振动
结垢
膜污染
生物系统
遗传算法
灰色关联分析
人工神经网络
生物污染
托普西斯
膜
生化工程
工程类
计算机科学
化学
数学
人工智能
声学
物理
机器学习
生物
生物化学
数理经济学
运筹学
作者
Shuhong Jiang,Shaoze Xiao,Huaqiang Chu,Fangchao Zhao,Zhenjiang Yu,Xuefei Zhou,Yalei Zhang
出处
期刊:Water Research
[Elsevier BV]
日期:2020-06-05
卷期号:182: 115972-115972
被引量:27
标识
DOI:10.1016/j.watres.2020.115972
摘要
Vibration membrane filtration has been confirmed as an effective method to improve algae separation from water. However, the fouling evolution process and the antifouling mechanism are not well understood. In this study, a novel hybrid method based on a dynamics model was proposed, a comprehensive evaluation was conducted, and the critical vibration frequency for accurate analysis and prediction of membrane fouling was developed. The dynamics model was studied with an improved collision-attachment model by considering all the concurrent and synergistic effects of the hydrodynamic interactions acting on algae. From the perspective of potential energy, the improved model systematically elucidated the reason why the antifouling performance was enhanced when the vibration frequency varied from 1 Hz to 5 Hz. In addition, the Technique for Order Preference by Similarity to Ideal Solution-grey relational analysis (TOPSIS-GRA) method with combined weights was incorporated for the first time to provide direct comprehensive evaluation evidence to determine the effect of the vibration frequency on membrane fouling. It was found that increasing the vibration frequency could not alleviate membrane fouling caused by extracellular organic matter. Moreover, the concept of a critical vibration frequency was proposed using genetic algorithm optimized back propagation neural network, and the energy consumption was analyzed. This combination could provide an effective means to choose the most appropriate vibration frequency, thereby improving the efficiency of the vibration membrane system in the algae separation process.
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