposed model successfully predicted the values of the specific electrical energy consumption and the exergy efficiency based on the correlation coefficient?0.9393 and 0.8723?of the actual and predicted values for the various proximate compositions of carbon materials.?7?Based on the results of this study it has been estimated that the effect of different carbonaceous reductant,its chemical composition,and industrial composition on the silicon production,by optimizing the ratio of raw materials as well as reducing agent composition,the pellets carbon composite reductant was prepared in the experimental-scale,and then the physical and chemical performance were tested.According to the comprehensive analysis,compared with the original raw material the cost of composite reductants used in the production process of silicon was reduced by?50-150/t,which has a relatively objective economic value.
Keywords/Search Tags:carbonaceous reductants, silicon industry, exergy efficiency, power consumption, artificial neural network model
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