A review on the application of biochar as an innovative and sustainable biocarrier material in moving bed biofilm reactors for dye removal from environmental matrices.

Dye decolorization through biological treatment techniques has been gaining momentum as it is based on suspended and attached growth biomass in both batch and continuous modes. Hence, this review focused on the contribution of moving bed biofilm reactors (MBBR) in dye removal. MBBR have been demonstrated to be an excellent technology for pollution extraction, load shock resistance, and equipment size and energy consumption reduction. The review went further to highlight different biocarrier materials for biofilm development this review identified biochar as an innovative and environmentally friendly material produced through the application of different kinds of reusable or recyclable wastes and biowastes. Biochar as a carbonized waste biomass could be a better competitor and environmentally friendly substitute to activated carbon given its lower mass costs. Biochar can be easily produced particularly in rural locations where there is an abundance of biomass-based trash. Given that circular bioeconomy lowers dependency on natural resources by turning organic wastes into an array of useful products, biochar empowers the creation of competitive goods. Thus, biochar was identified as a novel, cost-effective, and long-term management strategy since it brings about several essential benefits, including food security, climate change mitigation, biodiversity preservation, and sustainability improvement. This review concludes that integrating two treatment methods could greatly lead to better color, organic matter, and nutrients removal than a single biological MBBR treatment process.

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.

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.

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.

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.