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Nature is threatened by the environmental contamination caused by the wastewater produced and discharged every day. Wastes coming from the industrial and agricultural sectors contribute a large portion. One such industry is the textile industry.About 42,000 L of wastewater is discharged daily by each of the textile industries (Maravilla, 2003 as cited by Africa, 2005). Industrial wastewater from manufacturing sources contributes a devastating effect on the body of water as well as effects on individual’s health. Production of large volumes of highly colored wastewater is one problem encountered in a textile industry. It has been estimated to generate 1 to 2 million gallons per day of wastewater (Freeman, 1995). Every textile industry is unique with respect to the type of production and the technology and chemicals used in production.Thus, it is often unusual to predict the characteristics of textile wastewater by using reported values in the literature. Other factors are the different requirements of the fibers and the different quality required for the final fabric. Amount of pollutants present in textile wastewater varies according to the wastewater management practices and amount of water used in the production. The water consumption and wastewater generation from a textile industry depends upon the processing operations used during the conversion of fibers to textile fabric.Wastewater from the textile industry is characterized with high values of Biochemical Oxygen Demand (BOD), which can cause rapid depletion of dissolved oxygen; Chemical Oxygen Demand (COD); color; and pH. The dyeing section in a textile industry contributes a high level of COD, which is toxic to biological life. This high level of COD comes from the chemicals used in the operation. The high alkalinity interferes with the biological treatment process and high color makes the water unfit for use. Due to the harmful effects of the chemicals present in wastewater, intensive researches in new advanced treatment technologies are conducted.Appropriate treatment methods for wastewaters containing toxic or non-biodegradable compounds are developed. Advanced Oxidation Processes (AOP) are often employed in the treatment of textile wastewaters. Reduction of the COD and BOD levels are often done with the use of oxidants. AOP includes chemical oxidation processes using hydrogen peroxide, ozone, combined ozone and peroxide, hypochlorite, and Fenton’s reagent; ultra-violet enhanced oxidation such as UV/ozone, UV/hydrogen peroxide, and UV/air; and wet air oxidation and catalytic wet air oxidation.Fenton’s treatment is a highly competitive method, which is being used in the wastewater industry to treat a variety of industrial wastes containing a range of toxic organic compounds such as phenols, formaldehyde, BTEX, and complex wastes derived from dyestuffs, pesticides, wood preservatives, plastic additives, and rubber chemicals. The advantages of a Fenton process, being an AOP, are process operability, unattended operation, the absence of secondary wastes, and the ability to handle fluctuating flow rates and compositions.On the other hand, it requires a higher capital and operating costs. Basically, Fenton’s reagent is a solution of hydrogen peroxide and an iron catalyst that is used to oxidize contaminants or wastewater. Fenton process has a high efficiency and can even mineralize organic compounds completely to water and carbon dioxide. In synthesis, the outstanding characteristics and principal advantages of the use of peroxide in the suitable combination with ferrous sulphate are: 1) Knock down of surface-active agents; 2) Page 2 of 13Mineralization of the polluter with reduction of COD of about 40-60% and more; 3) Maintenance of the purification independently from the polluting load; 4) Chemical degradation of the polluting agent; and 5) Forecasts of the use of reagents which do not leave pollutant residues in the treated wastewater through treatment. 2. PROBLEM STATEMENT Actual wastewater was simulated using Reactive Turquoise Blue KNG dye and was subjected to analysis and treatments. The study evaluated the efficacy of Fenton’s Reagent in the color removal and COD reduction of a simulated textile wastewater (STW).It was then characterized in terms of pH, color (PCU), and COD. The effects of varying the amount of hydrogen peroxide (5, 10, 15, 20, and 25 mL), iron catalyst (1, 2, 3, 4, and 5 mL), and the concentration of the simulated textile wastewater (300, 400, 500, 600, and 700 ppm) on the COD reduction and percent color removal were determined. 3. 3. 1 METHODOLOGY EXPERIMENTAL PROCEDURE 3. 1. 1 Preparation and Initial Characterization of Simulated Textile Wastewater One liter of 6000 ppm (mg/L) simulated textile wastewater was prepared as a stock solution.Solutions of 300, 400, 500, 600, and 700 ppm were prepared from the stock solution and then kept in a dark container. The initial pH, color, and COD of the stock solution were measured using the pH meter and Hach Spectrophotometer. Characterization of the untreated STW is shown in Table 3. 1. Table 3. 1 Initial Characteristics of the Simulated Textile Wastewater Concentration (ppm) 300 400 500 600 700 pH 3. 39 2. 96 3. 12 2. 80 3. 88 Color (PCU) 48. 00 48. 33 53. 00 54. 00 64. 33 COD (ppm) 217. 67 269. 67 338. 00 495. 67 583. 33 3. 1. 