Membrane-based separation has gained increased popularity over the past few decades, particularly reverse osmosis (RO). A major impediment to the improved performance of membrane separation processes, in general, is membrane fouling. Fouling has detrimental effects on the membrane’s performance and integrity, as the deposition and accumulation of foulants on its surface and/or within its pores leads to a decline in the permeate flux, deterioration of selectivity, and permeability, as well as a significantly reduced lifespan. Several factors influence the fouling-propensity of a membrane, such as surface morphology, roughness, hydrophobicity, and material of fabrication. Generally, fouling can be categorized into particulate, organic, inorganic, and biofouling. Efficient prediction techniques and diagnostics are integral for strategizing control, management, and mitigation interventions to minimize the damage of fouling occurrences in the membranes. To improve the antifouling characteristics of RO membranes, surface enhancements by different chemical and physical means have been extensively sought after. Moreover, research efforts have been directed towards synthesizing membranes using novel materials that would improve their antifouling performance. This paper presents a review of the different membrane fouling types, fouling-inducing factors, predictive methods, diagnostic techniques, and mitigation strategies, with a special focus on RO membrane fouling.

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.

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.

This work aims to theoretically investigate the effect of both the fixed charge density of ion exchange membranes and the ionic strength of the treated aqueous NaCl solution on the generated Donnan potential at thermodynamic equilibrium conditions. The direct objective of our work is to calculate the equilibrium concentration of the Cl⁻ co-ion inside a swelled cation-exchange membrane equilibrated with a water/NaCl system. Two activity coefficient models are employed, i.e., the Debye–Huckel (DH) model (as a reference model) and the Meissner model, which is known for its applicability in treating concentrated solutions. Experimental data available in the literature for Donnan potential are used to verify model predictions. Our study confirms that a high fixed charge density is required to counterbalance the deterioration in membrane selectivity encountered in high-salinity systems. The DH model can be safely used to predict the Donnan potential for feed compositions up to 0.1 M. At higher compositions, the DH model significantly overestimates the predicted (absolute) Donnan potential compared to the Meissner model. The osmotic pressure resulting from the difference in ionic concentration between the membrane phase and the feed phase is found to have insignificant effects on the Donnan potential. The equilibrium computations and methodology are presented in a general way that enables handling multivalent electrolyte systems such as CaCl₂.

Absorption refrigeration cycles are alternatives to conventional vapor-compression cycles in which the energy required for refrigeration is provided by heat instead of mechanical work. In this paper, a novel refrigeration cycle utilizing the immiscible liquid-phase separation behavior is simulated and analyzed using Aspen simulator. The two conjugate liquids adopted in this work are triethylamine (solute) and water (solvent). This binary system has a low critical solution temperature of 18 °C. The thermophysical properties of the binary mixture are generated using the universal functional activity coefficient (UNIFAC) and the nonrandom two-liquid (NRTL) models. The phase splitting phenomenon at the generator temperature is predicted by both models. However, in comparison with the available experimental data for the same binary mixture, NRTL model gives better predictions for the flow rates and compositions of the material streams. Heat duties of the evaporator, absorber, and generator and the power consumption of the solution pump have been calculated using UNIFAC and NRTL models. The cycle COP that plays a major role in determining the cycle economical viability has been predicted for different operating conditions using the two models. Simulation results show that, for a waste heat reservoir at 60 °C and using NRTL model, the COP is about 2.0. Second law analysis conducted for all cycle components of the cycle shows that about 42% of the total exergy destructed occurs in the generator. Finally, the liquid-phase separation refrigeration cycle is predicted to be a promising cycle in the near future because of hardware and energy savings. Copyright © 2011 John Wiley & Sons, Ltd.