Evaluating an on-line cleaning agent for mitigating organic fouling in a reverse osmosis membrane

Recent advancement on water filtration membranes: Navigating biofouling challenges

This study investigates the field of antifouling membranes for water filtration and desalination applications, specifically focusing on two-dimensional materials. The study examines the importance of these membranes in the context of climate change and its effects on coastal ecosystems. The occurrence of biofouling in seawater desalination membranes is closely connected to intricate processes influenced by factors such as water quality, microbial communities, hydrodynamics, and membrane properties. Microorganism adhesion initiates the process, which then advances into irreversible attachment and the creation of biofilm. Detached pieces contribute to the perpetuation of fouling. Biofouling is caused by a variety of biomaterials and organics, including bacteria, extracellular polymeric substances (EPS), proteins, and humic compounds. Innovative methods such as surface alterations using two-dimensional materials like graphene and graphene oxide, as well as the use of biofouling-resistant materials, provide promising possibilities. These materials have antifouling characteristics, making them environmentally beneficial options that reduce the need for chemical cleaning. Their application improves the water treatment process by preventing fouling and enhancing membrane performance. Real-world research applications can enhance and optimize these tactics to effectively reduce biofouling in seawater desalination systems, hence improving efficiency and sustainability. This is particularly important in light of climate change and its impact on coastal ecosystems. The findings obtained from the literature review emphasise the utmost significance of tackling biofouling in the face of a changing environment, particularly with regard to microorganisms. Important factors to consider are the selection of coating materials, the implementation of environmentally friendly cleaning solutions made from natural chemicals, and the improvement of pretreatment systems. Green cleaning agents are important eco-friendly alternatives to typical biocides, as they possess antibacterial, antifungal, and antifouling capabilities. Given the existence of climate change, these observations serve as a basis for promoting environmentally friendly methods in water treatment technology.

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.

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.