Shi Yaoke, Wang Zhiwen, Du Xianjun, Gong Bin, Jegatheesan Veeriah, Haq Izaz Ul
Department of Automation, College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China.
Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China.
Membranes (Basel). 2021 May 24;11(6):381. doi: 10.3390/membranes11060381.
Compared to the traditional activated sludge process, the membrane bioreactor (MBR) has several advantages such as the production of high-quality effluent, generation of low excess sludge, smaller footprint requirements, and ease of automatic control of processes. The MBR has a broader prospect of its applications in wastewater treatment and reuse. However, membrane fouling is the biggest obstacle for its wider application. This paper reviews the techniques available to predict fouling in MBR, discusses the problems associated with predicting fouling status using artificial neural networks and mathematical models, summarizes the current state of fouling prediction techniques, and looks into the trends in their development.
与传统活性污泥法相比，膜生物反应器（MBR）具有若干优点，例如能产生高质量的出水、产生的剩余污泥量少、占地面积需求小以及易于实现工艺的自动控制。MBR在污水处理及回用方面具有更广阔的应用前景。然而，膜污染是其更广泛应用的最大障碍。本文综述了可用于预测MBR中膜污染的技术，讨论了使用人工神经网络和数学模型预测污染状态所涉及的问题，总结了膜污染预测技术的现状，并展望了其发展趋势。
