Nickel augmented biochar for sustaining produced water treatment to decarbonize oil and gas industrial waste using anaerobic-aerobic granular cylindrical periodic discontinuous batch reactors.

This study assessed the efficacy of granular cylindrical periodic discontinuous batch reactors (GC-PDBRs) for produced water (PW) treatment by employing eggshell and waste activated sludge (WAS) derived Nickel (Ni) augmented biochar. The synthesized biochar was magnetized to further enhance its contribution towards achieving carbon neutrality due to carbon negative nature, Carbon dioxide (CO2) sorption, and negative priming effects. The GC-PDBR1 and GC-PDBR2 process variables were optimized by the application of central composite design (CCD). This is to maximize the decarbonization rate. Results showed that the systems could reduce total phosphorus (TP) and chemical oxygen demand (COD) by 76-80% and 92-99%, respectively. Optimal organic matter and nutrient removals were achieved at 80% volumetric exchange ratio (VER), 5 min settling time and 3000 mg/L mixed liquor suspended solids (MLSS) concentration with desirability values of 0.811 and 0.954 for GC-PDBR1 and GC-PDBR2, respectively. Employing four distinct models, the biokinetic coefficients of the GC-PDBRs treating PW were calculated. The findings indicated that First order (0.0758-0.5365) and Monod models (0.8652-0.9925) have relatively low R2 values. However, the Grau Second-order model and Modified Stover-Kincannon model have high R2 values. This shows that, the Grau Second Order and Modified Stover-Kincannon models under various VER, settling time, and MLSS circumstances, are more suited to explain the removal of pollutants in the GC-PDBRs. Microbiological evaluation demonstrated that a high VER caused notable rises in the quantity of several microorganisms. Under high biological selective pressure, GC-PDBR2 demonstrated a greater percentage of nitrogen removal via autotrophic denitrification and a greater number of nitrifying bacteria. The overgrowth of bacteria such as Actinobacteriota spp. Bacteroidota spp, Gammaproteobacteria, Desulfuromonas Mesotoga in the phylum, class, and genus, has positively impacted on granule formation and stability. Taken together, our study through the introduction of intermittent aeration GC-PDBR systems with added magnetized waste derived biochar, is an innovative approach for simultaneous aerobic sludge granulation and PW treatment, thereby providing valuable contributions in the journey toward achieving decarbonization, carbon neutrality and sustainable development goals (SDGs).

Predicting micropollutant removal through nanopore-sized membranes using several machine-learning approaches based on feature engineering.

Predicting the efficacy of micropollutant separation through functionalized membranes is an arduous endeavor. The challenge stems from the complex interactions between the physicochemical properties of the micropollutants and the basic principles underlying membrane filtration. This study aimed to compare the effectiveness of a modest dataset on various machine learning tools (ML) tools in predicting micropollutant removal efficiency for functionalized reverse osmosis (RO) and nanofiltration (NF) membranes. The inherent attributes of both the micropollutants and the membranes are utilized as input factors. The chosen ML tools are supervised algorithm (adaptive network-based fuzzy inference system (NF), linear regression framework (linear regression (LR)), stepwise linear regression (SLR) and multivariate linear regression (MVR)), and unsupervised algorithm (support vector machine (SVM) and ensemble boosted tree (BT)). The feature engineering and parametric dependency analysis revealed that characteristics of micropollutants, such as maximum projection diameter (MaxP), minimal projection diameter (MinP), molecular weight (MW), and compound size (CS), exhibited a notably positive impact on the correlation with removal efficiency. Model combination with key variables demonstrated high prediction accuracy in both supervised and unsupervised ML for micropollutant removal efficiency. An NF-grid partitioning (NF-GP) model achieved the highest accuracy with an R 2 value of 0.965, accompanied by low error metrics, specifically an RMSE and MAE of 3.65. It is owed to the handling of the complex spatial and temporal aspects of micropollutant data through division into consistent subsets facilitating improved identification of rejection efficiency and relationships. The inclusion of inputs with both negative and positive correlations introduces variability, amplifies the system responsiveness, and impedes the precision of predictive models. This study identified key micropollutant properties, including MaxP, MinP, MW, and CS, as crucial factors for efficient micropollutant rejection during real-time filtration applications. It also allowed the design of pore size of self-prepared membranes for the enhanced separation of micropollutants from wastewater.

