Predicted Adsorption Capacity Research Articles

Activated carbon from a specific plant precursor biomass for hazardous Cr(VI) adsorption and recovery studies in batch and column reactors: Isotherm and kinetic modeling

The main aim of this work was the development of the chemical activated carbon (AC) method from specific wood biomass, i.e., Eucalyptus camaldulensis sawdust (ECS), and this AC is proposed as a promising alternative treatment for hazardous Cr(VI) from aqueous solution. ECS waste sawdust was carbonized in two stages, i.e. 170 °C for 60 min, followed by 500 °C for 120 min under the continuous steam of nitrogen gas, and rated as an efficient method with H3PO4 activation (>80% Cr(VI) removal). Finally, activated carbon-Eucalyptus camaldulensis sawdust (AC-ECS) was selected for batch and column reactor studies and different influencing parameters, such as contact time, pH, temperature, initial Cr(VI) metal concentration, particle size, and bed height were optimized. AC-ECS was characterized through analysis by SEM, EDX, FTIR, and BET. Cr(VI) adsorption was found to be highly pH-dependent, i.e., 87% at pH 3.0. AC-ECS adsorption mechanism for Cr(VI) with experimental and maximum predicted adsorption capacities of 104 and 125 mg g−1, respectively, was best described by the Langmuir isotherm (R2 = 0.999) and pseudo-second-order kinetics (R2 = 0.999). The column study showed an improvement in the breakthrough curve time from 5595 to 12,270 min, with the respective increase of bed height from 5 to 15 cm, respectively. Column breakthrough data was found to be well fitted to the bed depth service time model. Current batch and column studies indicate that freshwater contamination with Cr(VI) can be managed by upscaling the AC-ECS as an efficient treatment solution.

Kinetic and thermodynamic study of thionine dye adsorption by peanut hull

ABSTRACT In the present study, thionine dye was adsorbed on the surface of the peanut hull. Experiments were carried out by changing speed of agitation (50–300) rpm, time of contact (0–300) min, initial concentration of dye (10–50 mg/L), adsorbent dose (2–8 g/L), solution pH (2–12), and temperature (298–333 K) to evaluate their effect on the adsorption. The characterisation of the peanut hull was done by using XRF, FTIR and SEM. The optimum conditions were found as agitation speed = 200 rpm; time of contact = 300 min; initial dye concentration = 20 mg/L; adsorbent dose = 4 g/L; solution pH = 8 and temperature = 313 K. Equilibrium isotherm and kinetic models were studied to estimate their parameters and to predict adsorption capacity of peanut hull. Langmuir isotherm was found to be the best-fitted isotherm with R 2 of 0.992 and contributes the maximum adsorption capacity of 8.45 mg/g. Pseudo-first-order describes the kinetics with R 2 equal to 0.99. The change in enthalpy and entropy were also estimated from Gibbs’ free energy.

Iron and zirconium modified luffa fibre as an effective bioadsorbent to remove arsenic from drinking water

Porous luffa plant fibre (LF) was grafted with Fe and Zr, and the ability of the fabricated adsorbents to remove arsenate (As(V)) from water was investigated in batch and column adsorption experiments. The Langmuir adsorption capacity (mg g−1) at pH 7 of LF was found to be 0.035, which increased to 2.55 and 2.89 after being grafted with Fe (FLF-3) and Zr (ZLF-3), respectively. Grafting with Fe and Zr increased the zeta potential and zero point of charge (ZPC) of LF (from pH 3.9 to 7.4 for Fe grafting and to 7.6 for Zr grafting), due to chemical bonding of the metals, possibly with the hydroxyl and carboxylic groups in LF as indicated in FTIR peaks. Zeta potential and ZPC decreased after As adsorption owing to inner-sphere complexation mechanism of adsorption. The increase of pH from 3 to 10 progressively reduced the adsorbents’ adsorption capacity. Co-existing anions weakened the As(V) removal efficiency in the order, PO43− > SiO32− > CO32− > SO42−. Adsorption kinetics data fitted well to the Weber and Morris model, which revealed initial fast and subsequent slow rates of intra-particle As diffusion into the bigger pores and smaller pores, respectively. Column adsorption data fitted well to the Thomas model with the predicted adsorption capacities in the same order as in the batch adsorption experiment (ZLF-3 > FLF-3 > LF).

