摘要
以硝酸铜与硝酸铈对TiO2进行改性制备Cu-Ce/TiO2。通过均匀设计与BP神经网络结合,研究Cu-Ce/TiO2制备工艺参数,即Cu-Ce/TiO2中Cu-Ce与TiO2的物质的量比、Cu-Ce/TiO2中Cu与Ce的物质的量比、Cu-Ce/TiO2凝胶的煅烧温度、煅烧的升温速度和煅烧后恒温时间对Cu-Ce/TiO2湿性能和光催化性能的影响。构建制备工艺参数与性能的Cu-Ce/TiO2BP神经网络优化模型,获得优化制备工艺参数,并对优化Cu-Ce/TiO2进行性能测试与表征。结果表明,优化Cu-Ce/TiO2制备工艺参数:Cu-Ce/TiO2中Cu-Ce与TiO2的物质的量比为0.033、Cu-Ce/TiO2中Cu与Ce的物质的量比为0.89、Cu-Ce/TiO2凝胶的煅烧温度为502℃、煅烧的升温速度为1.8℃/min和煅烧后恒温时间为1.6h。优化Cu-Ce/TiO2的湿性能为0.0871g/g,优化Cu-Ce/TiO2的光催化性能为51.5%。对Cu-Ce/TiO2制备工艺参数进行优化,尤其是煅烧的升温速度与煅烧后恒温时间进行优化,可以进一步促使优化Cu-Ce/TiO2的粒径降低、均匀性增加。
Cu-Ce/TiO 2 was prepared by doping Cu and Ce respectively.Impacts on photocatalytic performance of Cu-Ce/TiO 2 exerted by various preparation parameters,namely,mole ratio of Cu-Ce and TiO 2,mole ratio of Cu and Ce,calcination temperature of Cu-Ce/TiO 2,rate of temperature increase of Cu-Ce/TiO 2 and holding time at constant temperature,were researched by combining uniform design with BP neural network.Optimization model based on BP neural network was established to obtain optimized preparation parameters of Cu-Ce/TiO 2,of which the photocatalytic performance were tested and characterized.Results show that optimized values of abovementioned preparation parameters are 0.033,0.89,502 ℃, 1.8 ℃/min and 1.6 h respectively and accordingly.And optimized humidity and photocatalytic property are presented as 0.087 1 g/g and 51.5%.Optimization on preparation parameters of Cu-Ce/TiO 2 especially in terms of temperature increase rate and holding time at constant temperature can promote decrease of particle size as well as increase of uniformity.
作者
杨小妮
杨宏刚
王丹
YANG Xiao-ni;YANG Hong-gang;WANG Dan(Huaqing College,Xi’an University of Architecture and Technology,Xi’an 710043,China;School ofBuilding Services Science and Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,China;College of Resources Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,China)
出处
《应用化工》
CAS
CSCD
北大核心
2019年第7期1644-1648,共5页
Applied Chemical Industry
基金
陕西省重点科技创新团队(2017KCT-14)
陕西省教育厅专项科研计划资助项目(16JK2092)
关键词
TIO2
均匀设计
BP神经网络
光湿性能
TiO2
uniform design
BP neural network
photocatalytic-humidity performance
