摘要
试验建立了UV/H2O2高级氧化工艺降解微囊藻毒素MC-LR的人工神经网络模型。研究了UV强度、H2O2投加量、MC-LR初始浓度、pH等对降解速率的影响,并以反向传播算法的神经网络模型对多因素条件下的降解效果进行仿真预测。结果表明,降解速率不受初始MC-LR浓度的影响;UV的加强及H2O2投加量的增加能有效提高MC-LR的降解速率;pH的降低能大幅度改善降解效果,尤其在酸性条件下,pH的变化对降解速率的影响程度更大。
An artificial neural network （ANN） model of microcystin-LR （MC-LR） degradation by UV/H2O2 advanced oxidation process was set up. The effects on degradation rate from the influencing factors, such as the intensity of UV radiation, H2O2 dose, MC-LR initial concentration, and pH value were studied, and the degradation effects under various influencing factors were si- mulated and predicted by reverse transportation calculation ANN model. The results indicated the degradation rate was invariable with different MC-LR initial concentrations; the increase of H2O2 dose and intensity of UV radiation could improve the MC-LR degradation rate effectively; the de- cline of pH value could enhance the degradation effect obviously, especially in acidity condition.
出处
《给水排水》
CSCD
北大核心
2009年第11期112-116,共5页
Water & Wastewater Engineering
基金
"十一五"国家科技支撑计划项目(2006BAJ08B06
2007BAC26B03)
国家科技重大专项资助(2008ZX07421-002)
上海市科委重点科技项目(072312001)
