Removal of malachite green from aqueous solution by zinc oxide nanoparticle loaded on activated carbon: Kinetics and isotherm study

In this research, a novel adsorbent, zinc oxide nanoparticle loaded on activated carbon (ZnO-NP-AC) was synthesized by a simple, low cost and efficient procedure. Subsequently, this novel material was characterizated and identified by different techniques such as Brunauer, Emmett and Teller (BET), scanning electron microscopy (SEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR) analysis. Unique properties such as high surface area (>603 m²/g) and low pore size (<61 Å) and average particle size lower than 100 Å in addition to high reactive atom and presence of various functional groups make it possible for efficient removal of malachite green (MG). In batch experimental set-up, optimum conditions for quantitative removal of MG by ZnO-NP-AC was attained following searching effect of variables such as adsorbent dosage, initial dye concentration and pH. Optimum values were set as pH of 7.0, 0.015 g of ZnO-NP-AC at removal time of 15 min. Kinetic studies at various adsorbent dosage and initial MG concentration show that maximum MG removal was achieved within 15 min of the start of every experiment at most conditions. The adsorption of MG follows the pseudo-second-order rate equation in addition to interparticle diffusion model (with removal more than 95%) at all conditions. Equilibrium data fitted well with the Langmuir model at all amount of adsorbent, while maximum adsorption capacity was 322.58 mg g⁻¹ for 0.005 g of ZnO-NP-AC.     

Modeling, analysis and optimization of aircyclones using artificial neural network, response surface methodology and CFD simulation approaches

The pressure drop is an important performance parameter to evaluate and design cyclone separators. In order to accurately predict the complex non linear relationships between pressure drop and geometrical dimensions, a radial basis neural network (RBFNN) is developed and employed to model the pressure drop for cyclone separators. The neural network has been trained and tested by experimental data available in literature. The result demonstrates that artificial neural networks can offer an alternative and powerful approach to model the cyclone pressure drop. Four mathematical models (Muschelknautz method “MM”, Stairmand, Ramachandran and Shepherd & Lapple) have been tested against the experimental values. The residual error (the difference between the experimental value and the model value) of the MM model is the lowest. The analysis indicates the significant effect of the vortex finder diameter D_x and the vortex finder length S, the inlet width b and the total height H_t. The response surface methodology has been used to fit a second order polynomial to the RBFNN. The second order polynomial has been used to get a new optimized cyclone for minimum pressure drop using the Nelder-Mead optimization technique. A comparison between the new design and the standard Stairmand design has been performed using CFD simulation. CFD results show that the new cyclone design is very close to the Stairmand high efficiency design in the geometrical parameter ratio, and superior for low pressure drop at nearly the same cut-off diameter. The new cyclone design results in nearly 75% of the pressure drop obtained by the old Stairmand design at the same volume flow rate.     

Adsorptive properties of As(III) from aqueous solution using magnetic nickel ferrite (NiFe2O4) nanoparticles: Isotherm and kinetic studies

As(III) adsorption on NiFe₂O₄ nanoparticles were systematically investigated by controlling parameters such as stirring rate, pH, initial arsenic concentration, contact duration, temperature, and adsorbent dose. It was observed that the amount of adsorbed arsenic concentration is strongly depended on pH and temperature. The temperature and pH give rise to significant changes in the amount of adsorbed arsenic. As compared with Langmuir and Freundlich isotherms models, the latter is found to be well suited. Pseudo-first order, pseudo-second order, and intraparticle diffusion models were applied to adsorption equilibrium data obtained from the analysis of arsenic with diverse amount of initial concentration.     

Combination of ACO-artificial neural network method for modeling of manganese and cobalt extraction onto nanometer SiO2 from water samples

In this study, modeling based on ant-colony optimization – artificial neural network have been employed to develop the model for simulation and optimization of nanometer SiO₂ for the extraction of manganese and cobalt from water samples. The pH, time, amount of SiO₂ nanoparticles and concentration of 1-(2-pyridylazo)-2-naphthol (PAN) were the input variables, while the extraction% of analytes was the output. Under the optimum conditions, the detection limits were 0.52 and 0.7 μg L^?1, for manganese and cobalt, respectively. The method was applied to the extraction of manganese and cobalt from water samples and one certified reference material.     

