School of Environmental Science and Technology, Hanoi University of Science and Technology, Hanoi, Vietnam.
Energy and Environmental Department, National Institute of Applied Sciences of Lyon, 69621, Villeurbanne Cedex, France.
Environ Sci Pollut Res Int. 2017 Oct;24(29):22885-22913. doi: 10.1007/s11356-017-0046-7. Epub 2017 Sep 4.
Membrane fouling is a major concern for the optimization of membrane bioreactor (MBR) technologies. Numerous studies have been led in the field of membrane fouling control in order to assess with precision the fouling mechanisms which affect membrane resistance to filtration, such as the wastewater characteristics, the mixed liquor constituents, or the operational conditions, for example. Worldwide applications of MBRs in wastewater treatment plants treating all kinds of influents require new methods to predict membrane fouling and thus optimize operating MBRs. That is why new models capable of simulating membrane fouling phenomenon were progressively developed, using mainly a mathematical or numerical approach. Faced with the limits of such models, artificial neural networks (ANNs) were progressively considered to predict membrane fouling in MBRs and showed great potential. This review summarizes fouling control methods used in MBRs and models built in order to predict membrane fouling. A critical study of the application of ANNs in the prediction of membrane fouling in MBRs was carried out with the aim of presenting the bottlenecks associated with this method and the possibilities for further investigation on the subject.
膜污染是优化膜生物反应器（MBR）技术的主要关注点。为了精确评估影响膜过滤阻力的污染机制，例如废水特性、混合液成分或操作条件等，已经进行了大量的膜污染控制研究。MBR 在世界各地的各种废水处理厂中的应用需要新的方法来预测膜污染，从而优化操作的 MBR。这就是为什么逐步开发了新的能够模拟膜污染现象的模型，主要使用数学或数值方法。面对这些模型的局限性，人工神经网络（ANNs）逐渐被认为可以预测 MBR 中的膜污染，并显示出巨大的潜力。本文综述了 MBR 中使用的污染控制方法和为预测膜污染而建立的模型。对人工神经网络在 MBR 中预测膜污染的应用进行了批判性研究，目的是介绍与该方法相关的瓶颈问题以及进一步研究该主题的可能性。
