| 摘 要: 曝气是MBR膜污染的操作条件影响因子中的一个重要参数，曝气强度过大易造成膜丝断裂，过小又不能
减缓膜污染。针对该问题，本研究首先运用灰色模型对中空纤维膜不同使用阶段中的最佳曝气强度值进行粗略预测。再
将影响膜过滤性能的三个因素作为BP神经网络的输入，不同膜清洗次数后的最佳曝气强度作为输出，进行曝气的BP网
络模型预测。最后将灰色模型的预测值及影响膜过滤性能的三个因素作为灰色神经网络的输入，最佳曝气强度作为输
出，进行曝气的灰色神经网络预测。通过对两个神经网络模型的预测结果对比分析，得出结论灰色神经网络模型优于
BP神经网络模型。 |
| 关键词: MBR：膜污染：曝气强度：灰色系统：灰色神经网络 |
|
中图分类号: TP389.1
文献标识码: A
|
| 基金项目: 国家自然科学基金项目 |
|
| A Study on the Application of Grey Neural Network in MBR Aeration Intensity |
|
SHI Yawei，LI Chunqing
|
( School of Computer Science and Software Technology, Tianjin Polytechnic University, Tianjin 300387, China)
|
| Abstract: Aeration is an important parameter in the operating conditions of MBR membrane pollution,and the aeration
intensity is too large to cause the rupture of membrane,which is too small to slow down the membrane fouling.To solve
this problem,this study first uses the gray model to predict the best aeration intensity value of hollow fiber membrane in
different stages.Three factors which affect the membrane filtration performance as the input of the BP neural network,the
best aeration intensity of different membrane cleaning times as the output,the BP network model prediction.Finally,the grey
model's predictive value and the three factors which affect the filtration performance of the membrane are used as the input
of the grey neural network,the best aeration intensity is the output,and the grey neural network prediction.After comparing
the prediction results of two neural network models,it is concluded that the grey neural network model is better than the BP
neural network model. |
| Keywords: MBR;membrane fouling;aeration intensity;the grey system;the grey neural network |