Mesoporous iron oxide nano spheres for capturing organic dyes from water sources

Over the last few years mesoporous iron oxide nanosphere and related products have received disproportionate interest. Nano sphere with highly ordered mesoporous iron oxide (α-Fe2O3) was successfully synthesized. The chemical composition and crystal structure of the mesopoures iron oxide nano spheres (MINs) were confirmed by X–ray diffraction and Fourier transform infrared measurements. The surface morphology and spherical mesostructures of MINs were characterized by scanning electron microscope (SEM) and transmission electron microscopy (TEM). The morphology of the MINs in spherical form is very uniform, the average particle size of which is 23–27 nm in diameter range. The content MINs has a quite high BET surface area of 188.25 m2g−1 and a total pore volume of 0.14 cm3g−1. BJH average pore size is 3.5 nm which is beneficial for adsorption application. In order to find the optimal adsorption conditions for extracting Acid Red 57 (AR57) and Remazol Red (RR) from aqueous solutions, parameter factors like initial pH, dosage of adsorbent, contact period, and temperature were examined. For AR57 and RR respectively, the optimum pH for extracting the investigated anionic dyes from water solutions was found to be 3 and 4. The maximum predicted adsorption capacities for AR57 and RR dyes were obtained as 930.16 and 896.87 mgg−1, respectively. The adsorption equilibrium also showed that for both AR57 and RR, the Langmuir equation provided the best fit of the experimental data compared with the other models. The mean values of adsorption energy (Ea) are 18.23 and 22.95 kJmol−1 for AR57 and RR, respectively, suggesting a chemisorption process. The adsorption kinetics was found to obey the kinetic model of the pseudo-second order. The adsorption process was verified to be endothermic and spontaneous by thermodynamic studies. The method of desorption of the adsorbed anionic dyes was also studied using ethanol as the solvent.

Assessing corrosion inhibition characteristics of hydrazone derivatives on mild steel in HCl: Insights from electronic-scale DFT and atomic-scale molecular dynamics

Abstract Understanding and predicting adsorption capacities of corrosion inhibitors on the metal surface is of great significance for designing high-performance inhibitor molecules. Herein, a computational methodology, which utilizes molecular dynamics (MD) simulation and Density Functional Theory (DFT), was adopted to investigate the inhibition performance of four hydrazone derivatives (HDZ1-HDZ4) on mild steel corrosion in acidic medium. The adsorption of the four compounds on the Fe (110) surface was studied and analyzed using global and local reactivity descriptors, molecular electrostatic potential (MEP), MD, and radial distribution function (RDF). MD results showed that the binding energy of investigated compounds with the iron surface follows the order of HDZ1 (912 kJ/mol) > HDZ2 (857 kJ/mol) > HDZ3 (801 kJ/mol) > HDZ4 (711 kJ/mol). Furthermore, the fractional free volume (FFV) and the interaction energy were selected for the evaluation of the diffusion behavior of hydronium and chloride ions inside inhibitor films. The results showed that both factors had a vital effect on the diffusion coefficient of corrosive particles. All results proved that inhibitor molecules having a high electron-accepting ability interact actively with the iron surface. Additionally, a direct relationship was observed between the experimentally determined inhibition efficiency and the adsorption strengths obtained from DFT and MD studies.

Estimation of adsorption capacity of CO2, CH4, and their binary mixtures in Quidam shale using LSSVM: Application in CO2 enhanced shale gas recovery and CO2 storage

Carbon dioxide enhanced shale gas recovery depends strongly on adsorption properties of carbon dioxide and methane. In this work, Least Squares Support Vector Machine (LSSVM) optimized by Particle Swarm Optimization, has been proposed to learn and then predict adsorption capacity of methane and carbon dioxide from pure and binary gas mixtures in Jurassic shale samples from the Qaidam Basin in China based on input parameters pressure, temperature, gas composition and TOC. A literature dataset of 348 points was applied to train and validate the model. The predicted values were compared with the experimental data by statistical and graphical approaches. The coefficients of determination of carbon dioxide adsorption were calculated to 0.9990 and 0.9982 for training and validation datasets, respectively. For CH4 the numbers are 0.9980 and 0.9966. The model was extrapolating reasonable trends beyond measurement ranges. More extensive datasets are needed to properly parameterize the role of shale properties.