Produced water treatment by semi-continuous sequential bioreactor and microalgae photobioreactor.

Produced water (PW) from oil and gas exploration adversely affects aquatic life and living organisms, necessitating treatment before discharge to meet effluent permissible limits. This study first used activated sludge to pretreat PW in a sequential batch reactor (SBR). The pretreated PW then entered a 13 L photobioreactor (PBR) containing Scenedesmus obliquus microalgae culture. Initially, 10% of the PW mixed with 90% microalgae culture in the PBR. After the exponential growth of the microalgae, an additional 25% of PW was added to the PBR without extra nutrients. This study reported the growth performance of microalgae in the PBR as well as the reduction in effluent's total organic carbon (TOC), total dissolved solids (TDS), electrical conductivity (EC), and heavy metals content. The results demonstrated removal efficiencies of 64% for TOC, 49.8% for TDS, and 49.1% for EC. The results also showed reductions in barium, iron, and manganese in the effluent by 95, 76, and 52%, respectively.

Fabrication of Tailored Membranes with Special Surface Wettability Features for Highly Efficient Crude Oil-in-Water Emulsion Separation.

Treating oily wastewater streams such as produced water has a huge potential to resolve the issue of wastewater disposal and generate useful water for reuse. Among different techniques employed for oily wastewater (oil-in-water; O/W emulsion) treatment, membrane-based separation is advantageous owing to its lower energy consumption, recycling, ease of operation, and wider scope of tuning the active layer chemistry for enhanced performance. In line with the possibilities of enhancing the performance of the membranes for efficient O/W emulsion separation, the current work is designed to yield five different variants of polyaniline (PANI) active layers with special surface wettability features (superhyrophilic and underwater superoleophobic) on a ceramic alumina support. To achieve variants of PANI on ceramic alumina supports, emulsion polymerization was carried out, and different concentrations of initiator ammonium persulfate (APS) were applied to lead to PANI-A@Aluminum Oxide membrane, PANI-B@Aluminum Oxide membrane, PANI-C@Aluminum Oxide membrane, PANI-D@Aluminum Oxide membrane, and PANI-E@Aluminum Oxide membrane corresponding to 0.15, 0.25, 0.35, 0.5, and 1.0 M concentrations of initiator. The variation in initiator concentration resulted in different PANI growth patterns; hence, the resultant membranes showed different structural, physical, and performance features. Different characterization techniques including 1H NMR, SEM, FE-TEM, AFM, water contact angle, XRD, EDX, and ATR-FTIR confirmed a more uniform and continuous growth of PANI (PANI-B) using a 0.25 M initiator concentration. The resultant PANI-B@Aluminum Oxide membrane showed an excellent surfactant stabilized crude O/W emulsion separation reaching >99% with a permeate flux of 2154 L m-2 h-1 (LMH) at 4 bar using a 100 ppm surfactant stabilized crude oil-in-water emulsion. The fouling and cleaning cycles revealed that the membrane can be reused with a 70% recovery of the initial permeate flux.

Enhancing Li+ recovery in brine mining: integrating next-gen emotional AI and explainable ML to predict adsorption energy in crown ether-based hierarchical nanomaterials.

Artificial intelligence (AI) is being employed in brine mining to enhance the extraction of lithium, vital for the manufacturing of lithium-ion batteries, through improved recovery efficiencies and the reduction of energy consumption. An innovative approach was proposed combining Emotional Neural Networks (ENN) and Random Forest (RF) algorithms to elucidate the adsorption energy (AE) (kcal mol-1) of Li+ ions by utilizing crown ether (CE)-incorporated honeycomb 2D nanomaterials. The screening and feature engineering analysis of honeycomb-patterned 2D materials and individual CE were conducted through Density Functional Theory (DFT) and Gaussian 16 simulations. The selected honeycomb-patterned 2D materials encompass graphene, silicene, and hexagonal boron nitride, while the specific CEs evaluated are 15-crown-5 and 18-crown-6. The crown-passivated 2D surfaces held a significant adsorption site through van der Waals forces for efficient recovery of Li+ ions. ENN predicted the targeted adsorption sites with high precision and minimal deviation. The eTAI (XAI) based Shapley Additive exPlanations (SHAP) was also explored for insight into the feature importance of CE embedded 2D nanomaterials for the recovery of Li+ ions. The extreme gradient boosting algorithm (XGBoost) model demonstrated a RT-2-MAPE = 0.4618% and ENN-2-MAPE = 0.4839% for the feature engineering analysis. This research would be an insight into the AI-driven nanotechnology that presents a viable and sustainable approach for the extraction of natural resources through the application of brine mining.

