Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review. (1st June 2022)

• Record Type: Journal Article
• Authors: Niu, Chengxin; Li, Xuesong; Dai, Ruobin; Wang, Zhiwei
• Is Part Of: Water research. Volume 216(2022)
• Publication Date: 2022-06-01
• Subjects: Membrane fouling -- artificial intelligence -- fouling prediction -- membrane-based process; AI Artificial intelligence -- ANN artificial neural networks -- ANFIS adaptive neuro-fuzzy inference system -- ASAGA adaptive simulated annealing genetic algorithm -- BANN bootstrap aggregated neural networks -- BP back-propagation -- BSA bovine serum albumin -- COD chemical oxygen demand -- CLSM confocal laser scanning microscopy -- CNN convoluted neural network -- DO dissolved oxygen -- DNN deep neural network -- ENN Elman neural network -- EPS extracellular polymeric substances -- FL fuzzy logic -- FIS fuzzy inference system -- FT-IR spectroscopy fourier transform infrared -- FO forward osmosis -- GA genetic algorithm -- GP genetic programming -- HRT hydraulic retention time -- LSSVM least-squares support vector machine -- LSTM Long short-term memory -- MF microfiltration -- MLP multilayer perceptron -- MBR membrane bioreactor -- MD membrane distillation -- MWCNTs multiwalled carbon nanotubes -- MAPE mean absolute percentage error -- NLDH nanolayered double hydroxide -- NSRMSE normalized square root of mean square error -- NF nanofiltration -- OCT optical coherence tomography -- OMBR osmotic membrane bioreactor -- PES polyethersulfone -- PEG polyethylene glycol -- PSO particle swarm optimization -- perm permeability -- QCM-D quartz crystal microbalance with dissipation monitoring -- RSM response surface methodology -- R2 coefficient of determination -- R correlation coefficient -- RBF radial basis function -- RO reverse osmosis -- SA simulated annealing -- SVM support vector machine -- SRT sludge retention time -- RNN recurrent neural network -- RMSE root mean squared error -- SEM scanning electron microscopy -- TMP trans-membrane pressure -- TDS total dissolved solids -- TN total nitrogen -- MLSS mixed liquor suspended solids -- TOC total organic carbon -- UF ultrafiltration -- XRF X-ray fluorescence -- XDLVO extended Derjaguin-Landau-Verwey-Overbeek; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2022.118299
• Languages: English
• ISSNs: 0043-1354

Abstract

Highlights: Recent advances in artificial intelligence for predicting fouling are presented. Working principles of AI technologies for membrane fouling prediction are discussed. Comparisons of the inputs, outputs, and accuracy of AI approaches are conducted. Future research efforts are highlighted for AI technologies in predicting membrane fouling. Abstract: Membrane fouling is one of major obstacles in the application of membrane technologies. Accurately predicting or simulating membrane fouling behaviours is of great significance to elucidate the fouling mechanisms and develop effective measures to control fouling. Although mechanistic/mathematical models have been widely used for predicting membrane fouling, they still suffer from low accuracy and poor sensitivity. To overcome the limitations of conventional mathematical models, artificial intelligence (AI)-based techniques have been proposed as powerful approaches to predict membrane filtration performance and fouling behaviour. This work aims to present a state-of-the-art review on the advances in AI algorithms (e.g., artificial neural networks, fuzzy logic, genetic programming, support vector machines and search algorithms) for prediction of membrane fouling. The working principles of different AI techniques and their applications for prediction of membrane fouling in different membrane-based processes are discussed in detail. Furthermore, comparisons of the inputs, outputs, and accuracy of different AI approaches for membrane fouling prediction have been conducted based on the literature database. Future research efforts are further highlighted for AI-based techniques aiming for a more accurate prediction of membrane fouling and the optimization of the operation in membrane-based processes. Graphical Abstract: Image, graphical abstract