Biomass ashes as potent adsorbent for pesticide: prediction of adsorption capacity by artificial neural network

Biomass ashes are used for adsorption of herbicides from aqueous solution. A relationship between physicochemical properties of biomass ashes such as carbon–hydrogen–nitrogen content (CHN analysis), silica content and BET surface area with their adsorption capacity was established and modeled using artificial neural network. 2,4-Dichlorophenoxyacetic acid (2,4-D) a commonly used herbicide is chosen a representative for this study. The artificial neural network model was trained, validated and tested using 35 data sets and was equipped with nine neuron hidden layers having tansig (tangent sigmoid) transfer function and an output layer with purelin (purely linear) transfer function. This model can be used to predict 2,4-D removal efficacy of any biomass ash by knowing its physicochemical properties like C, H, N, Si and BET surface area.

Correlations and prediction of adsorption capacity and affinity of aromatic compounds on activated carbons

Correlations capable of predicting organic compound adsorption by activated carbons (ACs) are essential to the applications of ACs as environmental adsorbents in water treatment. Adsorption isotherms of 21 aromatic compounds on 11 ACs both with various physicochemical properties were conducted and fitted by Dubinin-Ashtakhov model to develop the predictive correlations in this study. In addition to the correlations of adsorption capacity with total surface area of ACs, micropore surface area ratios (Rmicro) of ACs and chemical molar volume reported in previous studies, the negative correlation of adsorption capacity with chemical melting point was newly observed in this study. This negative correlation could be attributed to expansion of chemicals adsorbed on the mesopore or external surface of ACs. Meanwhile, in addition to the positive correlations of adsorption affinity with Rmicro of ACs, chemical polarity/polarizability and hydrogen bonding donor ability reported also in previous studies, the negative correlation of adsorption affinity with H/C of ACs was newly observed in this study, which should be attributed to that ACs with higher aromaticity could have stronger π-π interaction potential, hydrogen bonding interaction potential and hydrophobic effects for aromatic compounds. These observed correlations can be used to predict aromatic compound adsorption by ACs with readily available properties of both ACs (i.e., surface area, Rmicro and H/C) and aromatic compounds (i.e., molar volume, melting point and solvatochromic parameters). Moreover, these predictive correlations, incorporating various adsorptive forces, steric hindrance effect and packing efficiency in adsorption and having clearly physicochemical significance, are important for exploring the adsorption mechanisms, and guiding the synthesis of ACs with desired physicochemical properties, and selecting ACs as adsorbents in water treatment applications.

Characteristically Insights, Artificial Neural Network (ANN), Equilibrium, and Kinetic Studies of Pb(II) Ion Adsorption on Rice Husks Treated with Nitric Acid

The uses of rice husk are constantly increasing since the last decay. Rice husk is a by-product attained from agricultural activities and has a potential use as a bio-sorbent for the adsorption of heavy metal ions, for example, lead removal from water and other aqueous solutions. The basic objective of this research is to synthesis environment-friendly adsorbent using rice husks. The study also investigated the effects of rice husk treatment on the elimination of Pb(II) ion from aqueous solutions. The results of adsorption capability also been related to the commercial activated carbon (CAC). Rice husks were treated in a few processes which were the pretreatment of raw rice husks, chemical activation, and carbonization process. Subsequently, it was characterized using X-ray diffraction (XRD) and SEM. The structure of the adsorbent using rice husks is found to be amorphous. Based on the treated rice husks characterization, the optimum carbonization temperature is found to be 600 °C. Temperature alteration showed 50–60% weight loss, with up to 3.475% removal. Artificial neural network modeling was applied to predict the experimental data sets using feed forward back-propagation neural network (FFBPNN) and Levenberg–Marquardt (L–M) training algorithm. The customized neural network was applied to emphasize the predicted adsorption capacity and removal/uptake percentage of the investigated bio-sorbents. The outcomes from the artificial neural network model showed high validity of the predicted data compared to the initially examined experimental data sets. The adsorption efficiency increases with the increment of carbon presence ratio in the adsorbent. The adsorption measurements are well presented by the Langmuir isotherm. The kinetic modelling revealed that the adsorption of Pb(II) ions followed the pseudo-second-order models. The reported results in this work can deliberate the rice husks as a potential alternative to commercial adsorbents with lower cost and better environmental aspects.