Suitability of treated wastewater for irrigation and its impact on groundwater resources in arid coastal regions: Insights for water resources sustainability.

Water scarcity threatens agriculture and food security in arid regions like Saudi Arabia. The nation produces significant quantities of municipal wastewater, which, with adequate treatment, could serve as an alternative water source for irrigation, thereby reducing reliance on fossil and non-renewable groundwater. This study assessed the appropriateness of using treated wastewater (TWW) for irrigation in a dry coastal agricultural region in Eastern Saudi Arabia and its impact on groundwater resources. Field investigations were conducted in Qatif to collect water samples and field measurements. A multi-criteria approach was applied to evaluate the TWW's suitability for irrigation, including complying with Saudi Standards, the Irrigation Water Quality Index (IWQI), the National Sanitation Foundation water quality index (NSFWQI), and the individual irrigation indices. In addition, the impact of TWW on groundwater was assessed through hydrogeological and isotope approaches. The results indicate that the use of TWW in the study area complied with the Saudi reuse guidelines except for nitrate, aluminum, and molybdenum. However, irrigation water quality indices classify TWW as having limitations that necessitate the use for salt-tolerant crops on permeable and well-drained soils. Stable isotopic analysis (δ2H, δ18O) revealed that long-term irrigation with TWW affected the shallow aquifer, while deep aquifers were minimally impacted due to the presence of aquitard layer. The application of TWW irrigation has successfully maintained groundwater sustainability in the study area, as evidenced by increased groundwater levels up to 2.3 m. Although TWW contributes to crop productivity, long term agricultural sustainability could be enhanced by improving effluent quality, regulating irrigation practices, implementing buffer zones, and monitoring shallow groundwater. An integrated approach that combines advanced wastewater treatment methods, community involvement, regulatory oversight, and targeted monitoring is recommended to be implemented.

Insight into soft chemometric computational learning for modelling oily-wastewater separation efficiency and permeate flux of polypyrrole-decorated ceramic-polymeric membranes.

Reliable modeling of oily wastewater emphasizes the paramount importance of sustainable and health-conscious wastewater management practices, which directly aligns with the Sustainable Development Goals (SDG) while also meeting the guidelines of the World Health Organization (WHO). This research explores the efficiency of utilizing polypyrrole-coated ceramic-polymeric membranes to model oily wastewater separation efficiency (SE) and permeate flux (PF) based on established experimental procedures. In this area, computational simulation still needs to be explored. The study developed predictive regression models, including robust linear regression (RLR), stepwise linear regression (SWR) and linear regression (LR) for the ceramic-polymeric porous membrane, aiming to interpret its complex performance across diverse conditions and, thus, develop its utility in oily wastewater treatment applications. Subsequently, a novel, simple average ensemble paradigm was explored to reduce errors and improve prediction skills. Prior to the development of the model, stability and reliability analysis of the data was conducted based on Philip Perron tests with the Bartlett kernel estimation method. The accuracy of the SE exhibited a high consistency, averaging 99.92% with minimal variability (standard deviation of 0.026%), potentially simplifying its prediction compared to PF. The modes were validated and evaluated using metrics like MAE, RMSE, Speed, and MSE, in addition to 2D graphical and cumulative distribution function graphs. The LR model emerged as the best with the lowest RMSE =0.21951, indicating superior prediction accuracy, followed closely by RLR with an RMSE = 0.22359. SWLR, while having the highest RMSE = 0.34573, marked its dominance in prediction speed with 110 observations per second. Notably, the RLR model justified a reduction in error by approximately 35.29% compared to SWLR. Moreover, the training efficiency of the LR model exceeded, demanding a mere 2.9252 s, marking a reduction of about 32.54% compared to SWLR. The improved simple ensemble learning proved merit over the three models regarding error accuracy. This study emphasizes the essential role of soft-computing learning in optimizing the design and performance of ceramic-polymeric membranes.

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.

Trace element pollution tracking in the complex multi-aquifer groundwater system of Al-Hassa oasis (Saudi Arabia) using spatial, chemometric and index-based techniques.

