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.

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).

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.

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.

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.

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.

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.

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.

Recent Progress in Microfiltration/Ultrafiltration Membranes for Separation of Oil and Water Emulsions.

Oily wastewater has become one of the leading causes of environmental pollution. A massive quantity of oily wastewater is released from industries, oil spills, and routine activities, endangering the ecosystem's sustainability. Due to the enormous negative impact, researchers put strenuous efforts into developing a sustainable solution to treat oily wastewater. Microfiltration/ultrafiltration membranes are considered an efficient solution to treat oily wastewater due to their low cost, small footprint, facile operation, and high separation efficiencies. However, membranes severely fouled during the separation process due to oil's adsorption and cake layer formation, which shortens the membranes' life. This review has critically discussed the microfiltration/ultrafiltration membrane synthesizing methods and their emulsion's separation performance. In the end, key challenges and their possible solutions are highlighted to provide future direction to synthesize next-generation membranes.