Prediction of adsorption capacity for pharmaceuticals, personal care products and endocrine disrupting chemicals onto various adsorbent materials

Adsorption is a common process used to remove pharmaceuticals, personal care products and endocrine disrupting chemicals (PPCPs/EDCs) from water. However, as PPCPs/EDCs cover a wide range of molecules and chemical structures, prediction of the adsorption capacity is very challenging. In this study, a novel model was developed to predict adsorption isotherms of PPCPs/EDCs onto various types of adsorbents using a combination of Polanyi potential theory, molecular connectivity indices (MCIs) and molecular characteristics. Polanyi theory provided the basic mathematical form for the correlation. MCIs, hydrophobicity and H-bond count were used to normalize the Polanyi equation based on the molecular structure and interactions among the chemicals, the adsorbents, and the solution. In total, adsorption data were collected from 158 reports for 55 PPCPs/EDCs onto 306 different adsorbent materials. The correlation was first trained with 46 PPCPs/EDCs adsorbed onto 162 carbonaceous adsorbents (CAs), with 44.8% SDEV. Then the model was employed to predict 46 PPCPs/EDCs onto 118 other CAs and 9 new PPCPs/EDCs onto 23 CAs in ultrapure water, with error from 42 to 48% SDEV. When applying to non-carbonaceous adsorbents, the models can still predict the adsorption of PPCPs/EDCs with 90.09% SDEV. For the first time, a model, the PD – MCI – hydrophobic – H bond model, was developed to predict adsorption of a wide group of complicated PPCPs/EDCs onto a big variety of carbonaceous and non-carbonaceous adsorbents. The proposed model approach may provide a simple means for predicting adsorption capacities of PPCPs/EDCs onto various adsorbents.

Highly efficient adsorption of benzothiophene from model fuel on a metal-organic framework modified with dodeca-tungstophosphoric acid

Reduction of sulfur-containing compounds from fossil fuel is vital. This work reports on the adsorptive removal of benzothiophene (BT) from liquid fuel using a highly porous metal-organic framework based on a bicomponent zirconium(IV) benzene-tricarboxylate Zr(BTC), and its post-synthetically modified hybrid form with dodeca-tungstophosphoric acid (HPW/Zr(BTC). The HPW loading for the high-performing hybrid adsorbents was 0.5–2.0 wt/wt of W/Zr. Temperature and concentration dependent BT adsorption data were in good agreement with the non-linear Langmuir model combined with a van’t Hoff description of the heat of adsorption. The maximum predicted adsorption capacities were estimated to be 290 and 238 mg.g−1 for HPW(1.5)/Zr(BTC) and Zr(BTC) comparable to the contemporary reported adsorbents. The adsorption kinetics followed a pseudo-second order model, which was related to the low concentration of BT used for these studies. Although the highly porous Zr(BTC) had good adsorptive sites, its adsorption capacity was enhanced by incorporation of HPW, this was attributed to availability of added acid sites for adherence of basic BT. The adsorption findings were further corroborated with DFT calculations, which showed favorable BT adsorption with calculated binding energies of −140.2 and −47.3 kJ.mol−1 for HPW(1.5)/Zr(BTC) and Zr(BTC), respectively. Much of adsorption sites were retained after regeneration of adsorbents.

An exploratory study using QICAR models for prediction of adsorption capacity of multi-walled carbon nanotubes for heavy metal ions

ABSTRACTThe Quantitative Ion Character–Activity Relationship (QICAR) method was used for correlating metal ionic characteristics with the maximum adsorption capacity (qmax) of multi-walled carbon for heavy metals. The experimental values of qmax for 25 heavy metal ions, estimated by the Langmuir isotherm model, were used to construct a QICAR model. The genetic algorithm, enhanced replacement method and successive projection algorithm procedures were applied as variable selection algorithms to choose the optimal subsets of descriptors. The selected variables were correlated with qmax values by using partial least squares (PLS) regression. Orthogonal signal correction was applied as a pre-processing technique. Among of different variable selection methods, the enhanced replacement method displayed noticeable statistical parameters of the final model. The results of the enhancement replacement method–orthogonal correction signal–PLS model, with RMSEC = 0.733, r2c = 0.999 and r2p = 0.946, were excellent and dramatically better than those of other models. The developed QICAR model satisfied the internal and external validation criteria. The importance of electronegativity, ionic radius and atomic number of the heavy metal ions indicated the impact of the tendency to accept electrons and the size of ions in adsorption on carbon nanotubes.