In a global context, trace element pollution assessment in complex multi-aquifer groundwater systems is important, considering the growing concerns about water resource quality and sustainability worldwide. This research addresses multiple objectives by integrating spatial, chemometric, and indexical study approaches, for assessing trace element pollution in the multi-aquifer groundwater system of the Al-Hassa Oasis, Saudi Arabia. Groundwater sampling and analysis followed standard methods. For this purpose, the research employed internationally recognized protocols for groundwater sampling and analysis, including standardized techniques outlined by regulatory bodies such as the United States Environmental Protection Agency (USEPA) and the World Health Organization (WHO). Average values revealed that Cr (0.041) and Fe (2.312) concentrations surpassed the recommended limits for drinking water quality, posing serious threats to groundwater usability by humans. The trace elemental concentrations were ranked as: Li < Mn < Co < As < Mo < Zn < Al < Ba < Se < V < Ni < Cr < Cu < B < Fe < Sr. Various metal(loid) pollution indices, including degree of contamination, heavy metal evaluation index, heavy metal pollution index, and modified heavy metal index, indicated low levels of groundwater pollution. Similarly, low values of water pollution index and weighted arithmetic water quality index were observed for all groundwater points, signifying excellent groundwater quality for drinking and domestic purposes. Spatial distribution analysis showed diverse groundwater quality across the study area, with the eastern and western parts displaying a less desirable quality, while the northern has the best, making water users in the former more vulnerable to potential pollution effects. Thus, the zonation maps hinted the necessity for groundwater quality enhancement from the western to the northern parts. Chemometric analysis identified both human activities and geogenic factors as contributors to groundwater pollution, with human activities found to have more significant impacts. This research provides the scientific basis and insights for protecting the groundwater system and ensuring efficient water management.

Integrated clustering analysis for delineating seawater intrusion and heavy metals in Arabian Gulf Coastal groundwater of Saudi Arabia.

The intrusion of seawater (SWI) into coastal aquifers is a major concern worldwide, affecting the quantity and quality of groundwater resources. The region of Saudi Arabia that lies along the eastern coast has been affected by SWI, making it crucial to accurately identify and monitor the affected areas. This investigation aimed to map the degree of seawater intrusion in a complex aquifer system in the study area using an integrated clustering analysis approach. The study collected 41 groundwater samples from wells penetrating multi-layered aquifers, and the samples were analyzed for physicochemical properties and major ions. Clustering analysis methods, including Hierarchical Clustering Analysis (double-clustering) (HCA-DC), K-mean (KMC), and fuzzy k-mean clustering (FKM), were employed to evaluate the spatial distribution and association of the groundwater properties. The results revealed that the analyzed GW samples were divided into four clusters with varying degrees of SWI. Clusters A, B, C, and D contained GW samples with very low (fsea of 1.9%), high (fsea of 14.9%), intermediate (fsea of 7.9%), and low (fsea of 5.2%) degrees of SWI, respectively. FKM clustering exhibited superior performance with a silhouette score of 0.83. Additionally, the study found a direct correlation between the degree of SWI and increased concentrations of boron, strontium, and iron, demonstrating SWI's impact on heavy metal levels. Notably, the boron concentration in cluster B, which endured high SWI, exceeded WHO guidelines. The study demonstrates the value of clustering analysis for accurately monitoring SWI and associated heavy metals. The findings can guide policies to mitigate SWI impacts and benefit groundwater-dependent communities. Further research can help develop effective strategies to mitigate SWI effects on groundwater quality and availability.

Highly Permeable Sulfonated Polydopamine Integrated MXene Membranes for Efficient Surfactant-Stabilized Oil-in-Water Separation.

MXene is an incredibly promising two-dimensional material with immense potential to serve as a high-performing separating or barrier layer to develop advanced membranes. Despite the significant progress made in MXene membranes, two major challenges still exist: (i) effectively stacking MXene nanosheets into defect-free membranes and (ii) the high fouling tendency of MXene-based membranes. To address these issues, we employed sulfonated polydopamine (SPD), which simultaneously serves as a binding agent to promote the compact assembling of Ti3C2Tx MXenes (MX) nanosheets and improves the antifouling properties of the resulting sulfonated polydopamine-functionalized MX (SPDMX) membranes. The SPDMX membrane was tested for challenging surfactant-stabilized oil-in-water separation with an impressive efficiency of 98%. Moreover, an ultrahigh permeability of 1620 LMH/bar was also achieved. The sulfonation of PD helps in improving the antifouling characteristics of SPDMX by developing a strong hydration layer and enhancing the oleophobicity of the membrane. The underwater SPDMX membrane appeared superoleophobic with an oil contact angle of 153°, whereas the ceramic membrane exhibited an oil contact angle of 137°. The SPDMX membranes showed an improved flux recovery (31%) compared to the nonsulfonated counterpart. This work highlights the appropriate functionalization of MXene as a promising approach to developing MXene membranes with high permeation flux and better antifouling characteristics for oily wastewater treatment.

