فهرست مطالب

Advances in Environmental Technology - Volume:2 Issue: 3, Summer 2016

Advances in Environmental Technology
Volume:2 Issue: 3, Summer 2016

  • تاریخ انتشار: 1395/07/12
  • تعداد عناوین: 6
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  • Vahide Elhami *, Afzal Karimi Pages 111-117
    Titanium dioxide (TiO2) and Fe3O4 magnetite particles were coated on spherical Kissirises; glucose oxidase (GOx) enzyme was immobilized on Kissiris/Fe3O4/TiO2 by physical adsorption. This catalyst was analyzed by a scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and energy dispersive X-ray (EDX) measurements. The performance of the prepared biocatalyst in the decolorization of Malachite Green dye was investigated. The optimal operation parameters were 20 mg/L, 20 mM, 5.5 and 40 ̊C for initial dye concentration, initial glucose concentration, pH and temperature, respectively. Under these conditions, a 95% Malachite Green decolorization efficiency was obtained after 150 min of reaction by using 1 g of prepared heterogeneous bio-Fenton catalyst. In this process, in contrast to a conventional Fenton’s reaction, external hydrogen peroxide and ferrous ion sources were not used. The effect of various reaction parameters such as initial concentration of dye, amount of catalyst, concentration of glucose, pH value and temperature on MG decolorization efficiency was studied.
    Keywords: Decolorization, Glucose oxidase, Kissiris, Heterogeneous Bio-Fenton
  • Mohsen Mansouri * Pages 119-126
    The objective of this study was to investigate the growth rate of Chlorella vulgaris for CO2 biofixation and biomass production. Six mathematical growth models (Logistic, Gompertz, modified Gompertz, Baranyi, Morgan and Richards) were used to evaluate the biomass productivity in continuous processes and to predict the following parameters of cell growth: lag phase duration (λ), maximum specific growth rate (μmax), and maximum cell concentration (Xmax). The low root-mean-square error (RMSE) and high regression coefficients (R2) indicated that the models employed were well fitted to the experiment data and it could be regarded as enough to describe biomass production. Using statistical and physiological significance criteria, the Baranyi model was considered the most appropriate for quantifying biomass growth. The biological variables of this model are as follows: μmax=0.0309 h−1, λ=100 h, and Xmax=1.82 g/L.
    Keywords: Chlorella vulgaris, CO2, Photobioreactor, Predictive modeling
  • Hossein Lotfi, Mohsen Nademi, Mohsen Mansouri *, Mohammad Ebrahim Olya Pages 127-135
    Since groundwaters are a major source of drinking water, their pollution with organic contaminants such as methyl tertiary-butyl ether (MTBE) is a very significant issue. Hence, this research investigated the photocatalytic degradation of MTBE in an aqueous solution of TiO2-ZnO-CoO nanoparticle under UV irradiation. In order to optimize photocatalytic degradation, response surface methodology was applied to assess the effects of experimental variables such as catalyst loading, initial concentration of MTBE and pH on the dye removal efficiency. The optimal condition to achieve the best degradation for the initial concentration of 30.58 mg/L of MTBE was found at a pH of 7.68 and a catalyst concentration of 1.68 g/L after 60 min.
    Keywords: Photocatalytic degradation, MTBE, TiO2-ZnO-CoO nanoparticles, Response surface method
  • Zohreh Alimohamadi, Habibollah Younesi *, Nader Bahramifar Pages 137-141
    This study investigated the effect of temperature, different concentrations of sodium carbonate,and the dose of organic solvent on the desorption of Reactive Red 198 dye from dye-saturated activated carbon using batch and continuous systems. The results of the batch desorption test showed 60% acetone in water as the optimum amount. However, when the concentration of sodium carbonate was raised, the dye desorption percentage increased from 26% to 42% due to economic considerations; 15 mg/L of sodium carbonate was selected to continue the processof desorption. Increasing the desorption temperature can improve the dye desorption efficiency.According to the column test results, dye desorption concentration decreased gradually with the passing of time. The column test results showed that desorption efficiency and the percentage of dye adsorbed decreased; however, it seemed to stabilize after three repeated adsorption/desorption cycles. The repeated adsorption–desorption column tests (3 cycles) showed that the activated carbon which was prepared from walnut shell was a suitable and economical adsorbent for dye removal.
    Keywords: Dye, Activated carbon, Desorption, Batch system, Continuous system
  • Mohsen Mehdipour Ghazi *, Mohammad Ilbeigi, Mansour Jahangiri Pages 143-151
    This study investigated the photo-degradation of methyl orange (MO) as a type of azo dye using a CuO/α-Fe2O3 nanocomposite. A CuO/α-Fe2O3 powder with a crystalline size in the range of 27-49 nm was successfully prepared using simple co-precipitation along with a sonication method. The characterization of the synthesized sample was done via XRD, FE-SEM, EDS, FTIR and DRS analyses. The Tauc equation revealed that the band gap of the nano composite in the direct mood was 2.05 ev, which is in the visible light range. The effect of operating factors containing dye concentration, photocatalyst dosage and pH on dye degradation efficiency was measured. Response Surface Method (RSM) was employed to specify the parameter effects. The photocatalytic activity of the CuO/α-Fe2O3 nanocomposite was evaluated by degradation of MO under visible light irradiation. The results showed that the pH value played a very effective role in the dye degradation process efficiency. Also, the photocatalytic degradation of MO obtained was equal to 88.47% in the optimal values.
    Keywords: Photodegradation, CuO-?-Fe2O3, Nano composite, Methyl orange, Respond surface method
  • Ali Haghighi Asl *, Amin Ahmadpour, Narges Fallah Pages 153-168
    In this study, the photocatalytic method was used for treating the spent caustic in the wastewater of Olefin units used in petrochemical industries which contain large amounts of total dissolved solids (TDS). By using the synthetic photocatalyst of suspended titanium dioxide and measuring the chemical oxygen demand (COD) which was reduced in the photocatalyst (lbc) process, the values of COD were modeled and evaluated by means of the Box-Behnken (BBD) and the artificial neural network (ANN) using experimental tests in a double-cylindrical-shell photo reactor. According to the applied calculations, it was found that the artificial neural network was a more suitable method than the experimental design in modeling and forecasting the amount of COD removal. The modeling employed in this research showed that increasing the concentration of the photocatalyst in a state of neutral pH enhanced the COD removal up to the optimal amount of 1.31 g/L without restrictions and 2 g/L with restrictions at the rate of 81% and 79%, respectively. In addition, the study of the parameter effects including oxidizer amount, aeration rate, pH, and the amount of loaded catalyst indicated that all factors except pH had a positive effect on the model; furthermore, if the interactions were neglected, the COD removal efficiency would increase by increasing each of these factors (except pH). In addition, there was no interaction between the aeration and the concentration of the photocatalyst, and the acidic pH was more suitable at low concentrations of the photocatalyst. Besides that, by increasing the pH, the efficiency of removal was reduced when the oxidant was at its low level. The results showed that photolysis and adsorption adoptions had a very small effect on the efficiency of the removal of COD compared to the photocatalyst adoptions, and it was insignificant. In addition, the photocatalytic method had an acceptable capacity for removing the phenol in the wastewater sample, whereas it was inefficient in reducing the sulfide solution in the wastewater.
    Keywords: Photocatalytic wastewater treatment, Spent Caustic wastewater, Titanium dioxide, Artificial neural networks(ANN), Design of experiment (DOE)