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Numerous harmful phenolic contaminants are discharged into water that pose a serious threat to environment where two of the most important purification methodologies for the mitigation of phenolic contaminants are adsorption and photocatalysis. Besides cost, each process has drawbacks in terms of productivity, environmental impact, sludge creation, and the development of harmful by-products. To overcome these limitations, the modeling and optimization of water treatment methods is required. Artificial Intelligence (AI) is employed for the interpretation of treatment-based processes due to powerful learning, simplicity, high estimation accuracy, effectiveness, and improvement of process efficiency where artificial neural networks (ANNs) are most frequently employed for predicting and analyzing the efficiency of processes applied for the mitigation of these phenolic contaminants from water. ANNs are superior to conventional linear regression models because the latter are incapable of dealing with non-linear systems. ANNs can also reduce the operational cost of treating phenol-contaminated water. A correlation coefficient of >0.99 can be achieved using ANN with enhanced phenol mitigation percentage accuracy generally ranging from 80 % to 99.99 %. Using ANN optimization, the maximum phenol mitigation efficiencies achieved were 99.99 % for phenol, 99.93 % for bisphenol A, 99.6 % for nonylphenol, 97.1 % for 2-nitrophenol, 96.6 % for 4-chlorophenol and 90 % for 2,6-dichlorophenol. In numerous ANN models, Levenberg-Marquardt backpropagation algorithm for training was employed using MATLAB software. This study overviews their employment and application for optimization and modeling of removal processes and explicitly discusses the important input and output parameters necessary for better performance of the system. The comparison of ANNs with other AI techniques revealed that ANNs have better predictability for mitigation of most of the phenolic contaminants. Furthermore, several challenges and future prospects have also been discussed.

Copper oxide nanoparticles (CuO NPs) have been prepared via sol-gel synthetic approach using aqueous leaves extract of Musa paradisiacal and copper chloride dehydrate salt. UV visible spectroscopy showed maximum peak for CuO NPs at 535 nm. Additionally, the SEM XRD techniques confirmed spherical shape of CuO NPs with average size of 15 nm. Nitro compounds have been carefully chosen as a tested contaminant to study performance of CuO NPs. Catalytic reduction of nitro compounds was investigated under different temperatures to evaluate thermodynamic studies. According to the results, catalytic reduction of nitro compounds obeys Langmuir–Hinshelwood mechanism. The value of apparent rate constant shows a linear trend with catalyst concentration. The catalytic pyrolysis of corncob biomass in the presence of CuO NPs showed more bio-oil (46.13 %) yield as compared to ZSM-5 (40.07 %) and without catalyst (37.09 %) reactions. The data also confirmed that CuO NPs showed excellent performance as a micro-reactor for catalytic degradation of nitro compounds and catalytic pyrolysis. The CuO NPs have been isolated and reused in 5 consecutive cycles with good and reproducible excellent performance.

The current study is based on the synthesis and characterization of ZnO–NiO/rGO nanohybrid for photocatalytic degradation of organic pollutants. The physicochemical properties of synthesized products were estimated by X-ray diffraction technique (XRD), Fourier transform infrared spectroscopy (FTIR), field emission-scanning electron microscopy (FESEM), and Ultraviolet–Visible spectroscopy. The diffraction data showed the formation of binary metal oxide nanocomposite, containing ZnO in hexagonal whereas NiO in the cubic crystalline phase. XRD results revealed that the calculated crystallite size of NiO and ZnO in the ZnO–NiO nanocomposite was

In this research work, selenium dioxide (SeO2) nanorods have been prepared by a solvothermal method in which a strong reducing agent (NaBH4) was used to reduce precursor salt into SeO2 nanorods. X-ray diffraction (XRD) technique was applied to observe the crystal structure which confirmed its tetragonal geometry. Moreover, morphology and particle size were studied by scanning electron microscopy (SEM). SEM fully described the 1-dimensional morphology of SeO2 nanorods which then arranged themselves to create a 3-dimensional flower-like structure with an average particle size of 50 nm. Also, the catalytic activity of SeO2 nanorods as diesel-additive was studied by defining different parameters such as fire and flash points, calorific value, cloud and pour points, specific gravity, and kinematic viscosity. Subsequently, SeO2 nanorods proved to be an excellent diesel additive due to higher total heat content and lower value of kinematic viscosity which enhances the better performance of the diesel engine.

An ordered mesoporous novel material Ni-SBA-16 has been synthesized by internal pH-adjustment method. The synthesized material has been characterized by small angle XRD (SXRD), wide angle XRD (WXRD), Scanning Electron Microscope (SEM), High-Resolution Transmission Electron Microscope (HRTEM), Fourier-transform Infra-red (FTIR) spectroscope and Nitrogen Adsorption desorption techniques. The characterization results have shown that material possesses highly ordered mesostructure with high surface area (736 m2/g) and large pore diameter (3.8 nm). FTIR and WXRD spectra revealed that nickel was uniformly dispersed on SBA-16 surface. The synthesized material has been used as adsorbent for removal of toxic Brilliant Green dye due to its large surface area and pore size. At optimized conditions, almost 100% of Brilliant Green dye was removed from aqueous solution by Ni-SBA-16. The isotherms analysis indicates that the Langmuir and Hill models provide the best correlation of the experimental data. The maximum adsorption capacity (qmax) of Ni-SBA-16 for Brilliant Green dye was 322.58 mg/g.

In the present study a biomass derived from the leaves of Acacia nilotica was used as an adsorbent material for the removal of cadmium and lead from aqueous solution. The effect of various operating variables, viz., adsorbent dosage, contact time, pH and temperature on the removal of cadmium and lead has been studied. Maximum adsorption of cadmium and lead arises at a concentration of 2 g/50 ml and 3 g/50 ml and at a pH value of 5 and 4, respectively. The sorption data favored the pseudo-second-order kinetic model. Langmuir, Freundlich and Dubinin–Radushkevich (D–R) models were applied to describe the biosorption isotherm of the metal ions by A. nilotica biomass. Based on regression coefficient, the equilibrium data found were fitted well to the Langmuir equilibrium model than other models. Thermodynamic parameters such as free energy change (ΔG°), enthalpy change (ΔH°) and entropy change (ΔS°) have been calculated, respectively revealed the spontaneous, endothermic and feasible nature of adsorption process. The activation energy of the biosorption (Ea) was estimated as 9.34 kJ mol−1 for Pb and 3.47 kJ mol−1 for Cd from Arrhenius plot at different temperatures.