Deep learning model for simulating influence of natural organic matter in nanofiltration

Unveiling the spatiotemporal dynamics of membrane fouling: A focused review on dynamic fouling characterization techniques and future perspectives

Membrane technology has emerged as a crucial method for obtaining clean water from unconventional sources in the face of water scarcity. It finds wide applications in wastewater treatment, advanced treatment, and desalination of seawater and brackish water. However, membrane fouling poses a huge challenge that limits the development of membrane-based water treatment technologies. Characterizing the dynamics of membrane fouling is crucial for understanding its development, mechanisms, and effective mitigation. Instrumental techniques that enable in situ or real-time characterization of the dynamics of membrane fouling provide insights into the temporal and spatial evolution of fouling, which play a crucial role in understanding the fouling mechanism and the formulation of membrane control strategies. This review consolidates existing knowledge about the principal advanced instrumental analysis technologies employed to characterize the dynamics of membrane fouling, in terms of membrane structure, morphology, and intermolecular forces. Working principles, applications, and limitations of each technique are discussed, enabling researchers to select appropriate methods for their specific studies. Furthermore, prospects for the future development of dynamic characterization techniques for membrane fouling are discussed, underscoring the need for continued research and innovation in this field to overcome the challenges posed by membrane fouling.

Predictive modeling based on artificial neural networks for membrane fouling in a large pilot-scale anaerobic membrane bioreactor for treating real municipal wastewater

Membrane fouling is the primary obstacle to applying anaerobic membrane bioreactors (AnMBRs) in municipal wastewater treatment. This issue holds critical significance as efficient wastewater treatment serves as a cornerstone for achieving environmental sustainability. This study uses machine learning to predict membrane fouling, taking advantage of rapid computational and algorithmic advances. Based on the 525-day operation data of a large pilot-scale AnMBR for treating real municipal wastewater, regression prediction was realized using multilayer perceptron (MLP) and long short-term memory (LSTM) artificial neural networks under substantial variations in operating conditions. The models involved employing the organic loading rate, suspended solids concentration, protein concentration in extracellular polymeric substance (EPSp), polysaccharide concentration in EPS (EPSc), reactor temperature, and flux as input features, and transmembrane pressure as the prediction target output. Hyperparameter optimization enhanced the regression prediction accuracies, and the rationality and utility of the MLP model for predicting large-scale AnMBR membrane fouling were confirmed at global and local levels of interpretability analysis. This work not only provides a methodological advance but also underscores the importance of merging environmental engineering with computational advancements to address pressing environmental challenges.

Mixed scaling patterns and mechanisms of high-pressure nanofiltration in hypersaline wastewater desalination

Nanofiltration (NF) will play a crucial role in salt fractionation and recovery, but the complicated and severe mixed scaling is not yet fully understood. In this work, the mixed scaling patterns and mechanisms of high-pressure NF in zero-liquid discharge (ZLD) scenarios were investigated by disclosing the role of key foulants. The bulk crystallization of CaSO4 and Mg-Si complexes and the resultant pore blocking and cake formation under high pressure were the main scaling mechanisms in hypersaline desalination. The incipient scalants were Mg-Si hydrates, CaF2, CaCO3, and CaMg(CO3)2. Si deposited by adsorption and polymerization prior to and impeded Ca scaling when Mg was not added, thus pore blocking was the main mechanism. The amorphous Mg-Si hydrates contribute to dense cake formation under high hydraulic pressure and permeate drag force, causing rapid flux decline as Mg was added. Humic acid has a high affinity to Ca2+by complexation, which enhances incipient scaling by adsorption or lowers the energy barrier of nucleation but improves the interconnectivity of the foulants layer and inhibits bulk crystallization due to the chelation and directional adsorption. Bovine serum albumin promotes cake formation due to the low electrostatic repulsion and acts as a cement to particles by adsorption and bridging in bulk. This work fills the research gaps in mixed scaling of NF, which is believed to support the application of ZLD and shed light on scaling in hypersaline/ultra-hypersaline wastewater desalination applications.

The intelligent prediction of membrane fouling during membrane filtration by mathematical models and artificial intelligence models

Recently, membrane separation technology has been widely utilized in filtration process intensification due to its efficient performance and unique advantages, but membrane fouling limits its development and application. Therefore, the research on membrane fouling prediction and control technology is crucial to effectively reduce membrane fouling and improve separation performance. This review first introduces the main factors (operating condition, material characteristics, and membrane structure properties) and the corresponding principles that affect membrane fouling. In addition, mathematical models (Hermia model and Tandem resistance model), artificial intelligence (AI) models (Artificial neural networks model and fuzzy control model), and AI optimization methods (genetic algorithm and particle swarm algorithm), which are widely used for the prediction of membrane fouling, are summarized and analyzed for comparison. The AI models are usually significantly better than the mathematical models in terms of prediction accuracy and applicability of membrane fouling and can monitor membrane fouling in real-time by working in concert with image processing technology, which is crucial for membrane fouling prediction and mechanism studies. Meanwhile, AI models for membrane fouling prediction in the separation process have shown good potential and are expected to be further applied in large-scale industrial applications for separation and filtration process intensification. This review will help researchers understand the challenges and future research directions in membrane fouling prediction, which is expected to provide an effective method to reduce or even solve the bottleneck problem of membrane fouling, and to promote the further application of AI modeling in environmental and food fields.