Kinetic,isotherm, and thermodynamic studies of Cr(VI) removal from aqueous solution using mesoporous silica materials prepared by fly ash

A series of mesoporous silica materials (FMD, FMT, and FMC were synthesized with DTAB, TTAB, and CTAB as template, respectively) have been prepared using fly ash as a silica resource. The as-synthesized materials were characterized by BET, XRF, FTIR, and XPS. The results confirmed the mesoporous structure and nitrogen content to act as potential adsorbents. The adsorption properties of these materials were also investigated by batch adsorption experiments. The FMC exhibited the highest effective removal of Cr(VI) (99%). The Cr(VI) adsorption process over FMC follows the pseudo-second-order kinetic and Langmuir model. Thermodynamic studies revealed that the Cr(VI) adsorption by FMC was spontaneous and endothermic. The study of the adsorption mechanism showed that the removal of Cr (VI) by FMC is through electrostatic attraction and chemical reduction. The coexisting ions experiment showed that FMC had high selectivity for Cr(VI). After three regeneration cycles, the Cr(VI) removal rate of FMC adsorbent still remained about 80%. Thus, this inexpensive adsorbent (FMC) is suitable for removing Cr(VI) from discharged industrial water.     

Prediction of macerals contents of Indian coals from proximate and ultimate analyses using artificial neural networks

Coal, a prime source of energy needs in-depth study of its various parameters, such as proximate analysis, ultimate analysis, and its biological constituents (macerals). These properties manage the rank and calorific value of various coal varieties. Determination of the macerals in coal requires sophisticated microscopic instrumentation and expertise, unlike the other two properties mentioned above. In the present paper, an attempt has been made to predict the concentration of macerals of Indian coals using artificial neural network (ANN) by incorporating the proximate and ultimate analysis of coal. To investigate the appropriateness of this approach, the predictions by ANN are also compared with conventional multi-variate regression analysis (MVRA). For the prediction of macerals concentration, data sets have been taken from different coalfields of India for training and testing of the network. Network is trained by 149 datasets with 700 epochs, and tested and validated by 18 datasets. It was found that coefficient of determination between measured and predicted macerals by ANN was quite higher as well as mean absolute percentage error was very marginal as compared to MVRA prediction.     

Kaolinite, montmorillonite, and their modified derivatives as adsorbents for removal of Cu(II) from aqueous solution

Adsorption of metals by clay minerals is a complex process controlled by a number of environmental variables. The present work investigates the removal of Cu(II) ions from an aqueous solution by kaolinite, montmorillonite, and their poly(oxo zirconium) and tetrabutylammonium derivatives. The entry of ZrO and TBA into the layers of both kaolinite and montmorillonite was confirmed by XRD measurement. The specific surface areas of kaolinite, ZrO-kaolinite, TBA-kaolinite, montmorillonite, ZrO-montmorillonite, TBA-montmorillonite were 3.8, 13.4, 14.0, 19.8, 35.8 and 42.2 m²/g, respectively. The cation exchange capacity (CEC) was measured as 11.3, 10.2, 3.9, 153.0, 73.2 and 47.6 meq/100 g for kaolinite, ZrO-kaolinite, TBA-kaolinite, montmorillonite, ZrO-montmorillonite, TBA-montmorillonite, respectively. Adsorption increased with pH till Cu(II) ions became insoluble in alkaline medium. The kinetics of the interactions suggests that the interactions could be best represented by a mechanism based on second order kinetics (k₂ = 7.7 × 10⁻² to 15.4 × 10⁻² g mg⁻¹ min⁻¹). The adsorption followed Langmuir isotherm model with monolayer adsorption capacity of 3.0–28.8 mg g⁻¹. The process was endothermic with ΔH in the range 29.2–50.7 kJ mol⁻¹ accompanied by increase in entropy and decrease in Gibbs energy. The results have shown that kaolinite, montmorillonite and their poly(oxo zirconium) and tetrabutyl-ammonium derivatives could be used as adsorbents for separation of Cu(II) from aqueous solution.     

