Kinetic, thermodynamic and equilibrium studies on the removal of copper ions from aqueous solutions by natural and modified clinoptilolites

Mehdi Irannajad,Hossein Kamran Haghighi,Eshagh Safarzadeh 한국화학공학회 2016 Korean Journal of Chemical Engineering Vol.33 No.5

This paper presents the adsorption of Cu(II) ions from aqueous solution on Na and Fe-modified clinoptilolite. The copper adsorption experiments were performed in a batch system considering an optimum contact time of 24 h. Changes in the surfaces and structure were characterized by SEM data. According to the SEM results, it was anticipated that the removal efficiency of Fe-modified clinoptilolite was the highest compared with the natural and Namodified clinoptilolites. Adsorption of Cu(II) ions by modified clinoptilolites was investigated as a function of the initial Cu(II) concentration, solution pH, and temperature. According to the results, the maximum adsorbed Cu amount onto Fe-modified was 19.40mg/l at the optimum operating condition with a pH value of 5.5 and temperature of 60 oC. According to the thermodynamic evaluations, positive ΔS and negative ΔG were found for the adsorption process showing that the adsorption reaction is a spontaneous process and more favorable at high temperatures. Sorption data have been interpreted in terms of Langmuir and Freundlich, Temkin and Dubinin-Radushkevich. The adsorption equilibrium was best described by the Langmuir adsorption isotherm. In addition, according to the Sips model, the sorption of Cu(II) ions on the Fe-modified clinoptilolite was found to be heterogeneous. The kinetic study showed that the Fe-modified clinoptilolite followed the pseudo-second order model. The results indicated that the clinoptilolite-rich tuff in its iron oxide form could be efficiently used for the removal of copper from aqueous solutions.

Prediction of removal percentage and adsorption capacity of activated red mud for removal of cyanide by artificial neural network

Nazanin Deihimi,Mehdi Irannajad,Bahram Rezai 한국자원공학회 2018 Geosystem engineering Vol.21 No.5

In this study, the activated red mud was used as a new and appropriate adsorbent for the removal of ferrocyanide and ferricyanide from aqueous solution. Predicting the removal percentage and adsorption capacity of ferro-ferricyanide by activated red mud during the adsorption process is necessary which has been done by modeling and simulation. The artificial neural network (ANN) was used to develop new models for the predictions. A back propagation algorithm model was trained to develop a predictive model. The effective variables including pH, absorbent amount, absorbent type, ionic strength, stirring rate, time, adsorbate type, and adsorbate dosage were considered as inputs of the models. The correlation coefficient value (R 2) and root mean square error (RMSE) values of the testing data for the removal percentage and adsorption capacity using ANN models were 0.8560, 12.5667, 0.9329, and 10.8117, respectively. The results showed that the proposed ANN models can be used to predict the removal percentage and adsorption capacity of activated red mud for the removal of ferrocyanide and ferricyanide with reasonable error.

Comparison of microwave irradiation and oxidation roasting as pretreatment methods for modification of ilmenite physicochemical properties

Akbar Mehdilo,Mehdi Irannajad 한국공업화학회 2016 Journal of Industrial and Engineering Chemistry Vol.33 No.-

The effect of microwave irradiation and oxidation roasting (as a conventional heating method) onilmenite surface properties and its flotation behavior were compared. This comparison was performedby carrying out micro and laboratory scales flotation tests, XPS, XRD, FTIR, SEM and microprobe analyses,zeta potential and contact angle measurements. The micro scale flotation experiments indicated that theilmenite flotation recovery improves from 73.5% to almost 94% and 91% after pretreatment bymicrowave irradiation and oxidation roasting, respectively. This is in good concordance with the resultsof XPS analysis showing the increase of Fe3+ content from 48.5% to 66% after pretreatment by microwaveirradiation and 64.8% after oxidation roasting. As evidenced by FTIR analysis, zeta potential and contactangle measurements, the improvement in ilmenite floatability and hydrophobicity after pretreatment isdue to the entrance of more oleate ions into the Helmholtz layer and the increase of the formation offerric iron oleate which is more insoluble than the ferrous one. The surface properties and floatability ofquartz does not change significantly after pretreatment by both methods. In ore flotation experiments, when the grinded ore was pretreated by microwave irradiation andoxidation roasting, without significant changes in the TiO2 grade of flotation concentrate, the recovery ofTiO2 improves from 65.4 to 76.2% and 73.7%, respectively. In the case of non-grinded ore, the recovery ofTiO2 increases up to 79.8% and 70.2% after microwave irradiation and oxidation roasting, respectively. The decrease of iron content in the gangue minerals increases the improvement of TiO2 grade andrecovery after pretreatment by both methods. Using microwave irradiation as a pretreatment process,the improvement in the recovery of TiO2 and separation efficiency is more than those of oxidationroasting. In addition, microwave irradiation reduces the consumption of activation and depressantagents.