The binary gas sorption in the bituminous coal of the Huaibei Coalfield in China

Knowledge of the gas sorption characteristics of a coal not only helps to explain the mechanism of enhanced coalbed methane recovery but also provides an important basis for simultaneous coal and gas extraction. In consequence, the pure and binary gas excess sorption capacity of methane, carbon dioxide, and nitrogen of bituminous coal samples derived from the Xutuan Coal Mine in Huaibei coalfield, in Anhui Province in China, was measured using the volumetric method. The fitting analysis of the pure gas Langmuir adsorption model was carried out. The binary gas excess sorption measurement showed that the final sorption capacity of bituminous samples was the same no matter what the gas adsorption order of competitive adsorption and displacement adsorption. Hence, coal gas adsorption is physical adsorption, i.e. the different adsorption and desorption process of gas molecules does not affect the final adsorption amount of coal to each component of gas. Using the fitting parameters obtained by the Langmuir equation, the extended Langmuir equation was used to predict the adsorption capacity for each component of the binary gas. The comparison between predicted adsorption capacity and measured adsorption capacity showed that the extended Langmuir equation can better describe the trend of the adsorption isotherm curves of a binary gas under different pressures. The separation coefficient and displacement coefficient were defined from Langmuir adsorption theory. The separation coefficient involves the proportion of each component in the free phase and the proportion of each component in the adsorption phase. The displacement coefficient involves the displacement ability of gas molecules at adsorption sites by free gas molecules.

Influence of coal composition and maturity on methane storage capacity of coals of Raniganj Coalfield, India

Methane generation primarily depends on the quantity, type and structure of kerogens and is controlled by temperature to which the precursor material is subjected over the geologic time whereas the gas-in-place depends on the storage capacity and burial history. The Raniganj Coalfield of India hosts significant reserves of good quality coals of Permian age belonging to two formations, Barakar and Raniganj. The adsorption characteristics of the nine regional (R-I to R-IX) and three local seams from Raniganj Formation of south-western part of the coalfield is thoroughly investigated in the light of reflectance, petrography and chemical composition for their CBM potential. Ash (A), moisture (M), volatile matter (VM) and fixed carbon (FC) of these coals range within 12.8–37.3%, 1.1–4.0%, 31.7–51.1% (daf) and 48.9–68.3% (daf), respectively. Carbon (C) varies within the range 77.0–86.8% (dmmf) and reflectance (Rr) within 0.62–0.87%. The coals are high volatile gas prone orthohydrous bituminous type. Both C and Rr increase, and M and VM decrease with depth. Vitrinite ranges mostly within 64.5–85.5%, inertinite 4.8–24.1% and liptinite below 15%, on mineral matter free basis. Most of the samples fall in Type-III kerogen and few are transitional to Type-II and Type-III.Gas content of the samples lies between 3.08 and 14.96 cc/g, on daf basis and generally increases with depth. Langmuir volume (VL) for the coals varies between 17.4 and 29.8 cc/g (daf) and exhibits a moderately positive linear relationship with increase in depth (R2 = 0.58). Reflectance shows a strong linear positive relationship (R2 = 0.70) with Langmuir volume indicating the development of additional micropores with rank enhancement, thereby providing more surface area for adsorption. Gas content results are encouraging though the seams are considerably undersaturated. A very good correlation is observed for the combined effect of ash, moisture and carbon on volume adsorbed for the studied coals and can be used to predict adsorption capacity from compositional data.