Pressure-driven membrane filtration technology for terminal control of organic DBPs: A review

Disinfection by-products (DBPs), a series of undesired secondary contaminants formed during the disinfection processes, deteriorate water quality, threaten human health and endanger ecological safety. Membrane-filtration technologies are commonly used in the advanced water treatment and have shown a promising performance for removing trace contaminants. In order to gain a clearer understanding of the behavior of DBPs in membrane-filtration processes, this work dedicated to: (1) comprehensively reviewed the retention efficiency of microfiltration (MF), ultrafiltration (UF), nanofiltration (NF) and reverse osmosis (RO) for DBPs. (2) summarized the mechanisms involved size exclusion, electrostatic repulsion and adsorption in the membrane retention of DBPs. (3) In conjunction with principal component analysis, discussed the influence of various factors (such as the characteristics of membrane and DBPs, feed solution composition and operating conditions) on the removal efficiency. In general, the characteristics of the membranes (salt rejection, molecular weight cut-off, zeta potential, etc.) and DBPs (molecular size, electrical property, hydrophobicity, polarity, etc.) fundamentally determine the membrane-filtration performance on retaining DBPs, and the actual operating environmental factors (such as solute concentration, coexisting ions/NOMs, pH and transmembrane pressure) exert a positive/negative impact on performance to some extent. Current researches indicate that NF and RO can be effective in removing DBPs, and looking forward, we recommend that multiple factors should be taken into account that optimize the existed membrane-filtration technologies, rationalize the selection of membrane products, and develop novel membrane materials targeting the removal of DBPs.

Tuning nanofiltration membrane performance: OH-MoS2 nanosheet engineering and divalent cation influence on fouling and organic removal

Natural organic matter (NOM) present in surface water causes severe organic fouling of nanofiltration (NF) membranes employed for the production of potable water. Calcium (Ca2+) and magnesium (Mg2+) are alkaline earth metals present in natural surface water and severely exacerbate organic fouling owing to their ability to cause charge neutralization, complexation, and bridging of NOM and the membrane surface. Hence, it is of practical significance to engineer membranes with properties suitable for addressing organic fouling in the presence of these cations. This study employed OH-functionalized molybdenum disulphide (OH-MoS2) nanosheets as nanofillers via the interfacial polymerization reaction to engineer NF membranes for enhanced removal of NOM and fouling mitigation performance. At an optimized concentration of 0.010 wt.% of OH-MoS2 nanosheet, the membrane was endowed with higher hydrophilicity, negative charge and rougher membrane morphology which enhanced the pure water permeance by 46.33% from 11.2 to 16.39 L m-2 h-1 bar-1 while bridging the trade-off between permeance and salt selectivity. The fouling performance was evaluated using humic acid (HA) and sodium alginate (SA), which represent the hydrophobic and hydrophilic components of NOM in the presence of 0, 0.5, and 1 mM Ca2+ and Mg2+, respectively, and the performance was benchmarked with control and commercial membranes. The modified membrane exhibited normalized fluxes of 95.09% and 93.26% for HA and SA, respectively, at the end of the 6 h filtration experiments, compared to the control membrane at 89.71% and 74.25%, respectively. This study also revealed that Ca2+ has a more detrimental effect than Mg2+ on organic fouling and NOM removal. The engineered membrane outperformed the commercial and the pristine membranes during fouling tests in the presence of 1 mM Ca2+ and Mg2+ in the feed solution. In summary, this study has shown that incorporating OH-MoS2 nanosheets into membranes is a promising strategy for producing potable water from alternative water sources with high salt and NOM contents.

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.

