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

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.

A Review on Membrane Biofouling: Prediction, Characterization, and Mitigation.

Water scarcity is an increasing problem on every continent, which instigated the search for novel ways to provide clean water suitable for human use; one such way is desalination. Desalination refers to the process of purifying salts and contaminants to produce water suitable for domestic and industrial applications. Due to the high costs and energy consumption associated with some desalination techniques, membrane-based technologies have emerged as a promising alternative water treatment, due to their high energy efficiency, operational simplicity, and lower cost. However, membrane fouling is a major challenge to membrane-based separation as it has detrimental effects on the membrane's performance and integrity. Based on the type of accumulated foulants, fouling can be classified into particulate, organic, inorganic, and biofouling. Biofouling is considered the most problematic among the four fouling categories. Therefore, proper characterization and prediction of biofouling are essential for creating efficient control and mitigation strategies to minimize the damage associated with biofouling. Moreover, the use of artificial intelligence (AI) in predicting membrane fouling has garnered a great deal of attention due to its adaptive capability and prediction accuracy. This paper presents an overview of the membrane biofouling mechanisms, characterization techniques, and predictive methods with a focus on AI-based techniques, and mitigation strategies.

Prediction of Saturation Densities and Critical Properties of n-Decane, n-Pentadecane, and n-Eicosane Using Molecular Dynamics with Different Force-Fields.

Prediction of thermophysical properties of heavy hydrocarbons is important because of the recent increased interest in the extraction of heavy and shale oil to meet the global energy demand. Carrying out experimental work, such as determining the critical properties of heavy hydrocarbons, is challenging due to the possibility of thermal degradation during experimentation. This study focuses on the use of molecular simulations, specifically canonical molecular dynamics, to predict the critical properties of three hydrocarbons: n-decane (n-C10), n-pentadecane (n-C15), and n-eicosane (n-C20). The method uses volume-expansion molecular dynamics (VEMD), where a single box is enlarged in one axis and the vapor-liquid equilibrium is achieved. Three different force-fields (AMBER, COMPASS, and TraPPE) were employed to compare the accuracy of the simulated results and the required computational time. The results from the simulations were compared with available experimental data, equations-of-state, and several correlations. The results indicate that TraPPE is the most accurate and efficient force-field to predict the critical properties followed by AMBER and COMPASS.

Ultrasound-assisted forward osmosis desalination using inorganic draw solutes.

Internal concentration polarization (ICP) represents a serious challenge in forward osmosis (FO) desalination since it causes a significant decline in the water flux across the membrane. Mitigation of ICP is cumbersome since the phenomenon occurs within the membrane porous support layer and mitigation procedures such as inducing turbulence or changing the hydrodynamic conditions tend to be ineffective. In this study, the effect of 40 kHz ultrasound on FO desalination of synthesized brackish and seawater was investigated. The studied process utilizes two different inorganic draw solutes (magnesium and copper sulfate) that are available commercially, can generate high osmotic pressures, and can be easily separated from the product water. Different concentrations of the draw solutions were considered. Results show that the applied ultrasound was effective in partially mitigating the ICP effects and enhancing the water flux. Depending on the feed and the draw solution concentration, flux enhancements of up to 34.6% and 43.9% were observed with magnesium sulfate and copper sulfate draw solution, respectively. In addition, it was observed that the effect of ultrasound on flux enhancement was more evident at lower draw solution concentrations. Although water flux was enhanced, ultrasound resulted in an increased reverse draw solute flux across the membrane.