Preparation and characterization of γ-Fe2O3 nanoparticles and investigation of its adsorption performance for sulfide, sulfite and thiosulfate from aqueous solutions using ultrasonic assisted method: Modeling and optimization

Maghemite nanoparticles, as an adsorbent, was used for the removal of sulfur species including sulfide, sulfite and thiosulfate from waste water samples by ultrasonic-assisted adsorption method. The characterization of the prepared nanoparticles was carried out by Fourier transform infrared spectroscopy, scanning electron microscopy, X-ray diffraction and BET technique. The nanoparticles well dispersed in the water. The adsorbent was easily separated magnetically from the solution after loading with adsorbate. According to central composite design, the best experimental conditions including initial pH, the dosage of adsorbent and sonication time were obtained for sulfide, sulfite and thiosulfate. After optimization of the parameters, the removal of analytes in these conditions lead to the highest analytes removal efficiency (above 98%). The adsorption capacity was evaluated using different adsorption isotherm models. The maximum predicted adsorption capacities for sulfide, sulfite and thiosulfate were obtained as 148.5, 122.5 and 119.6mgg−1, respectively. Then, desorption process of the adsorbed thiosulfate was also investigated using sodium hydroxide solution as the solvent and the other conditions affect to desorption were optimized.

Prediction of Adsorption Equilibrium of VOCs onto Hyper-Cross-Linked Polymeric Resin at Environmentally Relevant Temperatures and Concentrations Using Inverse Gas Chromatography.

Hyper-cross-linked polymeric resin (HPR) represents a class of predominantly microporous adsorbents and has good adsorption performance toward VOCs. However, adsorption equilibrium of VOCs onto HPR are limited. In this research, a novel method for predicting adsorption capacities of VOCs on HPR at environmentally relevant temperatures and concentrations using inverse gas chromatography data was proposed. Adsorption equilibrium of six VOCs (n-pentane, n-hexane, dichloromethane, acetone, benzene, 1, 2-dichloroethane) onto HPR in the temperature range of 403-443 K were measured by inverse gas chromatography (IGC). Adsorption capacities at environmentally relevant temperatures (293-328 K) and concentrations (P/Ps = 0.1-0.7) were predicted using Dubinin-Radushkevich (DR) equation based on Polany's theory. Taking consideration of the swelling properties of HPR, the volume swelling ratio (r) was introduced and r·Vmicro was used instead of Vmicro determined by N2 adsorption data at 77 K as the parameter q0 (limiting micropore volume) of the DR equation. The results showed that the adsorption capacities of VOCs at environmentally relevant temperatures and concentrations can be predicted effectively using IGC data, the root-mean-square errors between the predicted and experimental data was below 9.63%. The results are meaningful because they allow accurate prediction of adsorption capacities of adsorbents more quickly and conveniently using IGC data.

Application of Full Factorial Design for Removal of Polycyclic Aromatic Dye from Aqueous Solution Using 4A Zeolite: Adsorption Isotherms, Thermodynamic and Kinetic Studies

ABSTRACTIn this study, removal of the cationic dye acridine orange (AO) from aqueous solution using 4A zeolite was studied. The adsorption experiments were performed using batch system, and full factorial design was employed for investigating the condition of removal efficiency of dye. The four most important operating variables were the initial pH of the solution, the concentration of dye, the contact time, and the temperature. The 18 experiments were required to investigate the effect of variables on removal of the dye. The results were statistically analyzed to define important experimental variables and their levels using the analysis of variance (ANOVA). A regression model that considers the significant main and interaction effects was suggested and fitted the experimental data very well. Model predictions were found to be in good agreement (R2 = 99.99%, adjusted R2 = 99.86%) with experimental data. The optimized conditions for dye removal were at initial pH 3.0, 20.0 mg L−1 dye, temperature 298.0 K and 80.0 min adsorption time. The experimental data were analyzed by the Langmuir, Freundlich, Temkin and Sips adsorption models. The maximum predicted adsorption capacities for AO was obtained as 29.851 mg g−1. The adsorption thermodynamic parameters, namely ΔH°ads, ΔG°ads and ΔS°ads, were determined. Furthermore, the kinetic of AO adsorption on the 4A zeolite was analyzed using pseudo-first- and second-order kinetic models and the results showed that the removal was mainly a pseudo-second-order process.

