A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs)

Abstract

Membrane fouling is a major hurdle to effective pressure-driven membrane processes, such as microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO). Fouling refers to the accumulation of particles, organic and inorganic matter, and microbial cells on the membrane’s external and internal surface, which reduces the permeate flux and increases the needed transmembrane pressure. Various factors affect membrane fouling, including feed water quality, membrane characteristics, operating conditions, and cleaning protocols. Several models have been developed to predict membrane fouling in pressure-driven processes. These models can be divided into traditional empirical, mechanistic, and artificial intelligence (AI)-based models. Artificial neural networks (ANNs) are powerful tools for nonlinear mapping and prediction, and they can capture complex relationships between input and output variables. In membrane fouling prediction, ANNs can be trained using historical data to predict the fouling rate or other fouling-related parameters based on the process parameters. This review addresses the pertinent literature about using ANNs for membrane fouling prediction. Specifically, complementing other existing reviews that focus on mathematical models or broad AI-based simulations, the present review focuses on the use of AI-based fouling prediction models, namely, artificial neural networks (ANNs) and their derivatives, to provide deeper insights into the strengths, weaknesses, potential, and areas of improvement associated with such models for membrane fouling prediction.

Heavy metals rejection from water by CMC complexation- PVDF ultrafiltration system: Box-Behnken design optimization

“…The high e ciency of the PVDF membrane was ascribed to the incorporation of hydrogel, which altered the hydrophilicity, porosity, and surface roughness [23]. Nevertheless, currently investigated PVDF membranes are not viable for their commercial implementation on a large scale due to membrane fouling, low e ciency, and reduced regeneration [24]. It should be noted that the pore size of ultra ltration membranes is usually larger than the metallic ions present in a targeted analyte.…”

Application of Machine Learning to Characterize the Permeate Quality in Pilot-Scale Vacuum-Assisted Air Gap Membrane Distillation Operation

“…Since permeate quality is subject to unknown defects on the membrane, the use of artificial neural networks is justified for modeling under different operating conditions [35,36]. Artificial Neural Networks (ANN) have emerged as a promising modeling tool in the realm of MD systems, especially for flux and thermal efficiency prediction [37] and fouling prediction [38,39]. One of the primary advantages of this methodology is its capability to effectively capture and fit almost all nonlinear processes.…”