Prediction of membrane fouling using artificial neural networks for wastewater treated by membrane bioreactor technologies: bottlenecks and possibilities

• Published in: Environmental science and pollution research international Vol. 24; no. 29; pp. 22885 - 22913
• Main Authors: Schmitt, Félix, Do, Khac-Uan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2017; Springer Nature B.V; Springer Verlag
• Subjects: Aquatic Pollution; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Beverage industry; Biofouling - prevention & control; Bioreactors; Bioreactors - microbiology; Computer Simulation; Control methods; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Filtration; Fouling; Fouling control; Influents; Mathematical models; membrane bioreactors; Membrane processes; Membrane resistance; Membranes; Membranes, Artificial; methodology; Neural networks; Neural Networks (Computer); Physics; prediction; Review Article; Waste Water - chemistry; Waste Water Technology; wastewater; Wastewater treatment; Wastewater treatment plants; Water Management; Water Pollution Control; Water Purification - methods; Fouling control; Membrane bioreactor; Microfiltration; Artificial neural network; Wastewater; Membrane fouling; Transmembrane pressure
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-017-0046-7

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.