Microalgae to biodiesel: A novel green conversion method for high-quality lipids recovery and in-situ transesterification to fatty acid methyl esters.

Greenhouse gases (GHGs) emissions due to increasing energy demand have raised the need to identify effective solutions to produce clean and renewable energy. Biotechnologies are an effective platform to attain green transition objectives, especially when synergically integrated to promote health and environmental protection. In this context, microalgae-based biotechnologies are considered among the most effective tools for treating gaseous effluents and simultaneously capturing carbon sources for further biomass valorisation. The production of biodiesel is regarded as a promising avenue for harnessing value from residual algal biomass. Nonetheless, the existing techniques for extracting lipids still face certain limitations, primarily centred around the cost-effectiveness of the process.This study is dedicated to developing and optimising an innovative and cost-efficient technique for extracting lipids from algal biomass produced during gaseous emissions treatment based on algal-bacterial biotechnology. This integrated treatment technology combines a bio-scrubber for degrading gaseous contaminants and a photobioreactor for capturing the produced CO2 within valuable algal biomass. The cultivated biomass is then processed with the process newly designed to extract lipids simultaneously transesterificated in fatty acid methyl esters (FAME) via In Situ Transesterification (IST) with a Kumagawa-type extractor. The results of this study demonstrated the potential application of the optimised method to overcome the gap to green transition. Energy production was obtained from residuals produced during the necessary treatment of gaseous emissions. Using hexane-methanol (v/v = 19:1) mixture in the presence KOH in Kumagawa extractor lipids were extracted with extraction yield higher than 12% and converted in fatty acid methyl esters. The process showed the enhanced extraction of lipids converted in bio-sourced fuels with circular economy approach, broadening the applicability of biotechnologies as sustainable tools for energy source diversification.

Soluble natural sweetener from date palm (Phoenix dactylifera L.) extract using colloidal gas aphrons generated with a food-grade non-ionic surfactant.

Date palm (Phoenix dactylifera L.) is the most commonly cultivated fruit tree in the Middle East and North Africa. Date fruits are an excellent source of nutrition due to their high sugar content and high levels of phenols, minerals, and antioxidants. This work aimed to prepare a soluble natural sweetener from date fruit extract using colloidal gas aprons (CGAs) generated with a food-grade non-ionic surfactant (Tween 20). Various process parameters, such as the flow rate of the CGAs, the volume of the feed, the temperature of the CGAs, and the feed solution, were varied to obtain the optimal parameters. In the foam phase, the maximum soluble sugar enrichment of 92% was obtained at a flow rate of 50 mL/min of CGA and a solution temperature of 23 °C. The formation of intermolecular hydrogen bonding between the glucose molecules and the surfactant Tween 20 was confirmed by molecular modeling studies. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-023-05907-9.

Whole-genome sequencing for One Health surveillance of antimicrobial resistance in conflict zones: a case study of Salmonella spp. and Campylobacter spp. in the West Bank, Palestine.

