Forward osmosis technology for water treatment: Recent advances and future perspectives

Compared with the traditional activated sludge process, a membrane bioreactor (MBR) has many advantages, such as good effluent quality, small floor space, low residual sludge yield and easy automatic control. It has a promising prospect in wastewater treatment and reuse. However, membrane fouling is the biggest obstacle to the wide application of MBR. This paper aims at summarizing the new research progress of membrane fouling mechanism, control, prediction and detection in the MBR systems. Classification, mechanism, influencing factors and control of membrane fouling, membrane life prediction and online monitoring of membrane fouling are discussed. The research trends of relevant research areas in MBR membrane fouling are prospected.

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A Review on the Mechanism, Impacts and Control Methods of Membrane Fouling in MBR System.

A critical review of advanced nanotechnology and hybrid membrane based water recycling, reuse, and wastewater treatment processes

Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review.

Enhancing the removal efficiency of osmotic membrane bioreactors: A comprehensive review of influencing parameters and hybrid configurations.

Characterization and control of membrane fouling during dewatering of activated sludge using a thin film composite forward osmosis membrane.

Currently, osmotic membrane bioreactor (OMBR) is actively employed for municipal and industrial wastewater treatment. However, there are still some barriers limit the application of the OMBR process on a real scale. This study aimed to develop artificial intelligence (AI)-based models for the early prediction of OMBR system performance in order to increase the system efficiency, then reducing the environmental impacts of wastewater. Based on systematic evaluation of the operational parameters, appropriate input datasets were recommended for each simulation model, in which conductivity and pH parameters played an essential role in all but one simulation. Model structures for all simulations were evaluated for the optimal number of hidden layers (2–6 layers) and the appropriate number of neurons in each layer (5–30 neurons). The developed models demonstrated extremely good performance, with R2 values of 0.92 and 0.93 reported for the prediction of water flux and membrane fouling simulations, respectively. The root mean square errors (RMSEs) for these predictions were 1.4 LMH and 0.8 × 1014 m−1, respectively. Moreover, the developed model for TOC removal also displayed very high efficiency, with R2 and RMSE values of 0.98% and 0.3%, respectively. Strong predictions were obtained for the simulation of nutrient rejections as well (NH4-N, TN, PO4-P, TP), with all R2 values > 0.84 and RMSEs

Development of artificial intelligence-based models for the prediction of filtration performance and membrane fouling in an osmotic membrane bioreactor

Real-time monitoring of forward osmosis membrane fouling in wastewater reuse process performed with a deep learning model.

In recent years, membrane technologies are widely utilized in water and wastewater treatment processes. However, controlling and improving these systems still need to be investigated and, therefore...

Enhancement of membrane system performance using artificial intelligence technologies for sustainable water and wastewater treatment: A critical review

Nguyen Duc Viet

Papers

Enhancing the removal efficiency of osmotic membrane bioreactors: A comprehensive review of influencing parameters and hybrid configurations.

Characterization and control of membrane fouling during dewatering of activated sludge using a thin film composite forward osmosis membrane.

Development of artificial intelligence-based models for the prediction of filtration performance and membrane fouling in an osmotic membrane bioreactor

Real-time monitoring of forward osmosis membrane fouling in wastewater reuse process performed with a deep learning model.

Enhancement of membrane system performance using artificial intelligence technologies for sustainable water and wastewater treatment: A critical review