Modeling defluoridation of real-life groundwater by a green adsorbent aluminum/olivine composite: Isotherm, kinetics, thermodynamics and novel framework based on artificial neural network and support vector machine

Adsorptive removal of perfluorooctanoic acid from aqueous matrices using peanut husk-derived magnetic biochar: Statistical and artificial intelligence approaches, kinetics, isotherm, and thermodynamics

Removal of perfluorooctanoic acid (PFOA) from water matrices is crucial owing to its pervasiveness and adverse ecological and human health effects. This study investigates the adsorptive removal of PFOA using magnetic biochar (MBC) derived from FeCl3-treated peanut husk at different temperatures (300, 600, and 900 °C). Preliminary experiments demonstrated that MBC600 exhibited superior performance, with its characterization confirming the presence of γ-Fe2O3. However, efficient PFOA removal from water matrices depends on determining the optimum combination of inputs in the treatment approaches. Therefore, optimization and predictive modeling of the PFOA adsorption were investigated using the response surface methodology (RSM) and the artificial intelligence (AI) models, respectively. The central composite design (CCD) of RSM was employed as the design matrix. Further, three AI models, viz. artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS) were selected to predict PFOA adsorption. The RSM-CCD model applied to optimize three input process parameters, namely, adsorbent dose (100-400 mg/L), pH (3-10), and contact time (20-60 min), showed a statistically significant (p < 0.05) effect on PFOA removal. Maximum PFOA removal of about 98.3% was attained at the optimized conditions: adsorbent dose: 400 mg/L, pH: 3.4, and contact time: 60 min. Non-linear analysis showed PFOA adsorption was best fitted by pseudo-second-order kinetics (R2 = 0.9997). PFOA adsorption followed Freundlich isotherm (R2 = 0.9951) with a maximum adsorption capacity of ∼307 mg/g. Thermodynamics and spectroscopic analyses revealed that PFOA adsorption is a spontaneous, exothermic, and physical phenomenon, with electrostatic interaction, hydrophobic interaction, and hydrogen bonding governing the process. A comparative analysis of the statistical and AI models for PFOA adsorption demonstrated high R2 (>0.99) for RSM-CCD, ANN, and ANFIS. This research demonstrates the applicability of the statistical and AI models for efficient prediction of PFOA adsorption from water matrices using MBC (MBC600).

Coagulative removal of polystyrene microplastics from aqueous matrices using FeCl3-chitosan system: Experimental and artificial neural network modeling

Effluent from sewage treatment plants (STPs) is a significant source of microplastics (MPs) re-entry into the environment. Coagulation-flocculation-sedimentation (CFS) process as an initial tertiary treatment step requires investigation for coagulative MPs removal from secondary-treated sewage effluents. In this study, experiments were conducted on synthetic water containing 25 mg/L polystyrene (PS) MPs using varying dosages of FeCl3 (1-10 mg/L) and chitosan (0.25-9 mg/L) to assess the effect of process parameters, such as pH (4-8), stirring speed (0-200 rpm), and settling time (10-40 min). Results revealed that ∼89.3% and 21.4% of PS removal were achieved by FeCl3 and chitosan, respectively. Further, their combination resulted in a maximum of 99.8% removal at favorable conditions: FeCl3: 2 mg/L, chitosan: 7 mg/L, pH: 6.3, stirring speed: 100 rpm, and settling time: 30 min, with a statistically significant (p < 0.05) effect. Artificial neural network (ANN) validated the experimental results with RMSE = 1.0643 and R2 = 0.9997. Charge neutralization, confirmed by zeta potential, and adsorption, ascertained by field-emission scanning electron microscope (FESEM) and Fourier-transform infrared spectroscopy (FTIR), were primary mechanisms for efficient PS removal. For practical considerations, the application of the FeCl3-chitosan system on the effluents from moving bed biofilm reactor (MBBR) and sequencing batch reactor (SBR)-based STPs, spiked with PS microbeads, showed > 98% removal at favorable conditions.

Phosphogypsum-Modified Vinasse Shell Biochar as a Novel Low-Cost Material for High-Efficiency Fluoride Removal

In this study, vinasse shell biochar (VS) was easily modified with phosphogypsum to produce a low-cost and novel adsorbent (MVS) with excellent fluoride adsorption performance. The physicochemical features of the fabricated materials were studied in detail using SEM, EDS, BET, XRD, FTIR, and XPS techniques. The adsorption experiments demonstrated that the adsorption capacity of fluoride by MVS was greatly enhanced compared with VS, and the adsorption capacity increased with the pyrolysis temperature, dosage, and contact time. In comparison to chloride and nitrate ions, sulfate ions significantly affected adsorption capacity. The fluoride adsorption capacity increased first and then decreased with increasing pH in the range of 3-12. The fluoride adsorption could be perfectly fitted to the pseudo-second-order model. Adsorption isotherms matched Freundlich and Sips isotherm models well, giving 290.9 mg/g as the maximum adsorption capacity. Additionally, a thermodynamic analysis was indicative of spontaneous and endothermic processes. Based on characterization and experiment results, the plausible mechanism of fluoride adsorption onto MVS was proposed, mainly including electrostatic interactions, ion exchange, precipitation, and hydrogen bonds. This study showed that MVS could be used for the highly efficient removal of fluoride and was compatible with practical applications.

Predicting the aggregation number of cationic surfactants based on ANN-QSAR modeling approaches: understanding the impact of molecular descriptors on aggregation numbers

In this work, a quantitative structure-activity relationship (QSAR) study is performed on some cationic surfactants to evaluate the relationship between the molecular structures of the compounds with their aggregation numbers (AGGNs) in aqueous solution at 25 °C. An artificial neural network (ANN) model is combined with the QSAR study to predict the aggregation number of the surfactants. In the ANN analysis, four out of more than 3000 molecular descriptors were used as input variables, and the complete set of 41 cationic surfactants was randomly divided into a training set of 29, a test set of 6, and a validation set of 6 molecules. After that, a multiple linear regression (MLR) analysis was utilized to build a linear model using the same descriptors and the results were compared statistically with those of the ANN analysis. The square of the correlation coefficient (R ²) and root mean square error (RMSE) of the ANN and MLR models (for the whole data set) were 0.9392, 7.84, and 0.5010, 22.52, respectively. The results of the comparison revealed the efficiency of ANN in detecting a correlation between the molecular structure of surfactants and their AGGN values with a high predictive power due to the non-linearity in the studied data. Based on the ANN algorithm, the relative importance of the selected descriptors was computed and arranged in the following descending order: H-047 > ESpm12x > JGI6> Mor20p. Then, the QSAR data was interpreted and the impact of each descriptor on the AGGNs of the molecules were thoroughly discussed. The results showed there is a correlation between each selected descriptor and the AGGN values of the surfactants.