A state-of-art-review on emerging contaminants: Environmental chemistry, health effect, and modern treatment methods.

Pollution problems are increasingly becoming e a priority issue from both scientific and technological points of view. The dispersion and frequency of pollutants in the environment are on the rise, leading to the emergence have been increasing, including of a new class of contaminants that not only impact the environment but also pose risks to people's health. Therefore, developing new methods for identifying and quantifying these pollutants classified as emerging contaminants is imperative. These methods enable regulatory actions that effectively minimize their adverse effects to take steps to regulate and reduce their impact. On the other hand, these new contaminants represent a challenge for current technologies to be adapted to control and remove emerging contaminants and involve innovative, eco-friendly, and sustainable remediation technologies. There is a vast amount of information collected in this review on emerging pollutants, comparing the identification and quantification methods, the technologies applied for their control and remediation, and the policies and regulations necessary for their operation and application. In addition, This review will deal with different aspects of emerging contaminants, their origin, nature, detection, and treatment concerning water and wastewater.

3D porous polymers for selective removal of CO2 and H2 storage: experimental and computational studies.

In this article, newly designed 3D porous polymers with tuned porosity were synthesized by the polycondensation of tetrakis (4-aminophenyl) methane with pyrrole to form M1 polymer and with phenazine to form M2 polymer. The polymerization reaction used p-formaldehyde as a linker and nitric acid as a catalyst. The newly designed 3D porous polymers showed permanent porosity with a BET surface area of 575 m2/g for M1 and 389 m2/g for M2. The structure and thermal stability were investigated by solid 13C-NMR spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, and thermogravimetric analysis (TGA). The performance of the synthesized polymers toward CO2 and H2 was evaluated, demonstrating adsorption capacities of 1.85 mmol/g and 2.10 mmol/g for CO2 by M1 and M2, respectively. The importance of the synthesized polymers lies in their selectivity for CO2 capture, with CO2/N2 selectivity of 43 and 51 for M1 and M2, respectively. M1 and M2 polymers showed their capability for hydrogen storage with a capacity of 66 cm3/g (0.6 wt%) and 87 cm3/g (0.8 wt%), respectively, at 1 bar and 77 K. Molecular dynamics (MD) simulations using the grand canonical Monte Carlo (GCMC) method revealed the presence of considerable microporosity on M2, making it highly selective to CO2. The exceptional removal capabilities, combined with the high thermal stability and microporosity, enable M2 to be a potential material for flue gas purification and hydrogen storage.

Fabrication of polyamide thin film composite membranes using aliphatic tetra-amines and terephthaloyl chloride crosslinker for organic solvent nanofiltration.

Given the huge significance of organic solvents in several industrial processes, the use of membranes for recovering the solvents has evolved into an industrially viable process. The current work has been focused on studying the effect of minor changes in the chemistry of the reacting monomers on the organic solvent nanofiltration/solvent resistance nanofiltration (OSN/SRNF) performance of the membranes. The two aliphatic amines with varying aliphatic chain lengths between primary and secondary amines were selected for this purpose. Based on the structure of the resultant active layer, the Janus nanofiltration performance of the membrane was evaluated. The two membranes, 4A-TPC@crosslinked PAN and 4A-3P@crosslinked PAN were fabricated by using two different tetra-amines, 4A (N,N'-bis(3-aminopropyl)ethylenediamine) and 4A-3P (N,N'-Bis(2-aminoethyl)-1,3-propanediamine) crosslinked with terephthaloyl chloride (TPC) on a crosslinked polyacryonitrile (PAN) support through interfacial polymerization (IP). The presence of multiple hydrophobic -CH2- groups in the structures of the aliphatic amines 4A and 4A-3P develops hydrophobic sites in the hydrophilic polyamide active layers of the membranes. In addition, 4A has two secondary amino groups separated by ethylene (-CH2-CH2-) groups, whereas in 4A-3P, the two secondary amino groups are separated by propylene (-CH2-CH2-CH2-) leading to variation in the structural features and performance of the two membranes. Both membranes were fully characterized by several membrane characterization techniques and applied for OSN/SRNF using both polar (methanol, ethanol, and isopropanol) and non-polar (n-hexane and toluene) solvents. Different dyes (Congo red, Eriochrome black T, and Methylene blue) were used as model solutes during the filtration experiment. The 4A-3P-TPC@crosslinked PAN showed n-hexane and toluene flux of 109.9 LMH and 95.5 LMH, respectively. The Congo red (CR) showed the highest rejection, reaching 99.1% for the 4A-TPC@Crosslinked PAN membrane and 98.8% for the 4A-3P-TPC@Crosslinked PAN membrane.