Antimicrobial resistance (AMR) is a critical global concern driven by the overuse, misuse, and/or usage of inadequate antibiotics on humans, animals' agriculture, and as a result of contaminated environments. This study is the first One Health survey in the Middle East that incorporated whole-genome sequencing (WGS) to examine the spread of AMR in Campylobacter spp. and Salmonella spp. This cross-sectional study was conducted to examine the role of AMR at the human-animal-environmental interface and was performed in Ramallah/Al-Bireh and Jerusalem governorates of the central West Bank, Palestine. In 2021 and 2022, a total of 592 samples were collected and analyzed. From a total of 65 Campylobacter jejuni and 19 Salmonella spp. isolates, DNA was extracted for WGS using Oxford Nanopore Technologies MinION platform. We found that the dominant serotypes of C. jejuni and Salmonella enterica were present in chicken manure, chicken meat sold in markets, and feces of asymptomatic farm workers, with high genetic similarities between the isolates regardless of origin. Additionally, our results showed rapid strain turnover in C. jejuni from the same sites between 2021 and 2022. Most of the positive Salmonella spp. samples were multidrug-resistant (MDR) S. enterica serovar Muenchen carrying the plasmid of emerging S. infantis (pESI) megaplasmid, conferring resistance to multiple antibiotics. Our findings highlight the spread of MDR foodborne pathogens from animals to humans through the food chain, emphasizing the importance of a One Health approach that considers the interconnections between human, animal, and environmental health. IMPORTANCE Prior to this study, there existed hardly an integrated human-animal-environmental study of Salmonellosis and Campylobacteriosis and related AMR in Middle Eastern countries. The few existing studies lack robust epidemiological study designs, adequate for a One Health approach, and did not use WGS to determine the circulating serotypes and their AMR profiles. Civil unrest and war in Middle Eastern countries drive AMR because of the breakdown of public health and food security services. This study samples simultaneously humans, animals, and the environment to comprehensively investigate foodborne pathogens in the broiler chicken production chain in Palestine using WGS. We show that identical serotypes of C. jejuni and S. enterica can be found in samples from chicken farms, chicken meat sold in markets, and asymptomatic broiler chicken production workers. The most striking feature is the rapid dynamic of change in the genetic profile of the detected species in the same sampling locations. The majority of positive Salmonella spp. samples are MDR S. enterica serovar Muenchen isolates carrying the pESI megaplasmid. The results demonstrate a close relationship between the S. enterica serovar Muenchen isolates found in our sample collection and those responsible for 40% of all clinical Salmonella spp. isolates in Israel as previously reported, with a sequence identity of over 99.9%. These findings suggest the transboundary spread of MDR S. enterica serovar Muenchen strains from animals to humans through the food chain. The study underscores the importance of combining integrated One Health studies with WGS for detecting environmental-animal-human transmission of foodborne pathogens that could not be detected otherwise. This study showcases the benefits of integrated environmental-animal-human sampling and WGS for monitoring AMR. Environmental samples, which may be more accessible in conflict-torn places where monitoring systems are limited and regulations are weak, can provide an effective AMR surveillance solution. WGS of bacterial isolates provides causal inference of the distribution and spread of bacterial serotypes and AMR in complex social-ecological systems. Consequently, our results point toward the expected benefits of operationalizing a One Health approach through closer cooperation of public and animal health and food safety authorities.

Colloidal gas aphrons for biotechnology applications: a mini review.

Colloidal gas aphrons (CGAs) are highly stable, spherical, micrometer-sized bubbles encapsulated by surfactant multilayers. They have several intriguing properties, including: high stability, large interfacial area, and the ability to maintain the same charge as their parent molecules. The physical properties of CGAs make them ideal for biotechnological applications such as the recovery of a variety of: biomolecules, particularly proteins, yeast, enzymes, and microalgae. In this review, the bio-application of CGAs for the recovery of natural components is presented, as well as: experimental results, technical challenges, and critical research directions for the future. Experimental results from the literature showed that the recovery of biomolecules was mainly determined by electrostatic or hydrophobic interactions between polyphenols and proteins (lysozyme, ß-casein, ß-lactoglobulin, etc.), yeast, biological molecules (gallic acid and norbixin), and microalgae with CGAs. Knowledge transfer is essential for commercializing CGA-based bio-product recovery, which will be recognized as a viable technology in the future.

Detection of SARS-CoV-2 in clinical and environmental samples using highly sensitive reduced graphene oxide (rGO)-based biosensor.

