应用正交设计与BP网络优化制备改性活性炭

Optimizing Preparation of Modified Granular activated carbon Using Orthogonal Design and BP Neural Network

摘要:

采用FeCl2•4H2O 和KMnO4对活性炭进行改性。为获取高效的三价砷去除率，运用正交实验设计结合BP神经网络优化活性炭的改性方案。以FeCl2•4H2O 和KMnO4的摩尔总浓度、FeCl2•4H2O 和KMnO4物质的量比、水浴温度、干燥温度为正交实验设计因子，每个因子各取5个水平，以三价砷的去除率为目标因子，编制4因素5水平正交设计表。结合BP网络强大的函数拟合功能，以正交设计表中4因素为网络输入层，以三价砷去除率为网络输出层，建立BP神经网络模型，并通过该模型进行预测和优选，得到最佳的活性炭改性方案。即FeCl2•4H2O和KMnO4的摩尔总浓度为0.12 mol•L-1，物质的量比为3：1，水浴温度 45℃，干燥温度190 ℃。此时三价砷的去除率为0.765，与网络预测值0.788相差3.00%。运用X射线衍射及SEM电镜扫描技术对最佳条件下的改性活性炭进行表面性能研究，并测定活性炭表面的铁锰的负载量，为进一步深入研究打下基础。

Abstract:

Granular activated carbon (GAC) was modified with FeCl2•4H2O and KMnO4. The scheme was optimized by orthogonal design combining with BP neural network to get effective removal efficiency of arsenite. The four factors five levels orthogonal design table, such as overall mole concentration, the mole ratio of FeCl2•4H2O and KMnO4, water bath temperature and drying temperature as factors and arsenite removal rate as the goal factor, was established. The optimal modified scheme was found via predicting and selecting the BP network model through four factors as inputs and arsenite removal rate as output, which was combined with the effective fitting function. The overall mole concentration and the mole ratio of FeCl2•4H2O and KMnO4 were 0.12mol•L-1and 3:1, water bath temperate and drying temperate were 45℃ and 190℃,respectively. The arsenite removal rate was 0.765, so there was 3.00% error discrepancy compared with the model value 0.788. XRD and SEM were applied to gain the optimal modified GAC surface property. At the same time, the iron and manganese amount was measured. These supplied fundamental information for the further research.