Thermodynamic and kinetic studies of heavy metal adsorption by modified nano-zeolite

Kamran Haghighi Hossein,Irannajad Mehdi,MohammadJafari Alireza 한국자원공학회 2021 Geosystem engineering Vol.24 No.2

In this study, for the first time, nano-sized clinoptilolite zeolite produced by a dry planetary ball mill in the presence of sodium hexametaphosphate was employed to remove heavy metals. Results represented that the concentration of adsorbed ions on nano-zeolite increases with increasing pH, initial concentration of metals, and temperature. The maximum adsorption efficiency for Ni2+, Cd2+, and Cu2+ was found to be 74.20%, 97.60%, and 99.50% at a pH of 7.5 and 60°C, respectively. The adsorption of Ni2+, Cd2+ and Cu2+ on nano-zeolite increased from 44.40% to 74.20 %, 76.4% to 97.60%, and 94.30% to 99.50% by enhancing temperature from 20 to 60 °C. Furthermore, Gibbs’s free energy obtained from thermodynamic evaluations depicted that adsorptions had spontaneous behavior. According to Langmuir models, the maximum capacity (qm) of Ni2+, Cu2+, and Cd2+using nano-zeolite was found to be 17.79, 17.92, and 18.32 mg/g. Adsorption isotherms showed that results fitted better on the Langmuir model for Ni2+and Cu2+ and the Freundlich model for Cd+2 because the correlation coefficients (R2) were 0.99 for them. Finally, the pseudo-second-order kinetic model was selected to interpret the experimental data.

Permeation and modeling studies on Ge(IV) facilitated transport using trioctylamine through supported liquid membrane

Hossein Kamran Haghighi,Mehdi Irannajad,Davood Moradkhani 한국화학공학회 2018 Korean Journal of Chemical Engineering Vol.35 No.1

Germanium transport from a solution containing tartaric acid by a flat sheet supported liquid membrane (FSSLM) using trioctylamine (TOA) as a carrier and polytetrafluoroethylene (PTFE) as a membrane was investigated. A mass transfer model was developed to monitor the transport process based on experimental results. The effect of parameters such as feed solution pH, TOA concentration, initial germanium concentration, and strip hydrochloric acid concentration on the germanium flux and the transport percentage were studied. A high permeation was observed at a feed solution pH of 3.00, 40%v/v TOA and 5mg/dm3 Ge4+. At HCl concentrations of 1-3mol/dm3, the germanium transport was complete. Finally, based on the mass transfer model, the aqueous and organic resistance values were 11,802 and 860.85 h/cm, respectively. The validity of the model was investigated by fitting the model and experimental data. The correlation coefficient of 0.99 showed the validity of the model.

Estimation of selectivity index and separation efficiency of copper flotation process using ANN model

Omid Salmani Nuri,Ebrahim Allahkarami,Mehdi Irannajad,Aliakbar Abdollahzadeh 한국자원공학회 2017 Geosystem engineering Vol.20 No.1

Artificial neural network was used to predict the copper ore flotation indices of Separation Efficiency (SE) and Selectivity Index (SI) within different operational conditions. The aim was to predict SECu and SIFe and SIMo as a function of chemical reagent dosages (collector, frother, modifier), feed rate, solid percentage, and the feed grade of Cu, Fe, and Mo. A three-layered back propagation neural network with the structure of 9-10-10-3 is selected and standard Bayesian regularization was used as a training function in which, it is unnecessary the validation data-set being apart from the training data-set. The advantage of this algorithm is the minimization of weights and linear combinations of squared errors of producing the appropriate network. In the training and testing stages, the quite satisfactory correlation coefficient of 1 for three training outputs and .93, .9, and .88 for testing outputs was achieved. The results show that the proposed approach models can be used to determine the most advantageous industrial conditions for the expected SE and SI in the froth flotation process.