Quantitative RT-PCR (qRT-PCR) is the most commonly used diagnostic tool for SARS-CoV-2 detection during the COVID-19 pandemic. Despite its sensitivity and accuracy, qRT-PCR is a time-consuming method that requires expensive laboratories with highly trained personnel. In this work, on-site detection of SARS-CoV-2 in municipal wastewater was investigated for the first time. The wastewater was unprocessed and did not require any prefiltration, prior spiking with virus, or viral concentration in order to be suitable for use with the biosensor. The prototype reported here is a reduced graphene oxide (rGO)-based biosensor for rapid, sensitive and selective detection of SARS-CoV-2. The biosensor achieved a limit of detection (LOD) of 0.5 fg/mL in phosphate-buffered saline (PBS) and exhibited specificity when exposed to various analytes. The response time was measured to be around 240 ms. To further explore the capabilities of the biosensor in real clinical and municipal wastewater samples, three different tests were performed to determine the presence or absence of the virus: (i) qRT-PCR, (ii) a rapid antigen-based commercially available test (COVID-19 Test Strips), and (iii) the biosensor constructed and reported here. Taken together, our results demonstrate that a biosensor that can detect SARS-CoV-2 in clinical samples as well as unfiltered and unprocessed municipal wastewater is feasible.

Surface-engineered polyethersulfone membranes with inherent Fe-Mn bimetallic oxides for improved permeability and antifouling capability.

In recent years, bimetallic oxide nanoparticles have garnered significant attention owing to their salient advantages over monometallic nanoparticles. In this study, Fe2O3-Mn2O3 nanoparticles were synthesized and used as nanomodifiers for polyethersulfone (PES) ultrafiltration membranes. A NIPS was used to fabricate asymmetric membranes. The effect of nanoparticle concentration (0-1 wt.%) on the morphology, roughness, wettability, porosity, permeability, and protein filtration performance of the membranes was investigated. The membrane containing 0.25 wt% nanoparticles exhibited the lowest water contact angle (67°) and surface roughness (10.4 ± 2.8 nm) compared to the other membranes. Moreover, this membrane exhibited the highest porosity (74%) and the highest pure water flux (398 L/m2 h), which was 16% and 1.9 times higher than that of the pristine PES membrane. The modified PES membranes showed an improved antifouling ability, especially against irreversible fouling. Bovine serum albumin protein-based dynamic five-cycle filtration tests showed a maximum flux recovery ratio of 77% (cycle-1), 67% (cycle-2), and 65.8% (cycle-5) for the PES membrane containing 0.25 wt% nanoparticles. Overall, the biphasic Fe2O3-Mn2O3 nanoparticles were found to be an effective nanomodifier for improving the permeability and antifouling ability of PES membranes in protein separation and water treatment applications.

Surface tuned polyethersulfone membrane using an iron oxide functionalized halloysite nanocomposite for enhanced humic acid removal.

Nanomodification of ultrafiltration (UF) membranes has been shown to be a simple and efficient technique for the preparation of high-performance membranes. In this work, an iron oxide functionalized halloysite nanoclay (Fe-HNC) nanocomposite was prepared and used as a nanofiller for polyethersulfone (PES) membranes. The effect of Fe-HNC concentration on the filtration performance of the membrane was investigated by varying the nanocomposite dosage (0-0.5 wt %) in the casting dope. Various characterization studies showed that the incorporation of Fe-HNC nanocomposites improved the membrane morphology and enhanced the surface properties, thermal stability, mechanical strength, hydrophilicity, and porosity. The permeability to pure water and filtration of humic acid (HA) were significantly improved by incorporating Fe-HNC into the PES membranes. The membrane with Fe-HNC loading of 0.1 wt % exhibited the highest pure water permeability (174.3 L/(m2 h bar)) and removal of HA (90.1 %), which were 1.8 times and 29 % higher, respectively than the pristine PES membrane. Moreover, fouling studies showed the enhanced antifouling ability of the Fe-HNC nanocomposites modified PES membranes, especially against irreversible fouling. Continuous membrane regeneration-based fouling removal studies from HA showed that the PES/0.1 wt % Fe-HNC membrane exhibited a high fouling recovery of 70.4 % with very low reversible and irreversible fouling resistance of 9.61 % and 14.78 %, respectively, compared to the pristine PES membrane (fouling recovery: 40.4 %; reversible fouling: 21.7 %; irreversible fouling: 20.1 %). Overall, the Fe-HNC nanocomposite proved to be an effective nanomodifier for improving the permeability of PES membranes and the antifouling ability to treat HA polluted aqueous streams.

Cross-linked laminar graphene oxide membranes for wastewater treatment and desalination: A review.

