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.

It is very important to implement the fault diagnosis technology in industrial processes to make the process more reliable. In this paper, an improved particle filter (PF) method based on a modified beetle swarm antennae search (BSAS) algorithm is proposed and verified in a doubly fed induction generator (DFIG) fault diagnosis application. Firstly, the search strategy of BSAS is improved to ensure its global search ability. Secondly, it is introduced to the traditional PF algorithm to improve the particle diversity and impoverishment drawbacks. Finally, the fault diagnosis algorithm is verified by combining the DFIG state space model. The simulation experimental of fault detection and isolation results show that the proposed method is simple and effective, and it can effectively monitor the occurrence of faults. For the fault diagnostic application, the method proposed in this paper could be implemented in other model based processes, including chemical process, biochemical wastewater treatment process, etc.

Activated sludge models (ASMs) are often used in the simulation of the wastewater treatment process to evaluate whether the effluent quality parameters of a wastewater treatment plant meet the standards. The premise of successful simulation is to choose appropriate dynamic parameters for the model. A niche based adaptive invasive weed optimization (NAIWO) algorithm is proposed in this paper to find the appropriate kinetic parameters of activated sludge model 1 (ASM1). The niche idea is used to improve the possibility of convergence to the global optimal solution. In addition, the adaptive mechanism and periodic operator are introduced to improve the convergence speed and accuracy of the algorithm. Finally, NAIWO is used to optimize the parameters of ASM1. Comparison with other intelligent algorithms such as invasive weed optimization (IWO), genetic algorithm (GA), and bat algorithm (BA) showed the higher convergence accuracy and faster convergence speed of NAIWO. The results showed that the ASM1 model results agreed with measured data with smaller errors.