Optimization of Ni(II) adsorption onto Cloisite Na+ clay using response surface methodology

Enhanced Ni(II) Removal from Wastewater Using Novel Molecular Sieve-Based Composites

This study focuses on the efficient removal of Ni(II) from spent lithium-ion batteries (LIBs) to support environmental conservation and sustainable resource management. A composite material, known as molecular sieve (MS)-based metal-organic framework (MOF) composites (MMCs), consisting of a synthesized MS matrix with integrated MOFs, was developed for the adsorption of Ni(II). The structural and performance characteristics of the MMCs were evaluated using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and N2 adsorption-desorption isotherms (BET). Batch adsorption experiments were conducted to assess the Ni(II) adsorption performance of the MMCs. The results revealed that, under conditions of pH 8 and a temperature of 298 K, the MMCs achieved near-equilibrium Ni(II) adsorption within 6 h, with a maximum theoretical adsorption capacity of 204.1 mg/g. Further analysis of the adsorption data confirmed that the adsorption process followed a pseudo-second-order kinetic model and Langmuir isotherm model, indicating a spontaneous, endothermic chemical adsorption mechanism. Importantly, the MMCs exhibited superior Ni(II) adsorption compared to the MS. This study provides valuable insights into the effective recovery and recycling of Ni(II) from spent LIBs, emphasizing its significance for environmental sustainability and resource circularity.

Optimization and prediction of lead removal from aqueous solution using FO-MD hybrid process: Statistical and artificial intelligence analysis

Heavy metals (HMs) has become one of the most serious pollutants that are harmful to the environment and ecology. This paper focused on the removal of lead contaminant from wastewater by forward osmosis-membrane distillation (FO-MD) hybrid process using seawater as draw solution. Modeling, optimization, and prediction of FO performance are developed using complementary approach based on response surface methodology (RSM) and an artificial neural network (ANN). FO process optimization using RSM revealed that under initial lead concentration of 60 mg/L, feed velocity of 11.57 cm/s and draw velocity of 7.66 cm/s, FO process achieved highest water flux of 6.75 LMH, lowest reverse salt flux of 2.78 gMH and highest lead removal efficiency of 87.07%. Fitness of all models was evaluated based on determination coefficient (R²) and mean square error (MSE). Results showed highest R² value up to 0.9906 and lowest RMSE value up to 0.0102. ANN modeling generates the highest prediction accuracy for water flux and reverse salt flux, while RSM produces the highest prediction accuracy for lead removal efficiency. Subsequently, FO optimal conditions are applied on FO-MD hybrid process using seawater as draw solution and evaluate their performance to simultaneously remove lead contaminant and desalination of seawater. Results displays that FO-MD process shows a highly efficient solution to produce fresh water with almost free heavy metals and very low conductivity.

Kinetic optimization of odor adsorption with acetate fiber cloth prepared from waste cigarette filter

Odor pollution with NH₃ as major contributor is a notorious issue that strongly influences our living environment. NH₃ removal with acetate fiber cloth (AFC) prepared from waste cigarette filter is an economic feasible approach for simultaneous solid wastes disposal. Herein, waste cigarette filter was used to prepare AFC through hot-pressing approach, which was convinced to have good adsorption efficiency on NH₃ due to large specific surface area. Effects of hot-pressing temperature, pressure and pressing time on AFC mechanical property and NH₃ adsorption efficiencies were optimized by response surface method. As results, hot-pressing treatment improved the specific surface area of AFC to 9.530 m²/g, and thus enhanced NH₃ adsorption efficiency to 68.73 % under hot-pressing temperature of 146 °C, pressure of 12.5 kPa and pressing time of 33 min. While the optimal tensile strength of AFC was obtained as 90.43 N under hot-pressing temperature of 140 °C, pressure of 15.0 kPa and pressing time of 30 min. The work provided an economic feasible approach for waste cigarette filter recycling and odor control.