Two-dimensional (2D) lamellar graphene oxide (GO) membranes are emerging as attractive materials for molecular separation in water treatment because of their single atomic thickness, excellent hydrophilicity, large specific surface areas, and controllable properties. To yet, commercialization of GO laminar membranes has been hindered by their propensity to swell in hydrated conditions. Thus, chemical crosslinking of GO sheets with the polymer matrix is used to improve GO membrane hydration stability. This review focuses on pertinent themes such as how chemical crosslinking improves the hydration stability, separation performance, and antifouling properties of GO membranes.

Hybrid capacitive deionization of NaCl and toxic heavy metal ions using faradic electrodes of silver nanospheres decorated pomegranate peel-derived activated carbon.

Capacitive deionization (CDI) is an evolving technology for eradicating salt and toxic heavy metal ions from brackish wastewater. However, traditional CDI electrodes have lower salt adsorption capacity and inadequate adsorption of selective metal ions for long-term operations. Herein, Ag nanospheres incorporated pomegranate peel-derived activated carbon (Ag/P-AC) was prepared and implied to the CDI process for removing NaCl, toxic mono-, di-, and trivalent metal ions. Morphological analysis revealed that the 80-100 nm-sized Ag nanospheres were uniformly decorated on the surfaces of P-AC nanosheets. The Ag/P-AC has a higher specific surface area (640 m2 g-1), superior specific capacitance (180 F g-1 at 50 mV s-1) and a lower charge transfer resistance (0.5 Ω cm2). CDI device was fabricated by Ag/P-AC as an anode, which adsorbed anions and P-AC as cathode for adsorption of positively charged ions at 1.2 V in an initial salt concentration of 1000 mg L-1. An asymmetric Ag/P-AC//P-AC exhibited a maximum NaCl adsorption capacity of 36 mg g-1 than symmetric P-AC//P-AC electrodes (22.7 mg g-1). Furthermore, Pb(II), Cd(II), F-, and As(III) ions were successfully removed from simulated wastewater by using Ag/P-AC//P-AC based CDI system. These asymmetric CDI-electrodes have an excellent prospect for the removal of salt and toxic contaminants in industrial wastewater.

Synthesis of polyethersulfone (PES)/GO-SiO2 mixed matrix membranes for oily wastewater treatment.

The treatment of oily wastewater continues to pose a challenge in industries worldwide. Membranes have been investigated recently for their use in oily wastewater treatment due to their efficiency and relatively facile operational process. Graphene oxide (GO) and silica (SiO2) nanoparticles have been found to improve membrane properties. In this study, a polyethersulfone (PES) based GO-SiO2 mixed matrix membrane (MMM) was fabricated, using the phase inversion technique, for the treatment of oil refinery wastewater. The PES/GO-SiO2 membrane exhibited the highest water flux (2,561 LMH) and a 38% increase in oil removal efficiency by comparison to a PES membrane. Compared to PES/GO and PES/SiO2 membranes, the PES/GO-SiO2 MMM also displayed the best overall properties in terms of tensile strength, water permeability, and hydrophilicity.

A conceptual framework modeling of functional microbial communities in wastewater treatment electro-bioreactors.

Understanding the microbial ecology of a system allows linking members of the community and their metabolic functions to the performance of the wastewater bioreactor. This study provided a comprehensive conceptual framework for microbial communities in wastewater treatment electro-bioreactors (EBRs). The model was based on data acquired from monitoring the effect of altering different bioreactor operational parameters, such as current density and hydraulic retention time, on the microbial communities of an EBR and its nutrient removal efficiency. The model was also based on the 16S rRNA gene high-throughput sequencing data analysis and bioreactor efficiency data. The collective data clearly demonstrated that applying various electric currents affected the microbial community composition and stability and the reactor efficiency in terms of chemical oxygen demand, N and P removals. Moreover, a schematic that recommends operating conditions that are tailored to the type of wastewater that needs to be treated based on the functional microbial communities enriched at specific operating conditions was suggested. In this study, a conceptual model as a simplified representation of the behavior of microbial communities in EBRs was developed. The proposed conceptual model can be used to predict how biological treatment of wastewater in EBRs can be improved by varying several operating conditions.

