Investigation of experimental results and D-optimal design of hafnium ion extraction from aqueous system using emulsion liquid membrane technique

Impact of SiO2 doping on the structure and oil-water separation properties of a PVDF membrane: insights from molecular dynamics simulation

Hybrid inorganic particles combined with polymers are widely used to modify the properties of polymer membranes. However, the mechanism by which particles affect membranes remains unclear. This study investigates SiO2-hybridized PVDF membranes through molecular dynamic simulation, focusing on the interaction between SiO2 clusters and PVDF chains. It examines the impact of varying SiO2 concentrations (3.5 wt%, 6.8 wt%, 9.9 wt%, 12.8 wt%, and 15.5 wt%) on membrane stability and structure. The results indicate that adding SiO2 can inhibit PVDF chain mobility in the membrane with minimal effect on fractional free volume (FFV), except for altering interactions between PVDF-PVDF, PVDF-SiO2, and SiO2-SiO2, thereby affecting the structure of hybrid membranes. The adsorption and diffusion behavior of water and oil molecules on these membranes were also studied. It was observed that the adsorption energy and diffusion coefficient initially increase and then decrease with increasing SiO2 concentration, reaching an optimum between 6.8 wt% and 12.8 wt%. This phenomenon is attributed to the ability of optimal SiO2 concentrations to create hydrophilic channels in PVDF membranes, enhancing water affinity and reducing oil affinity. Consequently, water permeation through the hybrid membrane is promoted, improving the efficiency of oil/water separation compared to pure PVDF membranes. This research contributes to understanding the function of adding inorganic particles to polymer membranes and provides insights for designing advanced functional hybrid membranes.

Machine learning-powered estimation of malachite green photocatalytic degradation with NML-BiFeO3 composites

This study explores the potential of photocatalytic degradation using novel NML-BiFeO3 (noble metal-incorporated bismuth ferrite) compounds for eliminating malachite green (MG) dye from wastewater. The effectiveness of various Gaussian process regression (GPR) models in predicting MG degradation is investigated. Four GPR models (Matern, Exponential, Squared Exponential, and Rational Quadratic) were employed to analyze a dataset of 1200 observations encompassing various experimental conditions. The models have considered ten input variables, including catalyst properties, solution characteristics, and operational parameters. The Exponential kernel-based GPR model achieved the best performance, with a near-perfect R2 value of 1.0, indicating exceptional accuracy in predicting MG degradation. Sensitivity analysis revealed process time as the most critical factor influencing MG degradation, followed by pore volume, catalyst loading, light intensity, catalyst type, pH, anion type, surface area, and humic acid concentration. This highlights the complex interplay between these factors in the degradation process. The reliability of the models was confirmed by outlier detection using William's plot, demonstrating a minimal number of outliers (66-71 data points depending on the model). This indicates the robustness of the data utilized for model development. This study suggests that NML-BiFeO3 composites hold promise for wastewater treatment and that GPR models, particularly Matern-GPR, offer a powerful tool for predicting MG degradation. Identifying fundamental catalyst properties can expedite the application of NML-BiFeO3, leading to optimized wastewater treatment processes. Overall, this study provides valuable insights into using NML-BiFeO3 compounds and machine learning for efficient MG removal from wastewater.

Integrative analysis of multi machine learning models for tetracycline photocatalytic degradation with MOFs in wastewater treatment

This study focuses on the utilization of connectionist models, specifically Independent Component Analysis (ICA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Genetic Algorithm-Particle Swarm Optimization (GAPSO) integrated with a least-squares support vector machine (LSSVM) to forecast the degradation of tetracycline (TC) through photocatalysis using Metal-Organic Frameworks (MOFs). The primary objective of this study was to evaluate the viability and precision of these connectionist models in estimating the efficiency of TC degradation, particularly within the context of wastewater treatment. The input parameters for these models cover essential MOF characteristics, such as pore size and surface area, along with critical operational factors, such as pH, TC concentration, catalyst dosage, and illumination duration, all of which are linked to the photocatalytic performance of MOFs. Sensitivity analysis revealed that the illumination duration is the primary influencer of TC photodegradation with MOF photocatalysts, while the MOFs' surface area is the second crucial parameter shaping the efficiency and dynamics of the TC-MOF photocatalytic system. The developed LSSVM models display impressive predictive capabilities, effectively forecasting the experimental degradation of TC with high accuracy. Among these models, the GAPSO-LSSVM model excels as the top performer, achieving notable evaluation metrics, including STD, RMSE, MSE, MRE, and R2 at values of 3.09, 3.42, 11.71, 5.95, and 0.986, respectively. In comparison, the PSO-LSSVM, ICA-LSSVM, and GA-LSSVM models yield mean relative errors of 6.18%, 7.57%, and 11.37%, respectively. These outcomes highlight the exceptional predictive capabilities of the GAPSO-LSSVM model, solidifying its position as the most accurate and dependable model for predicting TC photodegradation in this study. This study contributes to advancing photocatalytic research and effectively reinforces the importance of leveraging machine learning methodologies for tackling environmental challenges.