Removal of natural organic matter from surface water sources by nanofiltration and surface engineering membranes for fouling mitigation - A review

Given that surface water is the primary supply of drinking water worldwide, the presence of natural organic matter (NOM) in surface water presents difficulties for water treatment facilities. During the disinfection phase of the drinking water treatment process, NOM aids in the creation of toxic disinfection by-products (DBPs). This problem can be effectively solved using the nanofiltration (NF) membrane method, however NOM can significantly foul NF membranes, degrading separation performance and membrane integrity, necessitating the development of fouling-resistant membranes. This review offers a thorough analysis of the removal of NOM by NF along with insights into the operation, mechanisms, fouling, and its controlling variables. In light of engineering materials with distinctive features, the potential of surface-engineered NF membranes is here critically assessed for the impact on the membrane surface, separation, and antifouling qualities. Case studies on surface-engineered NF membranes are critically evaluated, and properties-to-performance connections are established, as well as challenges, trends, and predictions for the field's future. The effect of alteration on surface properties, interactions with solutes and foulants, and applications in water treatment are all examined in detail. Engineered NF membranes containing zwitterionic polymers have the greatest potential to improve membrane permeance, selectivity, stability, and antifouling performance. To support commercial applications, however, difficulties related to material production, modification techniques, and long-term stability must be solved promptly. Fouling resistant NF membrane development would be critical not only for the water treatment industry, but also for a wide range of developing applications in gas and liquid separations.

Quantification of interfacial interaction related with adhesive membrane fouling by genetic algorithm back propagation (GABP) neural network

Since adhesive membrane fouling is critically determined by the interfacial interaction between a foulant and a rough membrane surface, efficient quantification of the interfacial interaction is critically important for adhesive membrane fouling mitigation. As a current available method, the advanced extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory involves complicated rigorous thermodynamic equations and massive amounts of computation, restricting its application. To solve this problem, artificial intelligence (AI) visualization technology was used to analyze the existing literature, and the genetic algorithm back propagation (GABP) artificial neural network (ANN) was employed to simplify thermodynamic calculation. The results showed that GABP ANN with 5 neurons could obtain reliable prediction performance in seconds, versus several hours or even days time-consuming by the advanced XDLVO theory. Moreover, the regression coefficient (R) of GABP reached 0.9999, and the error between the prediction results and the simulation results was less than 0.01%, indicating feasibility of the GABP ANN technique for quantification of interfacial interaction related with adhesive membrane fouling. This work provided a novel strategy to efficiently optimize the thermodynamic prediction of adhesive membrane fouling, beneficial for better understanding and control of adhesive membrane fouling.

Deep reinforcement learning in an ultrafiltration system: Optimizing operating pressure and chemical cleaning conditions

Enhancing engineering efficiency and reducing operating costs are permanent subjects that face all engineers over the world. To effectively improve the performance of filtration systems, it is necessary to determine an optimal operating condition beyond conventional methods of periodic and empirical operation. Herein, this paper proposes an effective approach to finding an optimal operating strategy using deep reinforcement learning (DRL), particularly for an ultrafiltration (UF) system. Deep learning was developed to represent the UF system utilizing a long-short term memory and provided an environment for DRL. DRL was designed to control three actions; operating pressure, cleaning time, and cleaning concentration. Ultimately, DRL proposed the UF system to actively change the operating pressure and cleaning conditions over time toward better water productivity and operating efficiency. DRL denoted ∼20.9% of specific energy consumption can be reduced by increasing average water flux (39.5-43.7 L m^-2 h^-1) and reducing operating pressure (0.617-0.540 bar). Moreover, the optimal action of DRL was reasonable to achieve better performance beyond the conventional operation. Crucially, this study demonstrated that due to the nature of DRL, the approach is tractable for engineering systems that have structurally complex relationships among operating conditions and resultants.

Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review

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.

A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring

Artificial-intelligence methods and machine-learning models have demonstrated their ability to optimize, model, and automate critical water- and wastewater-treatment applications, natural-systems monitoring and management, and water-based agriculture such as hydroponics and aquaponics. In addition to providing computer-assisted aid to complex issues surrounding water chemistry and physical/biological processes, artificial intelligence and machine-learning (AI/ML) applications are anticipated to further optimize water-based applications and decrease capital expenses. This review offers a cross-section of peer reviewed, critical water-based applications that have been coupled with AI or ML, including chlorination, adsorption, membrane filtration, water-quality-index monitoring, water-quality-parameter modeling, river-level monitoring, and aquaponics/hydroponics automation/monitoring. Although success in control, optimization, and modeling has been achieved with the AI methods, ML models, and smart technologies (including the Internet of Things (IoT), sensors, and systems based on these technologies) that are reviewed herein, key challenges and limitations were common and pervasive throughout. Poor data management, low explainability, poor model reproducibility and standardization, as well as a lack of academic transparency are all important hurdles to overcome in order to successfully implement these intelligent applications. Recommendations to aid explainability, data management, reproducibility, and model causality are offered in order to overcome these hurdles and continue the successful implementation of these powerful tools.

Membrane technologies for sports supplementation

The important developments in membrane techniques used in the dairy industrial processes to whey manufacturing are discussed. Particular emphasis is placed on the description of membrane processes, characterization of protein products, biological issues related to bacteriophages contamination, and modeling of the processes. This choice was dictated by the observed research works and consumer trends, who increasingly appreciate healthy food and its taste qualities.