Impounding of ortho-Chlorophenol by Zeolitic Materials Adapted from Bagasse Fly Ash: Four Factor Three Level Box-Behnken Design Modelling and Optimization

The present study enlightens the impounding of ortho-chlorophenol (OCP) onto zeolitic composites derived from agricultural waste Bagasse Fly Ash (BFA). The OCP impounding was enhanced by modifying native BFA by electrolyte supported microwave hydrothermal treatment (MZBFA) and magnetic modification (MMZBFA). The synthesized sorbents were characterized by instrumental techniques (XRF, PXRD, SEM and FTIR). The adsorption process was optimized under four different process variables like: pH (4–7), agitation time (30–120 min), initial sorbate concentration (\({50-150\,{\rm mg}\,{\rm L}^{-1}}\)), sorbent dosage (\({1-4\,{\rm g}\,{\rm L}^{-1}}\)) based on Box-Behnken design with response surface methodology. The highest predicted adsorption capacities at pH 7 with \({1\,{\rm g}\,{\rm L}^{-1}}\) sorbent dose for MZBFA and MMZBFA were found to be 29.95 and \({31.94\,{\rm mg}\,{\rm g}^{-1}}\) for agitation time of 75 and 120 min and initial sorbate concentration 150 and \({100\,{\rm mg}\,{\rm g}^{-1}}\) , respectively, which were approximated with laboratory results. The isotherm data and kinetic data were best described by Langmuir model and pseudo-second-order model, respectively. The sorbents are stable after three cyclic runs when regenerated with 0.5M NaOH solution.

Abu Zenima synthetic zeolite for removing iron and manganese from Assiut governorate groundwater, Egypt

Groundwater in Upper Egypt especially in Assiut Governorate is considered the second source of fresh water and used for drinking, agriculture, domestic and industrial purposes. Unfortunately, it is characterized by high concentrations of iron and manganese ions. The study aimed at synthesizing zeolite-4A from kaolinite for removing the excess iron and manganese ions from Assiut Governorate groundwater wells. Therefor, the kaolinite was hydrothermally treated through the metakaolinization and zeolitization processes to produce crystalline zeolite-4A. The chemical composition of crystalline zeolite-4A and its morphology were then characterized using X-ray diffraction (XRD) and scanning electron microscopy (SEM). Then the column experiments were conducted to study the performance of crystalline salt-4A as ion exchange and investigate their operating parameters and regeneration conditions. Thomas and Yoon-Nelson models were applied to predict adsorption capacity and the time required for 50 % breakthrough curves. The effects of initial concentrations of 600 and 1000 mg L−1 for Fe2+ and Mn2+, feed flow rate of 10–30 ml/min, and height range of 0.4–1.5 cm on the breakthrough behavior of the adsorption system were determined. The obtained results indicated that the synthesized zeolite-A4 can remove iron and manganese ions from groundwater to the permissible limit according to the standards drinking water law.

Synthesis of graphene oxide-poly(2-hydroxyethyl methacrylate) composite by dispersion polymerization in supercritical CO2: adsorption behavior for the removal of organic dye

The use of a biocompatible and thermoresponsive polymer, poly(2-hydroxyethyl methacrylate) (PHEMA) grafted onto the surface of graphene oxide (GO) as an adsorbent for the removal of a cationic dye (methylene blue [MB]) from an aqueous solution is examined in this work. GO–PHEMA forms a hydrogel in water thus overcoming the problem faced by carbon-based adsorbent materials during post-treatment (i.e., separation of adsorbent from the aqueous phase). The GO–PHEMA composite was synthesized using a green approach through dispersion polymerization in supercritical CO2. The successful preparation of this composite was confirmed by a series of characterization techniques. The adsorption behavior of the composite toward MB such as the effect of the adsorbent dosage, pH, contact time, dye concentration, and recyclability were observed. In addition, the adsorption isotherm, kinetics and thermodynamics were investigated. According to the experimental data, the adsorption parameters were found to fit well into the Freundlich adsorption isotherm with a correlation coefficient of 0.975 and a maximum predicted adsorption capacity of 39.41 mg g−1 at 25 °C. The adsorption kinetics studies showed that the adsorption behavior followed a pseudo-second-order reaction. On the other hand, the thermodynamics studies showed that the adsorption of MB on GO–PHEMA composite followed spontaneous and endothermic adsorption process with an efficient adsorption temperature at 45 °C. The experimental results also showed that the GO–PHEMA composite could remove 99.8% of the dye in 45 min. Therefore, GO–PHEMA composite is a favorable green adsorbent for environmental applications.