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.

Nanocrystalline cellulose incorporated biopolymer tailored polyethersulfone mixed matrix membranes for efficient treatment of produced water.

Membrane technology is a sustainable method to remove pollutants from petroleum wastewater. However, the presence of hydrophobic oil molecules and inorganic constituents can cause membrane fouling. Biomass derived biopolymers are promising renewable materials for membrane modification. In this study, fouling resistant biopolymer N-phthaloylchitosan (CS)- based polythersulfone (PES) mixed matrix membranes (MMMs) incorporated with nanocrystalline cellulose (NCC) was fabricated via phase inversion method and applied for produced water (PW) treatment. The morphological and Fourier-transform infrared spectroscopy (FTIR) analyses of the as-prepared NCC evidenced the formation of fibrous sheet-like structure and the presence of hydrophilic group. The membrane morphology and AFM analysis showed that the NCC altered the surface and cross-sectional morphology of the CS-PES MMMs. The tensile strength of NCC-CS-PES MMMs was also enhanced. 0.5 wt% NCC-CS-PES MMMs displayed a water permeability of 1.11 × 10-7 m/s.kPa with the lowest contact angle value of 61°. It affirmed that its hydrophilicity increased through the synergetic interaction between CS biopolymer and NCC. The effect of process variables such as transmembrane pressure (TMP) and synthetic produced water (PW) concentration were evaluated for both neat PES and NCC-CS-PES MMMs membranes. 0.5 wt% NCC-CS-PES MMMs exhibited the highest PW rejection of 98% when treating 50 mgL-1 of synthetic PW at a transmembrane pressure (TMP) of 200 kPa. The effect of nano silica and sodium chloride on the long-term PW filtration of NCC-CS-PES MMMs was also investigated.

Low-cost silica based ceramic supported thin film composite hollow fiber membrane from guinea corn husk ash for efficient removal of microplastic from aqueous solution.

In this study, an economic silica based ceramic hollow fiber (HF) microporous membrane was fabricated from guinea cornhusk ash (GCHA). A silica interlayer was coated to form a defect free silica membrane which serves as a support for the formation of thin film composite (TFC) ceramic hollow fiber (HF) membrane for the removal of microplastics (MPs) from aqueous solutions. Polyacrylonitrile (PAN), polyvinyl-chloride (PVC), polyvinylpyrrolidone (PVP) and polymethyl methacrylate (PMMA) are the selected MPs The effects of amine monomer concentration (0.5 wt% and 1 wt%) on the formation of poly (piperazine-amide) layer via interfacial polymerization over the GCHA ceramic support were also investigated. The morphology analysis of TFC GCHA HF membranes revealed the formation of a poly (piperazine-amide) layer with narrow pore arrangement. The pore size of TFC GCHA membrane declined with the formation of poly (piperazine-amide) layer, as evidenced from porosimetry analysis. The increase of amine concentration reduced the porosity and water flux of TFC GCHA HF membranes. During MPs filtration, 1 wt% (piperazine) based TFC GCHA membrane showed a lower transmission percentage of PVP (2.7%) and other suspended MPs also displayed lower transmission. The impact of humic acid and sodium alginate on MPs filtration and seawater pretreatment were also analyzed.

Parametric analysis of lignocellulosic ultrafiltration in lab scale cross flow module using pore blocking and artificial neural network model.

In this study, fouling mechanism and modelling analysis of synthetic lignocellulose biomass and agricultural palm oil effluent was studied using polyethersulfone (PES) ultrafiltration (UF) 10 kDa membrane. The impact of process variables (transmembrane pressure (TMP), pH and concentration of feed solution) on lignocellulosic flux was analysed using pore blocking model. The feasible approaches on utilising deep learning artificial neural network (ANN) to predict smaller flux datasets are studied. Among the input variables, pH of lignin feed solution has significant control towards flux and lignin rejection coefficient for both lignin and lignocellulosic solution. Alteration in the structure of lignin at different pH conditions contributed in the improvement of lignin rejection coefficient to 0.98 at the feed pH of 9. A maximum steady state flux of 52.03 L/m2h was observed at the lower lignin concentration (0.25 g/L), TMP of 200 kPa and feed pH of 3. At high TMP and concentration, lignin rejection decreased due to enhancement of feed concentration on membrane surface. The mechanistic model exhibited that cake layer phenomena was dominant in both lignin and lignocellulosic solution. The proposed ANN model showed good correlation (R2-1.00) with experimental non-linear flux dynamic data of both lignin and synthetic lignocellulosic solution. In ANN analysis, activation function, algorithm and neuron effect have significant effect in design of accurate model for prediction of small flux datasets. Aerobically-treated palm oil mill filtration analysis also showed that cake layer phenomenon was dominant. A water recovery of 82 % was achieved even at low TMP under short durations.

Performance evaluation of whey flux in dead-end and cross-flow modes via convolutional neural networks.

Effluent originating from cheese production puts pressure onto environment due to its high organic load. Therefore, the main objective of this work was to compare the influence of different process variables (transmembrane pressure (TMP), Reynolds number and feed pH) on whey protein recovery from synthetic and industrial cheese whey using polyethersulfone (PES 30 kDa) membrane in dead-end and cross-flow modes. Analysis on the fouling mechanistic model indicates that cake layer formation is dominant as compared to other pore blocking phenomena evaluated. Among the input variables, pH of whey protein solution has the biggest influence towards membrane flux and protein rejection performances. At pH 4, electrostatic attraction experienced by whey protein molecules prompted a decline in flux. Cross-flow filtration system exhibited a whey rejection value of 0.97 with an average flux of 69.40 L/m2h and at an experimental condition of 250 kPa and 8 for TMP and pH, respectively. The dynamic behavior of whey effluent flux was modeled using machine learning (ML) tool convolutional neural networks (CNN) and recursive one-step prediction scheme was utilized. Linear and non-linear correlation indicated that CNN model (R2 - 0.99) correlated well with the dynamic flux experimental data. PES 30 kDa membrane displayed a total protein rejection coefficient of 0.96 with 55% of water recovery for the industrial cheese whey effluent. Overall, these filtration studies revealed that this dynamic whey flux data studies using the CNN modeling also has a wider scope as it can be applied in sensor tuning to monitor flux online by means of enhancing whey recovery efficiency.

ZrO2-TiO2 Incorporated PVDF Dual-Layer Hollow Fiber Membrane for Oily Wastewater Treatment: Effect of Air Gap.

Dual-layer hollow fiber (DLHF) nanocomposite membrane prepared by co-extrusion technique allows a uniform distribution of nanoparticles within the membrane outer layer to enhance the membrane performance. The effects of spinning parameters especially the air gap on the physico-chemical properties of ZrO2-TiO2 nanoparticles incorporated PVDF DLHF membranes for oily wastewater treatment have been investigated in this study. The zeta potential of the nanoparticles was measured to be around -16.5 mV. FESEM-EDX verified the uniform distribution of Ti, Zr, and O elements throughout the nanoparticle sample and the TEM images showed an average nanoparticles grain size of ~12 nm. Meanwhile, the size distribution intensity was around 716 nm. A lower air gap was found to suppress the macrovoid growth which resulted in the formation of thin outer layer incorporated with nanoparticles. The improvement in the separation performance of PVDF DLHF membranes embedded with ZrO2-TiO2 nanoparticles by about 5.7% in comparison to the neat membrane disclosed that the incorporation of ZrO2-TiO2 nanoparticles make them potentially useful for oily wastewater treatment.