Phase inverted hydrophobic polyethersulfone/iron oxide-oleylamine ultrafiltration membranes for efficient water-in-oil emulsion separation.

Exploration and transportation of oil offshore can result in oil spills that cause a wide range of adverse environmental consequences and destroy aquatic life. Membrane technology outperformed the conventional procedures for oil emulsion separation due to its improved performance, reduced cost, removal capacity, and greater eco-friendly. In this study, a hydrophobic iron oxide-oleylamine (Fe-Ol) nanohybrid was synthesized and incorporated into polyethersulfone (PES) to prepare novel PES/Fe-Ol hydrophobic ultrafiltration (UF) mixed matrix membranes (MMMs). Several characterization techniques were performed to characterize the synthesized nanohybrid and fabricated membranes, including scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), Fourier transform-infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thermal gravimetric analysis (TGA), contact angle, and zeta potential. The membranes' performance was assessed using a surfactant-stabilized (SS) water-in-hexane emulsion as a feed and a dead-end vacuum filtration setup. The incorporation of the nanohybrid enhanced the hydrophobicity, porosity, and thermal stability of the composite membranes. At 1.5 wt% Fe-Ol nanohybrid, the modified PES/Fe-Ol MMM membranes reported high water rejection efficiency of 97.4% and 1020.4 LMH filtrate flux. The re-usability and antifouling properties of the membrane were examined over five filtration cycles, demonstrating its great potential for use in water-in-oil separation.

Recovery of Dissolved Hydrogen Sulfide from Various Wastewater Streams Using Membranes and Other Relevant Techniques: A Review.

Given the significance of dissolved H2S, various techniques have been explored in the literature. The current review describes in detail the various membrane-based techniques, such as membrane contactors, for removing dissolved H2S from various wastewater streams. Various types of hydrophobic membranes have been used, with more emphasis placed on PVDF hollow fiber membranes. The hydrophobic membranes do not allow water to pass through, whereas H2S is readily allowed to pass through the membrane at ambient conditions. In addition, the use of monoethanol amine triazine (MEA-Triazine)- based H2S scavengers has also been described in detail, including the possible scavenging mechanism. The possibility of different types of byproducts has also been explained along with the possible routes to get rid of scavenger byproducts, such as apDTZ. The use of peroxy acetic acid has also been explained to oxidize and solubilize apDTZ. Furthermore, the use of vacuum-based dissolved H2S gas has also been described in detail. The application of the Knudsen and bulk diffusion models to the separation of dissolved H2S through the pores of the hollow fibers has also been explained. Finally, the future challenges and possible solutions along with concluding remarks have also been mentioned in the current review.

Fluoride and nitrate enrichment in coastal aquifers of the Eastern Province, Saudi Arabia: The influencing factors, toxicity, and human health risks.