Numerical modeling of an electrically enhanced membrane bioreactor (MBER) treating medium-strength wastewater.

In this paper, a numerical model of an electrically enhanced membrane bioreactor (MBER) was developed. MBER is a reactor that combines biological decomposition, membrane filtration and electrocoagulation of wastewater pollutants in a hybrid unit. To assess its design, the final contents and removal efficiencies of organics, nutrients, and metals were carried out using varying influent compositions. In a 60-day test of a laboratory-scale MBER, experimental results were used to calibrate and validate the model. The modeling results were in agreement with the experimental data and showed that the MBER can remove 99% of total phosphorus (TP), 99.9% of chemical oxygen demand (COD), 91% of total nitrogen (TN), 79% of nickel (Ni), 89% of iron (Fe), and 80% of chromium (Cr), using a current density of 15 A/m(2) intermittently supplied in a cycle of 5 min ON and 15 min OFF.

Enhanced sludge properties and distribution study of sludge components in electrically-enhanced membrane bioreactor.

This study investigated the impact of electric field on the physicochemical and biological characteristics of sludge wasted from an electrically-enhanced membrane bioreactor treating medium-strength raw wastewater. This method offers a chemical-free electrokinetic technique to enhance sludge properties and remove heavy metals. For example, sludge volume index (SVI), time-to-filter (TTF), mean sludge particle diameter (PSD), viscosity, and oxidation-reduction potential (ORP) of 21.7 mL/g, 7 min, 40.2 µm, 3.22 mPa s, and -4.9 mV were reported, respectively. Also, X-ray fluorescence (XRF) and X-ray diffraction (XRD) analyses provided mechanisms for heavy metal removal so as to establish relevant pathways for nutrient recovery. Furthermore, variations in dissolved oxygen (DO), conductivity, viscosity, ORP, total suspended solids (MLSS), and volatile suspended solids (MLVSS) were interrelated to evaluate the quality of wasted sludge. A pathway study on the transport and chemical distribution of nutrients and metals in sludge showed great potential for metal removal and nutrient recovery.

Recent improvements in oily wastewater treatment: Progress, challenges, and future opportunities.

Oily wastewater poses significant threats to the soil, water, air and human beings because of the hazardous nature of its oil contents. The objective of this review paper is to highlight the current and recently developed methods for oily wastewater treatment through which contaminants such as oil, fats, grease, and inorganics can be removed for safe applications. These include electrochemical treatment, membrane filtration, biological treatment, hybrid technologies, use of biosurfactants, treatment via vacuum ultraviolet radiation, and destabilization of emulsions through the use of zeolites and other natural minerals. This review encompasses innovative and novel approaches to oily wastewater treatment and provides scientific background for future work that will be aimed at reducing the adverse impact of the discharge of oily wastewater into the environment. The current challenges affecting the optimal performance of oily wastewater treatment methods and opportunities for future research development in this field are also discussed.

Analytical methods for determining environmental contaminants of concern in water and wastewater.

Control and prevention of environmental pollution have emerged as paramount global concerns. Anthropogenic activities, such as industrial discharges, agricultural runoff, and improper waste disposal, introduce a wide range of contaminants into various ecosystems. These pollutants encompass organic and inorganic compounds, particulates, microorganisms, and disinfection by-products, posing severe threats to human health, ecosystems, and the environment. Effective monitoring methods are indispensable for assessing environmental quality, identifying pollution sources, and implementing remedial measures. This paper suggests that the development and utilization of highly advanced analytical tools are both essential for the analysis of contaminants in water samples, presenting a foundational hypothesis for the review. This paper comprehensively reviews the development and utilization of highly advanced analytical tools which is mandatory for the analysis of contaminants in water samples. Depending on the specific pollutants being studied, the choice of analytical methods widely varies. It also reveals insights into the diverse applications and effectiveness of these methods in assessing water quality and contaminant levels. By emphasizing the critical role of the reviewed monitoring methods, this review seeks to deepen the understanding of pollution challenges and inspire innovative monitoring solutions that contribute to a cleaner and more sustainable global environment.•Urgent global concerns: control and prevention of pollution from diverse sources.•Varied contaminants, diverse methods: comprehensive review of analytical tools.•Inspiring a sustainable future: innovative monitoring for a cleaner environment.

