Enhanced efficiency of ANN using non-linear regression for modeling adsorptive removal of fluoride by calcined Ca-Al-(NO3)-LDH

C/N ratio effect on oily wastewater treatment using column type SBR: machine learning prediction and metagenomics study

The sequencing batch reactor has emerged as a promising technology in treating wastewater; however, its application in the treatment of generated water still needs to be explored. This research gap led to the investigation of various carbon-to-nitrogen (C/N) ratios in a column-type sequencing batch reactor (cSBR). The resulting data and model demonstrated that augmenting the SND process with an external carbon source is effective until the C/N ratio reaches 15, ultimately eliminating nitrogen in the produced water. Conversely, a reduced C/N ratio can limit the ability of polyphosphate-accumulating organisms to incorporate carbon into polyphosphate synthesis, thereby decreasing phosphorus removal efficiency within the cSBR. When the C/N ratio ranged from 6 to 8, and the mixed liquor suspended solids concentration was high, the average phosphate removal was approximately 55%, compared to only around 25% when the C/N ratio was less than 6.

Ultrasonication expedited As(III) adsorption onto chitosan impregnated Ni-Fe layered double hydroxide biosorbent: Optimization studies and artificial intelligence modelling

Chitosan intercalated Ni-Fe layered double hydroxide (Ni-Fe LDH/Ch), prepared by co-precipitation was examined for adsorptive elimination of arsenic (III). Energy Dispersive X-ray analysis, X-ray diffraction, Fourier Transform Infrared spectroscopy, Scanning Electron Microscopy, and Dynamic Light Scattering validated the successful synthesis of the composite with enhanced adsorption sites. Maximal As(III) removal was obtained at adsorbent dose 1 gL^-1, pH 7, ultrasonication time 30 min, temperature 298 K, and initial arsenic concentration 50 mgL^-1. The experimentally obtained values fit the Langmuir isotherm and pseudo-second-order dynamics well (R² > 0.98), while thermodynamic evaluation confirmed exothermic and spontaneous reaction (ΔG = -8.13 kJ mol^-1). Further, adaptive neuro-fuzzy inference system and artificial neural network successfully predicted As(III) removal percentage with a high correlation coefficient (R² > 0.94) and low statistical errors (MSE< 0.002, AARE< 0.063). The prepared material successfully brought down arsenic level by 62% in a natural water sample and showed good reusability up to 5 consecutive treatment cycles. The results recommended that Ni-Fe LDH/Ch has ample potential for arsenic remediation, and further investigations can be carried out for large-scale applications.

Remediation of carcinogenic arsenic by pyroaurite-based green adsorbent: isotherm, kinetic, mechanistic study, and applicability in real-life groundwater

The removal of the harmful carcinogen arsenic from drinking water by a green technology is a major concern in the field of environmental engineering. The sorptive profile of arsenic remediation by calcined Mg-Fe-layered double hydroxide, fabricated by a one-pot synthesis technique, was investigated to delineate its applicability in real-life water. The physicochemical properties of adsorbent, as demonstrated from spectroscopy and microscopy, which described the existence of amorphous material with significant surface roughness possess selectivity towards arsenic. The isotherm and kinetic along with thermodynamic modeling exhibited the occurrence of spontaneous (ΔG⁰ value = - 8.084 kJ/mol to - 10.942 kJ/mol), endothermic (ΔH⁰ value = 12.135 kJ/mol), and physisorption reactions (E_ad = 4.103-5.832 kJ/mol, E_a = 11.546 kJ/mol, S* = 0.0005 << 1, and ΔH_x = 9.23-16.29 kJ/mol) with high uptake rate and adsorption potential of adsorbent. The isotherm and kinetics were demonstrated by Temkin (R² = 0.944-0.969) and Elovich (R² = 0.996-0.998) models, respectively, with high statistical significance. The intraparticle diffusion model which established the rate-limiting step is the combination of both film and pore diffusions. The applicability of layered double hydroxide (LDH) material in the real-life water was confirmed by isotherm and kinetic modeling along with the regeneration/reuse potential. The adsorptive removal of arsenic by the LDH material exhibited to be a promising technique without creating any secondary hazard.

Efficient removal of iodide anion from aqueous solution with recyclable core-shell magnetic Fe3O4@Mg/Al layered double hydroxide (LDH)

Magnetic Mg/Al layered double hydroxides (LDH) with three cationic ratios (Mg/Al = 2:1, 3:1, and 4:1) were successfully synthesized and utilized for the first time in an iodide adsorption study. The effects of the Mg/Al ratio of LDH on iodide adsorption were investigated, and physicochemical properties of synthetic LDHs depending on Mg/Al ratio were confirmed by XRD, TEM, ICP-OES, VSM, Zeta-potential, and BET analyses. The ferrimagnetic property was well preserved even after a coating of LDH on magnetite irrespective of the Mg/Al ratio. Among the three Mg/Al ratios, the calcined Fe₃O₄@4:1 Mg/Al LDH exhibited excellent performance for iodide removal with 105.04 mg/g of the maximum iodide adsorption capacity due to its wide interlayer spacing and largest BET surface area. In the presence of competing carbonate anions, the Fe₃O₄@4:1 LDH showed removal rate of >80% at a dosage of over 3 g/L solid to liquid ratio. The recyclability test of Fe₃O₄@4:1 LDH showed that the removal performance for iodide is maintained at >80% even during the first to the fourth cycles. These results demonstrated that the magnetic Mg/Al LDH adsorbent can be effectively utilized for remediation of radioactive iodide anions with high efficiency and economics.