Synthesis and optimization of spherical nZVI (20-60 nm) immobilized in bio-apatite-based material for efficient removal of phosphate: Box-Behnken design in a fixed-bed column

In the present study, bio-apatite/nZVI composite was synthesized through Fe(III) reduction with sodium borohydride and was fully characterized by FTIR, XRD, SEM-EDX, TEM, BET, BJH, and pH_PZC. Column experiments were carried out for the removal of phosphate as a function of four operational parameters including initial phosphate concentration (100-200 mg L^-1), initial solution pH (2-9), bed height (2-6 cm), and influent flow rate (2.5-7.5 mL min^-1) using a response surface methodology (RSM) coupled with Box-Behnken design (BBD). 2D contour and 3D surface plots were employed to analyze the interactive effects of the four operating parameters on the column performance (e.g., uptake capacity and saturation time). According to ANOVA analysis, the influent flow rate and bed height are the most important factor on phosphate uptake capacity and saturation time, respectively. A quadratic polynomial model was excellently fitted to experimental data with a high coefficient of determination (> 0.96). The RSM-BBD model predicted maximum phosphate adsorption capacity of 85.71 mg g^-1 with the desirability of 0.995 under the optimal conditions of 135.35 mg L^-1, 2, 2 cm, and 7.5 mL min^-1 for initial phosphate concentration, initial solution pH, bed height, and influent flow rate, respectively. The XRD analysis demonstrated that the reaction product between bio-apatite/nZVI composite and phosphate anions was Fe₃ (PO₄)₂. 8H₂O (vivianite). The suggested adsorbent can be effectively employed up to five fixed-bed adsorption-desorption cycles and was also implemented to adsorb phosphate from real samples.

Non-thermal plasma irradiated polyaluminum chloride for the heterogeneous adsorption enhancement of Cs+ and Sr2+ in a binary system

The natural ecosystem will continually deteriorate for decades by the leakage of Cs and Sr isotopes. The exploration of the new materials or techniques for the efficient treatment of radioactive wastewater is critically important. In this study, a dielectric barrier discharge (DBD) configuration was constructed to operate the non-thermal plasma (NTP). The NTP was incorporated into the synthesis of polyaluminum chloride (PAC) in two different procedures to intensify the synthesis of PAC (NTP-PAC) and enhance the further removal of Cs and Sr from wastewater. The employment of NTP in two procedures both had significantly changed the physicochemical characteristics of PAC materials, which facilitated the further adsorption application of NTP-PAC on the treatment of Cs⁺ and Sr²⁺. Different molecular, morphological, and adsorption characteristics were confirmed to the NTP-PAC materials. The heterogeneous adsorption of the NTP-PAC can be appropriately fitted by both the pseudo-first-order kinetic model and the Elovich model. Both physisorption and chemisorption reaction mechanisms were ensured for the heterogeneous adsorption of the NTP-PAC material towards Cs⁺ and Sr²⁺, which guaranteed the excellent adsorption performance of NTP-PAC materials compared to PAC. The electron collisions caused by NTP with alum pulp created highly reactive growth precursors and intensified the nucleation and hydrolysis polymerization of PAC. The employment of NTP explicitly broadens the reaction pathways between PAC and cationic contaminants in the aqueous environment, which expands the application area of PAC materials in environmental sustainability.

Hemp fibers modified with graphite oxide as green and efficient solution for water remediation: Application to methylene blue

In this paper, the use of hemp fibers modified with graphite oxide for the removal of methylene blue (MB) from aqueous solutions was investigated. Parameters such as contact time, pH, temperature, initial concentration of dye and ionic strength were varied and their effects on the adsorption recovery were evaluated. The adsorption process attained the equilibrium within 30 min while the adsorption capacity was found to increase with increasing contact time. The experimental data were fitted through a pseudo-second order model. Maximum adsorption capacity slightly increases with temperature changing from 54 mg/g to 58 mg/g at pH = 7.5, from 37 mg/g to 45 mg/g at pH = 3 and from 44 mg/g to 49 mg/g at pH = 12, by increasing the temperature from 20 °C to 80 °C indicating that the process is slightly endothermic (ΔH = 3.43 kJ/mol). The thermodynamic parameters were even calculated demonstrating that the process is spontaneous (ΔG ≈ -4.4 J/mol K and ΔS = 3.16 J/mol K)). Finally, a mathematical algorithm was applied to forecast the response surface model. A second order model was chosen to fit the experimental data and the statistical effect of the process parameters were estimated. A numerical optimization was even performed to individuate the optimal set of process parameters (pH = 9.25, T = 53.8 °C and C₀ = 13.2 mg/L) which maximizes the removal capacity. A possible adsorption mechanism was even presented. So, it was proved the efficiency of the adsorption of a novel, inexpensive and sustainable composite material obtained from agro-waste resources by performing reusability cycles.