Fluoride and nitrate contamination of groundwater is a major environmental issue in the world's arid and semiarid regions. This issue is severe in both developed and developing countries. This study aimed at assessing the concentration levels, contamination mechanisms, toxicity, and human health risks of NO3- and F- in the groundwater within the coastal aquifers of the eastern part of Saudi Arabia using a standard integrated approach. Most of the tested physicochemical properties of the groundwater exceeded their standard limits. The water quality index and synthetic pollution index evaluated the suitability of the groundwater and showed that all the samples have poor and unsuitable quality for drinking. The toxicity of F- was estimated to be higher than NO3-. Also, the health risk assessment revealed higher risks due to F- than NO3-. Younger populations had higher risks than elderly populations. For both F- and NO3-, the order of health risk was Infants > Children > Adults. Most of the samples posed medium to high chronic risks due to F- and NO3- ingestion. However, negligible health risks were obtained for potential dermal absorption of NO3-. Na-Cl and Ca-Mg-Cl water types predominate in the area. Pearson's correlation analysis, principal component analysis, regression models, and graphical plots were used to determine the possible sources of the water contaminants and their enrichment mechanisms. Geogenic and geochemical processes had greater impact he groundwater chemistry than anthropogenic activities. For the first time, these findings provide public knowledge on the overall water quality of the coastal aquifers and could help the inhabitants, water management authorities, and researchers to identify the groundwater sources that are most desirable for consumption and the human populations that are vulnerable to non-carcinogenic health risks.

Fabrication of a New Rationally Designed Mussel-Inspired Cationic Amphiphilic Terpolymer to Enhance the Separation and Anti-Fouling Performance of Membranes.

Polyvinylidene fluoride (PVDF) membrane-based systems for treating oily wastewater are prone to fouling. Herein, we introduced a novel mussel-inspired cationic amphiphilic terpolymer consisting of monomers N,N-diallyldimethylammonium chloride (DADMAC), N,N-diallyltetradecan-1-ammonium chloride (DTDAC), and mussel-inspired N,N-diallyldopamine hydrochloride (DADAHC) to improve the performance and characteristics of the PVDF membranes for oil-in-water emulsion separations. The cationic terpolymer, poly(DADMAC-co-DTDAC-co-DADAHC), shortened as PDDD, was synthesized in excellent yields via free radical polymerization and has good compatibility with the PVDF owing to the presence of hydrophobic long alkyl chains in DTDAC. The presence of dopamine motifs helps stabilize the PDDD-PVDF membrane by chelating with Fe3+ ions. The water contact angle on the PDDD-incorporated PVDF membranes was reduced from 87.6 to 54.6°, demonstrating improved hydrophilicity than pristine PVDF (M-0). The incorporation of PDDD into the PVDF improved the separation efficiencies of the membrane, which reached up to 99% while treating the oil-in-water emulsions. Incorporating PDDD into PVDF has significantly enhanced the anti-fouling characteristics of the membranes, which are indicated by their remarkable flux recovery ratio (FRR) (up to 92%). The hydrophobic and hydrophilic groups worked synergetically to enhance the performance of the fabricated membrane.

Intelligent optimization for modelling superhydrophobic ceramic membrane oil flux and oil-water separation efficiency: Evidence from wastewater treatment and experimental laboratory.

Due to the significant energy and economic losses brought on by the global oil spill, there has been an increased interest in oil-water separation. This study presents strong non-linear machine learning models (support vector regression (SVR) and Gaussian process regression (GPR)) with the Response surface method (RSM) to predict the oil flux and oil-water separation efficiency of wastewater using ceramic membrane technology. For the model development and prediction of oil flux (OF) and oil-water separation efficiency (OSE), oil concentration (mg/L), feed flow rate (mL/min), and pH were considered as input variables. The input variables are combined in three combinations to study the most contributing input features to the models' performance. Mean square error (MSE) and Nash-Sutcliffe coefficient efficiency (NSE) were used to assess the prediction performances of the developed models with the different number of input combinations considered in the study. For the two target variables (OF and OSE), GPR and SVR models were used to separately predict them. For OF, the SVR-2 [Combo-2] model (MSE = 0.9255 and NSE = 2.7976) performed better with higher prediction accuracy compared to GPR-2 [Combo-2] model (MSE = 0.763 and NSE = 6.437). In addition, for OSE, the GPR-3 [Combo-3] model (MSE = 0.995 and NSE = 0.5544) performed slightly better than SVR-3 [Combo-3] model (MSE = 0.992 and NSE = 0.8066). The results showed that the SVR model with the combo-2 and GPR-3 models for OF and OSE variables are the proposed models with the best performance and accuracy. This machine learning study will aid in better evaluating the function of materials such as ceramic in membrane performance features such as oil flux and rejection prediction, separation efficiency, water recovery, membrane fouling, and so on. As for academics and manufacturers, this machine learning (ML) strategy will boost performance and allow a better understanding of system governance.