Novel eco-friendly polylactic acid nanocomposite integrated membrane system for sustainable wastewater treatment: Performance evaluation and antifouling analysis.

Water contamination caused by heavy metals, nutrients, and organic pollutants of varying particle sizes originating from domestic and industrial processes poses a significant global challenge. There is a growing concern, particularly regarding the presence of heavy metals in freshwater sources, as they can be toxic even at low concentrations, posing risks to human health and the environment. Currently, membrane technologies are recognized as effective and practical for treating domestic and industrial wastewater. However, these technologies are hindered by fouling issues. Furthermore, the utilization of conventional membranes leads to the accumulation of non-recyclable synthetic polymers, commonly used in their production, resulting in adverse environmental consequences. In light of our previously published studies on environmentally friendly, biodegradable polylactic acid (PLA) nanocomposite mixed matrix membranes (MMMs), we selected two top-performing PLA-based ultrafiltration nanocomposite membranes: one negatively charged (PLA-M-) and one positively charged (PLA-M+). We integrated these membranes into systems with varying arrangements to control fouling and eliminate heavy metals, organic pollutants, and nutrients from raw municipal wastewater collected by the local wastewater treatment plant in Abu Dhabi (UAE). The performance of two integrated systems (i.e., PLA-M+/PLA-M- and PLA-M-/PLA-M+) was compared in terms of permeate flux, contaminant removal efficiencies, and fouling mitigation. The PLA-M+/PLA-M- system achieved removal efficiencies of 79.6 %, 92.6 %, 88.7 %, 85.2 %, 98.9 %, 94 %, 83.3 %, and 98.3 % for chemical oxygen demand (COD), nitrate (NO3--N), phosphate (PO43--P), ammonium (NH4+-N), iron (Fe), zinc (Zn), nickel (Ni), and copper (Cu), respectively. On the other hand, the PLA-M-/PLA-M+ system recorded removal efficiencies of 85.8 %, 95.9 %, 100 %, 81.9 %, 99.3 %, 91.9 %, 72.9 %, and 98.9 % for COD, NO3--N, PO43--P, NH4+-N, Fe, Zn, Ni, and Cu, respectively. Notably, the PLA-M-/PLA-M+ system demonstrated superior antifouling resistance, making it the preferred integrated system. These findings demonstrate the potential of eco-friendly PLA nanocomposite UF-MMMs as a promising alternative to petroleum-based polymeric membranes for efficient and sustainable wastewater treatment.

Comprehensive review on pH and temperature-responsive polymeric adsorbents: Mechanisms, equilibrium, kinetics, and thermodynamics of adsorption processes for heavy metals and organic dyes.

Wastewater treatment technologies have been developed to address the health and environmental risks associated with toxic and cancer-causing dyes and heavy metals found in industrial waste. The most commonly used method to mitigate and treat such effluents is adsorption, which is favored for its high efficiency, low costs, and ease of operation. However, traditional adsorbents have limitations in terms of regeneration and selectivity compared to smart adsorbents. Smart polymeric adsorbents, on the other hand, can undergo physical and chemical changes in response to external factors like temperature and pH, enabling a selective adsorption process. These adsorbents can be easily regenerated and reused with minimal generation of secondary pollutants during desorption. The unique properties acquired by stimuli-responsive adsorbents have encouraged researchers to investigate their potential for the selective and efficient removal of organic dyes and heavy metals. This comprehensive review focuses on two common stimuli, pH and temperature, discussing the fabrication methods and characteristics of smart adsorbents responsive to these factors. It also provides an overview of the mechanisms, isotherms, kinetics, and thermodynamics of the adsorption process for each type of stimuli-responsive adsorbent. Finally, the review concludes with discussions on future perspectives and considerations.

Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives.

Membrane technologies have become proficient alternatives for advanced wastewater treatment, ensuring high contaminant removal and sustainable resource recovery. Despite significant progress, ongoing research efforts aim to further optimize treatment performance. Among the challenges faced, membrane fouling persists as a relevant obstacle in membrane technologies, necessitating the development of more effective mitigation strategies. Mathematical models, widely employed for predicting treatment performance, generally exhibit low accuracy and suffer from uncertainties due to the complex and variable nature of wastewater. To overcome these limitations, numerous studies have proposed artificial intelligence (AI) modeling to accurately predict membrane technologies' performance and fouling mechanisms. This approach aims to provide advanced simulations and predictions, thereby enhancing process control, optimization, and intensification. This literature review explores recent advancements in modeling membrane-based wastewater treatment processes through AI models. The analysis highlights the enormous potential of this research field in enhancing the efficiency of membrane technologies. The role of AI modeling in defining optimal operating conditions, developing effective strategies for membrane fouling mitigation, enhancing the performance of novel membrane-based technologies, and improving membrane fabrication techniques is discussed. These enhanced process optimization and control strategies driven by AI modeling ensure improved effluent quality, optimized resource consumption, and minimized operating costs. The potential contribution of this cutting-edge approach to a paradigm shift toward sustainable wastewater treatment is examined. Finally, this review outlines future perspectives, emphasizing the research challenges that require attention to overcome the current limitations hindering the integration of AI modeling in wastewater treatment plants.

Design and Machine Learning Prediction of In Situ Grown PDA-Stabilized MOF (UiO-66-NH2) Membrane for Low-Pressure Separation of Emulsified Oily Wastewater.

Significant progress has been made in designing advanced membranes; however, persistent challenges remain due to their reduced permeation rates and a propensity for substantial fouling. These factors continue to pose significant barriers to the effective utilization of membranes in the separation of oil-in-water emulsions. Metal-organic frameworks (MOFs) are considered promising materials for such applications; however, they encounter three key challenges when applied to the separation of oil from water: (a) lack of water stability; (b) difficulty in producing defect-free membranes; and (c) unresolved issue of stabilizing the MOF separating layer on the ceramic membrane (CM) support. In this study, a defect-free hydrolytically stable zirconium-based MOF separating layer was formed through a two-step method: first, by in situ growth of UiO-66-NH2 MOF into the voids of polydopamine (PDA)-functionalized CM during the solvothermal process, and then by facilitating the self-assembly of UiO-66-NH2 with PDA using a pressurized dead-end assembly. A stable MOF separating layer was attained by enriching the ceramic support with amines and hydroxyl groups using PDA, which assisted in the assembly and stabilization of UiO-66-NH2. The PDA-s-UiO-66-NH2-CM membrane displayed air superhydrophilicity and underwater superoleophobicity, demonstrating its oil resistance and high antifouling behavior. The PDA-s-UiO-66-NH2-CM membrane has shown exceptionally high permeability and separation capacity for challenging oil-in-water emulsions. This is attributed to numerous nanochannels from the membrane and its high resistance to oil adhesion. The membranes showed excellent stability over 15 continuous test cycles, which indicates that the developed MOFs separating layers have a low tendency to be clogged by oil droplets during separation. Machine learning-based Gaussian process regression (GPR) models as nonparametric kernel-based probabilistic models were employed to predict the performance efficiency of the PDA-s-UiO-66-NH2-CM membrane in oil-in-water separation. The outcomes were compared with the support vector machine (SVM) and decision tree (DT) algorithm. This efficiency includes various metrics related to its separation accuracy, and the models were developed through feature engineering to identify and utilize the most significant factors affecting the membrane's performance. The results proved the reliability of GPR optimization with the highest prediction accuracy in the validation phase. The average percentage increase of the GPR model compared to the SVM and DT model was 6.11 and 42.94%, respectively.