Modelling of adsorption of nickel (II) by blend hydrogels (cellulose nanocrystals and corn starch) from aqueous solution using adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural networks (ANN)

The current study looks at the effectiveness of the removal of nickel (II) from aqueous solution using an adsorption method in a laboratory-size reactor. An artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) were used in this study to predict blend hydrogels adsorption potential in the removal of nickel (II) from aqueous solution. Four operational variables, including initial Ni (II) concentration (mg/L), pH, contact duration (min), and adsorbent dose (mg/L), were used as an input with removal percentage (%) as the only output; they were studied to assess their impact on the adsorption study in the ANFIS model. In contrast, 70% of the data was used for training, while 15% of the data was used in testing and validation to build the ANN model. Feedforward propagation with the Levenberg–Marquardt algorithm was employed to train the network. The use of ANN and ANFIS models for experiments was used to optimize, construct, and develop prediction models for Ni (II) adsorption using blend hydrogels. The adsorption isotherm and kinetic models were also used to describe the process. The results show that ANN and ANFIS models are promising prediction approaches that can be applied to successfully predict metal ions adsorption. According to this finding, the root mean square errors (RMSE), absolute average relative errors (AARE), average relative errors (ARE), mean squared deviation (MSE), and R² for Ni (II) in the training dataset were 0.061, 0.078, 0.017, 0.019, and 0.986, respectively, for ANN. In the ANFIS model, the RMSE, AARE, ARE, MSE, and R² were 0.0129, 0.0119, 0.028, 0.030, and 0.995, respectively. The adsorption process was spontaneous and well explained by the Langmuir model, and chemisorption was the primary control. The morphology, functional groups, thermal characteristics, and crystallinity of blend hydrogels were all assessed.     

Assessment of Cu (II) removal from an aqueous solution by raw Gundelia tournefortii as a new low-cost biosorbent: Experiments and modelling

Lignocellulosic materials can be used as biosorbent for refinement of the wastewaters when they are available in large quantities. Many studies were conducted to uptake Cu (II) ion from aqueous solutions. In this paper, the biosorption efficiency of Cu (II) ions from a synthetic aqueous solution was investigated using Gundelia tournefortii (GT), without any pre-treatment. Fourier transform infrared spectroscopy, scanning electron microscopy and determining the point of zero charge were employed to characterise the biosorbent. Batch experiments were performed to study the influence of pH, biosorbent dosage, contact time, temperature and initial Cu (II) concentration on Cu (II) removal. The biosorption isotherms were investigated using the Langmuir, Freundlich, Temkin and D-R isotherm models. The findings show that the biosorption isotherm was better fitted by the Langmuir equation and the maximum adsorption capacity of GT was found to be 38.7597 mg·g^-1. The kinetics data were analysed by pseudo-first order, pseudo-second order, and intra-particle diffusion equations. The results indicate that the pseudosecond-order model was found to explain the adsorption kinetics most effectively. The values of thermodynamic parameters including Gibbs free energy (△G°), enthalpy (△H°), and entropy (△S°) demonstrate that the biosorption process was exothermic and spontaneous. The multiple nonlinear regression (MnLR) and artificial neural network (ANN) analyses were applied for the prediction of biosorption capacity. A relationship between the predicted and observed data was obtained and the results show that the MnLR and ANN models provided successful predictions.     

Selective removal of Cr (VI) from hydrometallurgical effluent using modified cellulose nanocrystals (CNCs) with succinic anhydride and ethylenediaminetetraacetic acid: Isotherm,kinetics, and thermodynamic studies

Biological sources are renewable basic resources that may be used for several purposes, including the development of green materials for the removal of heavy metal ions. Cellulose nanocrystals (CNCs) extracted from waste papers via acid hydrolysis were modified and utilized as adsorbents to remove Cr (VI) ions from metallurgical effluent in this work. X-ray diffraction, scanning electron microscopy, Fourier-transform infrared spectroscopy, thermogravimetric analysis, and zeta potentiometer were used to characterize the CNCs. The CNCs treated with succinic anhydride and ethylenediaminetetraacetic acid tetrasodium salt have thin particle sizes and are porous. The carboxylate functional group is primarily engaged in the coordination and selective removal of metal ions (–COO²⁻) and thermal degradation of 85%, observed at temperatures between 250–380°C. On the surface of the modified CNCs, the zeta potential data showed a decrease in negative value. The results revealed that the modified CNCs had a maximum adsorption capacity of 387.25 ± 0.88 mg L⁻¹ at pH 5, at CNCs doses of 25 and 400 mg L⁻¹ as starting concentrations. The adsorption equilibrium period was 300 min and the temperature was 313 K. The equilibrium results fit the Langmuir isotherm model with an R² of 0.993 and a qmax of 340 ± 0.97. The Chi-square (X²) and Marquardt's percent standard deviation tests confirmed that the adsorption process was pseudo-second-order with an R² of 0.998, and the Elovich model revealed that Cr (VI) complexed with the adsorbent's functional groups. The reaction was endothermic due to positive ΔH and spontaneous due to negative ΔG. The positive ΔS indicates that the adsorption process enhances the unpredictability of the solid/liquid interface, according to thermodynamic analysis. After acid treatment, the CNCs may be effectively reused for six cycles with an adsorption capacity of 220 ± 0.78 mg g⁻¹.     

