Removal of organic dyes by UV/H2O2 process: modelling and optimization.

The effects of different operational parameters on the decolorization of a dye solution containing C.I. Acid Blue 92 (AB92) or C.I. Acid Black 1 (AB1) by the UV/H2O2 process were optimized using response surface methodology (RSM). The reaction time, dye and H2O2 initial concentrations and distance of the UV lamp from the solution were chosen as input variables. The removal process was performed according to a central composite design. Predicted results by the proposed models were in good agreement with experimental values (R2 = 0.942 and 0.957 for AB92 and AB1, respectively). The optimum points were located by graphical response surfaces and contour plots. The removal process of the dyes was compared and the efficiency difference justified by considering the chemical structure of the dyes. Additionally, the electrical energy consumption and the related treatment costs were estimated employing the figure-of-merit electrical energy per order (E(EO)).

Investigation of the solution initial pH effects on the performance of UV/Fe-ZSM5/H(2)O(2) process.

Effect of the solution initial pH was investigated on the performance of photo-Fenton process using Fe-ZSM5 zeolite as a heterogeneous catalyst for degradation of azo dye Acid Red 14. All experiments were performed in a 1.0 L tubular closed circulation batch photo-reactor. The dye degradation quantum yield was maximal when the solution initial pH was about 5. An amount of about 0.3 mg L(-1) of Fe ions leached out of the zeolite. However, monitoring Fe ion concentration during the experiments showed that the efficiency of the process is not due to the leached Fe ions. The effect of pH on decomposition rate of hydrogen peroxide in different systems such as H(2)O(2) alone, UV/H(2)O(2), UV/Zeolite and UV/Zeolite/H(2)O(2) was explored in detail to explain the results obtained.

Decolorization of dye solution containing Acid Red 14 by electrocoagulation with a comparative investigation of different electrode connections.

This study was performed to investigate the variables that influence the efficiency of decolorization of a solution containing an azo dye (Acid Red 14) by electrocoagulation (EC) in order to compare the efficiency of different electrode connections for color removal. Current density, time of electrolysis, interelectrode distance, and pH of the solution were the variables that most influenced color removal. Initially, a simple electrochemical cell was prepared with an anode and a cathode. Then the effect of each variable was studied separately using synthetic wastewater in a batch mode. The efficiency of the method tested was determined by measurement of color removal and reduction of Chemical Oxygen Demand (COD). For dye solutions with COD of approximately 30 ppm and dye concentrations less than 150 ppm, high color removal (93%) was obtained when the pH ranged from 6 to 9, time of electrolysis was approximately 4 min, current density was approximately 80 A/m2, the temperature was approximately 300 K, and interelectrode distance was 1 cm. During the EC process under these conditions, the COD decreased by more than 85%. In the second series of experiment, the efficiency of EC cells with monopolar electrodes in series and parallel connections and an EC cell with bipolar electrodes were compared with results using a simple electrochemical cell. The experimental results showed that an EC cell with several electrodes was more effective than a simple electrochemical cell in color removal. The results also showed that an EC cell with monopolar electrodes had a higher color removal efficiency than an EC cell with bipolar electrodes. Furthermore, within an EC cell, the series connection of the monopolar electrodes was more effective for the treatment process than the parallel connection in color removal.

Modeling and optimization of heterogeneous photo-Fenton process with response surface methodology and artificial neural networks.

In this study, estimation capacities of response surface methodology (RSM) and artificial neural network (ANN) in a heterogeneous photo-Fenton process were investigated. The zeolite Fe-ZSM5 was used as heterogeneous catalyst of the process for degradation of C.I. Acid Red 14 azo dye. The efficiency of the process was studied as a function of four independent variables, concentration of the catalyst, molar ratio of initial concentration of H2O2 to that of the dye (H value), initial concentration of the dye and initial pH of the solution. First, a central composite design (CCD) and response surface methodology were used to evaluate simple and combined effects of these parameters and to optimize process efficiency. Satisfactory prediction second-order regression was derived by RSM. Then, the independent parameters were fed as inputs to an artificial neural network while the output of the network was the degradation efficiency of the process. The multilayer feed-forward networks were trained by the sets of input-output patterns using a backpropagation algorithm. Comparable results were achieved for data fitting by using ANN and RSM. In both methods, the dye mineralization process was mainly influenced by pH and the initial concentration of the dye, whereas the other factors showed lower effects.