Simultaneous removal of Zn2+ and p-nitrophenol from wastewater using nanocomposites of montmorillonite with alkyl-ammonium and complexant

Three types of alkyl-ammonium with different branching chains and three complexants with different functional groups were used to prepare alkyl-ammonium or complexant intercalated montmorillonite nanocomposite (A-Mt or C-Mt). In addition, synergistic intercalated montmorillonite nanocomposites (A/C-Mt) with alkyl-ammonium along with complexant were also prepared. The adsorption performance of the various nanocomposites toward Zn²⁺ and p-nitrophenol (PNP) from simulated binary wastewater containing both Zn²⁺ and PNP were systematically investigated. Characterization of Mt nanocomposites showed that both alkyl-ammoniums and complexants were successfully intercalated into the interlayers of Mt. The surfactant loading amounts of the various nanocomposites were also determined and correlated with the resulting expansion of the interlayer spacing. It was found that intercalation of alkane (OTAC) and -SH (CSH) were conducive to the adsorption of Zn²⁺ while -C₂H₄NH (TETA) and all alkyl-ammoniums were beneficial for PNP adsorption. The extent of adsorption was found to be controlled primarily by pH, i.e., the higher pH had a good effect on the adsorption of both Zn²⁺ and PNP. The adsorption process of Zn²⁺ onto Mt nanocomposites was more in line with the Freundlich model (R² = 0.99), while the Langmuir model described the adsorption of PNP well (R² = 0.99). The adsorption kinetics could be well described by the Elovich equation (R² = 0.98) and the double-constant model (R² = 0.89). Chemical adsorption was determined to be the dominant process between the contaminant and Mt nanocomposite surfaces.

Adsorption of dicamba and MCPA onto MIL-53(Al) metal-organic framework: response surface methodology and artificial neural network model studies

An aluminium-based metal–organic framework ((MOF), MIL-53(Al)), was hydrothermally synthesized, characterized and applied for the remediation of the herbicides dicamba (3,6-dichloro-2-methoxy benzoic acid) and 4-chloro-2-methylphenoxyacetic acid (MCPA) in aqueous medium. Response surface methodology (RSM) and artificial neural network (ANN) were used to design, optimize and predict the non-linear relationships between the independent and dependent variables. The shared interaction of the effects of key response parameters on the adsorption capacity were assessed using the central composite design-RSM and ANN optimization models. The optimum adsorption capacities for dicamba and MCPA are 228.5 and 231.9 mg g⁻¹, respectively. The RSM ANOVA results showed significant p-values, with coefficients of determination (R²) = 0.988 and 0.987 and R² adjusted = 0.974 and 0.976 for dicamba and MCPA, respectively. The ANN prediction model gave R² = 0.999 and 0.999, R² adjusted = 0.997 and 0.995 and root mean square errors (RMSEs) of 0.001 and 0.004 for dicamba and MCPA, respectively. In each set of experimental conditions used for the study, the ANN gave better prediction than the RSM, with high accuracy and minimal error. The rapid removal (∼25 min), reusability (5 times) and good agreement between the experimental findings and simulation results suggest the great potential of MIL-53(Al) for the remediation of dicamba and MCPA from water matrices.

Rapid equilibration within a short time, high adsorption capacity, optimization, multivariate interaction of adsorption parameters and artificial neural network prediction model.