Please use this identifier to cite or link to this item: http://hdl.handle.net/10266/2979
Title: Treatment of Dye Wastewater by Electro-Oxidation Process
Authors: Parminder, Kaur
Supervisor: Vikas Kumar, Sangal
Kushwaha, Jai Prakash
Keywords: Dye;Removal;Electro-oxidation
Issue Date: 19-Aug-2014
Abstract: Large amount of high strength colored wastewater is produced during dyeing/printing and finishing operations of textile processing. CBSOL LE dyes are reactive dyes developed for wool dyeing. Many physico-chemical treatment techniques like chemical coagulation, adsorption processes and membrane filtration are not preferred generally, for the treatment of such type of dye containing effluents due to costly chemical coagulants, adsorbents, membranes fouling and production of large volume of secondary pollutants. Whereas, non-biodegradability of reactive dyes and high energy requirement, limits the use of biological methods for the treatment of dye containing wastewater. These shortcomings i.e. large volume of secondary pollutants generation and non-biodegradability of dye bearing wastewater can be overcome by the use of electro-oxidation (EO) process. During the EO process, dye is first degraded to some intermediates, and these intermediates are further oxidized to carbon dioxide and water by various chlorine species (HOCl and ClO─). In the present study, EO of CBSOL LE red wool dye containing wastewater was studied with RuO2 coated Ti electrode (Ti/RuO2). Effect of EO processes parameters like pH, current (i) and time (t) on % dye removal (%DR), % color removal (% CR) and energy consumed (EC) was investigated. Also, Artificial neural networks (ANNs) and response surface methodolgy (RSM) were used for modeling and optimization of EO process. The modeling of such EO system having three process variables (pH, i and t) and three response (%DR, % CR and EC) is quite complex. Obsiviously, such problem cannot be solved by simple linear multivariate correlation. Since, Modeling based on ANNs does not require the mathematical description of the phenomena involved in the process. Therefore, ANNs was used for modeling of EO process. Hyperbolic tangent ‗TANSIG‘ being a sigmoid transfer function was chosen for the input to hidden layer mapping while a purely linear transfer function ‗PURELIN‘ was chosen for the hidden layer to the output layer mapping and the final selected network architecture was trained for 2000 iterations. Number of hidden layer neurons in the ANN architecture was optimized with the reduction in the mean square error. it was found that the optimized neurons for this process was eight. The training versus target gives regression coefficient of 0.995 along with validation, test and all data sets regression coefficient value 0.992, 0.996, 0.995 respectively which implied that training of the ANN model was done accurately and model was ready to stimulate the outputs from a given inputs. The Stimulated data obtained from AAN modeling was used for the optimization of the process. the optimization was done by central composite design (CCD) under RSM.Three operation parameters variables current (I) 0.25-1.25A, electrolysis time (t) 10-90 min and pH 4-10 was considered as input parameters and %age of color removal, %age of dye degradation and specific electrical energy consumption was taken as responses of the system. Multi-response processes optimization by desirability function approach was used to optimize the EO process which maximize the Y1 and Y2 simultaneously and minimize Y3. The optimum values of operational parameters were found to be I (0.25 A), t (90min) and pH (4.00). At optimum condition, the Y1 (% dye degradation), Y2 (% color removal) and Y3 energy consumed were found to be 91.75%, 99.00%, 2.327 Wh respectively was found. Optimum condition for EO process was experimentally verified. Optimization by CCD under response surface methodology (RSM) vividly underscores interactions between variables and their effects for the degradation of CBSOL LE red wool dye by EO process and the predictions agree well with the experimental results.
Description: MT, SEE
URI: http://hdl.handle.net/10266/2979
Appears in Collections:Electronic Theses & Dissertations @ TIET
Electronic Theses & Dissertations @ TIET

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