Modeling and analysis of adsorptive removal of arsenite by Mg-Fe-(CO3) layer double hydroxide with its application in real-life groundwater

The kinetic, isotherm and thermodynamic modeling of the adsorption of arsenite by layered double hydroxide have been performed to analyze the feasibility, efficacy and mechanism of the system. The fast uptake was observed during the initial phase of the process, which reached equilibrium at 240 min following Elovich model. The diffusion kinetic model exhibited that the rate-limiting step of adsorption was controlled by film diffusion as well as intraparticle diffusion. The isotherm modeling revealed the applicability of the Freundlich equation with the K_f values as 8.19-13.99 (mg g^-1)(L mg^-1)^1/n at 283-323 K showing the increasing trend of adsorption capacity, which was further confirmed by the positive value of ΔH⁰ (9.49 kJ mol^-1) demonstrating the endothermic nature of the adsorption process. The spontaneous nature of the adsorption reaction was established by the negative values of ΔG⁰. Application of the calcined Mg-Fe-LDH adsorbent for the removal of arsenic from real arsenic contaminated groundwater was also successfully performed. The effect of process parameters of the adsorption system was modeled by an artificial neural network (ANN) for adsorption capacity and removal efficiency. The optimized model exhibited high R², F-value and low values of error functions, establishing the significant applicability of the ANN model.

Sorptive equilibrium profile of fluoride onto aluminum olivine [(FexMg1-x)2SiO4] composite (AOC): Physicochemical insights and isotherm modeling by non-linear least squares regression and a novel neural-network-based method

A novel aluminum/olivine composite (AOC) was prepared by wet impregnation followed by calcination and was introduced as an efficient adsorbent for defluoridation. The adsorption of fluoride was modeled with one-, two- and three-parameter isotherm equations by non-linear regression to demonstrate the adsorption equilibrium. The FI was the best-fitted model among the two-parameter isotherms with a R² value of 0.995. The three-parameter models were found to have better performance with low values of the error functions and high F values. The neural-network-based model was applied for the first time in the isotherm study. The optimized model was framed with eight neurons in hidden layer with a mean square of error of 0.0481 and correlation coefficient greater than 0.999. The neural-based model has the better predictability with a higher F value of 9484 and R² value of 0.998 compared to regression models, exhibiting the F value and the R² in the range of 86-3572 and 0.835-0.995, respectively. The material characterization established the formation of the aluminum oxide, silicate, etc. onto the olivine which is conducive of the removal of fluoride by the formation of aluminum fluoride compounds, such as AlF₃ in the spent material after defluoridation.

Adsorptive removal of arsenic by novel iron/olivine composite: Insights into preparation and adsorption process by response surface methodology and artificial neural network

Olivine, a low-cost natural material, impregnated with iron is introduced in the adsorptive removal of arsenic. A wet impregnation method and subsequent calcination were employed for the preparation of iron/olivine composite. The major preparation process parameter, viz., iron loading and calcination temperature were optimized through the response surface methodology coupled with a factorial design. A significant variation of adsorption capacity of arsenic (measured as total arsenic), i.e., 63.15 to 310.85 mg/kg for arsenite [As(III)^T] and 76.46 to 329.72 mg/kg for arsenate [As(V)^T] was observed, which exhibited the significant effect of the preparation process parameters on the adsorption potential. The iron loading delineated the optima at central points, whereas a monotonous decreasing trend of adsorption capacity for both the As(III)^T and As(V)^T was observed with the increasing calcination temperature. The variation of adsorption capacity with the increased iron loading is more at lower calcination temperature showing the interactive effect between the factors. The adsorbent prepared at the optimized condition of iron loading and calcination temperature, i.e., 10% and 200 °C, effectively removed the As(III)^T and As(V)^T by more than 96 and 99%, respectively. The material characterization of the adsorbent showed the formation of the iron compound in the olivine and increase in specific surface area to the tune of 10 multifold compared to the base material, which is conducive to the enhancement of the adsorption capacity. An artificial neural network was applied for the multivariate optimization of the adsorption process from the experimental data of the univariate optimization study and the optimized model showed low values of error functions and high R² values of more than 0.99 for As(III)^T and As(V)^T. The adsorption isotherm and kinetics followed Langmuir model and pseudo second order model, respectively demonstrating the chemisorption in this study.

An efficient adsorption of indigo carmine dye from aqueous solution on mesoporous Mg/Fe layered double hydroxide nanoparticles prepared by controlled sol-gel route

A new approach for removal of indigo carmine blue (IC) dye which is extensively used in jeans manufacture was successfully performed on novel mesoporous [LDH] nanoparticles prepared by sol-gel route using CTAB as shape and pore directing agent. The physicochemical features were monitored by X-ray diffraction (XRD), Fourier transformer infra-red (FTIR), N₂ adsorption-desorption isotherm, Field emission electron microscope (FESEM) and high resolution transmission electron microscope (HRTEM). The influence of reaction parameters affecting dye adsorption including contact time, initial dye concentration, pH and temperature were investigated. Textural analysis and HRTEM images indicate the existence of mesoporous spherical nanoparticles of size = 26 nm connected to each other's and embedded large numbers of mesopores of average pore radius = 43.5 Å. A successful adsorption of IC on LDH nanoparticles of surface area = 85.6 m²/g at various pH with maximum adsorption capacity = 62.8 mg/g at pH = 9.5. Langmuir model is more favorable to describe the adsorption of IC rather than Freundlich model which reflecting the preferential formation of monolayer on the surface of LDH. Both film diffusion and the intraparticle diffusion affect the dye adsorption. The values of enthalpy change (ΔH) for and (ΔS) are + 28.18 and + 0.118 kJ/mol, respectively indicating that the removal process is endothermic. The results indicated that LDH nanoparticles conserved a good activity even after five consecutive cycles of reuse. Our results suggest that mesoporous LDH nanoparticles are considered a potential novel adsorbent for remediation of wastewater containing IC.