Fixed-bed column study on removal of Mn(II), Ni(II) and Cu(II) from aqueous solution by surfactant bilayer supported alumina

In the present work, the potential of modified alumina for the removal of heavy metals such as Mn(II), Ni(II) and Cu(II) was evaluated in a fixed-bed column operation. The effects of bed depth, flow rate and initial concentration on the removal of Mn(II), Ni(II) and Cu(II) were investigated at the optimum pH. The modified alumina was found to be very efficient for the removal of such heavy metals from water environment. Bed depth service time (BDST) model was best fitted to adsorption data. The theoretical and experimental breakthrough curves were comparable for all heavy metals.     

Synthesis of Fe2O3-coated and HCl-treated bauxite ore waste for the adsorption of arsenic (III) from aqueous solution: Isotherm and kinetic models

The present work has focused on the removal of arsenic (III) using two effective adsorbents such as red mud treated with HCl and coated with Fe₂O₃. Adsorption of As (III) was performed by the function of pH, adsorbent dose, contact time, initial ion concentration, and the appropriate conditions for adsorption were determined. The characterization studies of the adsorbent were analyzed using X-ray diffraction, X-ray fluorescence, Brauner–Emmett–Teller, scanning electron microscope, and FTIR spectroscopy. The result of the studies shows that the adsorbent is suitable for the effective removal of As (III) ions. Batch adsorption process showed that the maximum adsorption occurred at Fe₂O₃-coated red mud. The equilibrium data were well fitted to the nonlinear Langmuir isotherm model and the maximum adsorption capacity (q_m) of Fe₂O₃-coated red mud was found to be 21.85?mg?g^?1 which indicates that Fe₂O₃-coated red mud had more adsorption capacity. In the Freundlich isotherm, the experimentally obtained n value of Fe₂O₃-coated red mud was 2.393 which indicates the favorable adsorption of As (III) on the adsorbent. Dubinin–Radushkevich isotherm confirms that the adsorption process is physical in nature. Furthermore, the adsorption kinetic studies followed the pseudo-first-order model. All the results concluded that Fe₂O₃-coated red mud can be considered as a cost-effective and potential adsorbent for As (III) removal.     

Photocatalytic oxidation of treated municipal wastewaters for the removal of phenolic compounds: optimization and modeling using response surface methodology (RSM) and artificial neural networks (ANNs)

BACKGROUND: TiO₂ heterogeneous photocatalysis should be optimized before application for the removal of pollutants in treated wastewaters. The response surface methodology (RSM) and artificial neural networks (ANNs) were applied to model and optimize the photocatalytic degradation of total phenolic (TPh) compounds in real secondary and tertiary treated municipal wastewaters. RESULTS: RSM was developed by considering a central composite design (CCD) with three input variables, i.e. TiO₂ mass, initial concentration of TPh and irradiation intensity. At the same time a feed‐forward multilayered perceptron ANN trained using back propagation algorithms was used and compared with RSM. Under the optimum conditions established in experiments ([TPh]₀ = 3 mg L^?1; [TiO₂] = 300 mg L^?1; I = 600 W m^?2) the degradation for both TPh and total organic carbon (TOC) followed pseudo‐first‐order kinetic model. Complete degradation of TPh took place in 180 min and reduction of TOC reached 80%. A significant abatement of the overall toxicity was accomplished as revealed by Microtox bioassay. CONCLUSIONS: It was found that the variables considered have important effects on TPh removal efficiency. The results demonstrated that the use of experimental design strategy is indispensable for successful investigation and adequate modeling of the process and that ANNs gave better modelling capability than RSM. Copyright © 2012 Society of Chemical Industry