2 Treatment of the Simulated Textile WastewaterThe treatment with Fenton’s reagent proceeded in three levels which include the effects of varying the amount of Hydrogen Peroxide, effects of varying the amount of iron catalyst, and the effects of varying the STW concentration. The succeeding level is dependent on its preceding level. Five glass jars were used as the reaction vessels. Twenty-five milliliters of the 300 ppm dye solution was placed in each vessel. The pH was then adjusted to 3. 2 – 3. 5 using a pH meter using 10% sulphuric acid (H2SO4). After which, 1 mL of 10% FeSO44•7H2O was added.Hydrogen peroxide of 5, 10, 15, 20, and 25 mL was then added slowly and the solution was allowed to react. The reaction was then allowed to proceed until no evident of bubble formation due to gas evolution. The Page 3 of 13 time from the hydrogen peroxide addition up to which the reaction stopped was recorded. The solution was cooled to room temperature. Then, the pH was adjusted to pH 9-10 by adding 1M NaOH to precipitate the iron. The filtrate was then used in the pH, color, and COD analysis. Finally, the best ratio was determined in accord to the allowable concentration requirements.The volume of the H2O2 of the chosen ratio was used and held constant in determining the effects of varying the amounts of the iron catalyst. Using the same procedure employed in determining the effects of varying the amounts of H2O2, Iron catalyst were added instead. Amounts of the iron catalyst were added in 1, 2, 3, 4, and 5 mL. The best ratio was determined by considering its COD reduction. The amount of the iron catalyst in this ratio was used and held constant in the determination of the effects of varying the STW concentration.For determining the effects of varying the concentration of the wastewater samples, the best ratio that was determined in the second level treatment was used following the same procedure to measure the pH, color removal, and COD reduction for the treated wastewater. The best ratio was determined by considering its COD reduction. 3. 2 METHODS OF STATISTICAL ANALYSIS Similarly, statistical analysis of the raw data proceeded in three levels as well, which acted as the factors: varying the Hydrogen Peroxide (H2O2), varying the Ferrous Sulphate Heptahydrate (FeSO44•7H2O), and varying the Simulated Textile Wastewater (STW) concentration.Each level’s interest is on the color removal and COD reduction (both are considered as response). Microsoft Excel and Minitab 14 and a few of the SAS system were used in the analysis of the data obtained. Three trials were conducted in each of the three levels where in five inputs (volume or concentration) were evaluated. There were a total of 15 observations in the experimental design. Analysis of Variance (One way ANOVA) was employed to examine the effect of each of the factor on the response (% color removal and % COD reduced).A 5% level of significance was used in the ANOVA. In addition, Duncan’s Multiple Range Test for the experimental results of the % Color and COD removed were employed. The experimental design will also make use of the Multiple Linear Regression to establish the relationship between these three factors. A 95% confidence level was used. 4. RESULTS AND DATA ANALYSES The effect of each level on the Color removal and COD reduction was analyzed using the One Way ANOVA at 5% significance level. 4. 1 VARYING HYDROGEN PEROXIDE 4. 1. 1 Dependent Variable: Color Page 4 of 13 Table 4. Effect of Varying H2O2 on the Color Removal from STW H2O2, mL 5 10 15 20 25 Average color removal (%) Trial 1 97. 92 98. 60 97. 92 98. 60 100. 00 Trial 2 98. 60 97. 92 98. 60 97. 92 100. 00 Trial 3 97. 92 98. 60 98. 60 97. 92 100. 00 Average color removal (%) 98. 14 98. 38 98. 37 98. 16 100. 00 *300 ppm simulated textile wastewater Table 4. 2 One Way ANOVA for the Effect of Varying H2O2 on the Color Removal from STW SUMMARY Groups Count Sum Average Variance H2O2= 5 3 294. 44 98. 14667 0. 15413 H2O2=10 3 295. 12 98. 37333 0. 15413 H2O2=15 3 295. 12 98. 37333 0. 15413 H2O2=20 3 294. 44 98. 4667 0. 15413 H2O2=25 3 300 100 0 ANOVA Source of Variation Between Groups Within Groups Total SS 7. 42037 1. 23306 8. 65344 df 4 10 14 MS 1. 85509 0. 12330 F 15. 04455 P-value 0. 00031 F crit 3. 47805 It can be seen in the ANOVA table (Table 4. 2) that F > Fcrit, therefore at least one group for the %Color removed at varying H2O2 levels is different at 5% level of significance. Duncan’s Multiple Range Test for Color NOTE: This test controls the type I comparison wise error test, not the experiment wise error rate. Alpha Error Degrees of Freedom Error Mean Square 0. 05 8 0. 035185Number of Means Critical Range 2 0. 3532 3 0. 3680 4 0. 3764 5 0. 3813 Page 5 of 13 Duncan Grouping A A A A B Mean 0. 8889 0. 7778 0. 7778 0. 7778 0 N 3 3 3 3 3 Ratio 20 5 15 10 25 *Means with the same letter are not significantly different 4. 1. 2 Dependent Variable: COD Table 4. 3 Effect of Varying H2O2 on the COD Reduction H2O2, mL 5 10 15 20 25 Average COD removal (%) Trial 1 89. 28 89. 59 91. 42 94. 03 96. 63 Trial 2 87. 29 89. 13 92. 65 92. 80 97. 85 Trial 3 88. 06 88. 97 92. 65 95. 25 97. 70 Average COD removal (%) 88. 21 89. 23 92. 24 94. 03 97. 40 *300 ppm simulated textile wastewaterTable 4. 4 One Way ANOVA for the Effect of Varying H2O2 on the COD Reduction SUMMARY Groups Count Sum Average Variance H2O2=5 3 264. 63 88. 21 1. 0069 H2O2=10 3 267. 69 89. 23 0. 1036 H2O2=15 3 276. 72 92. 24 0. 5043 H2O2=20 3 282. 08 94. 02667 1. 50063 H2O2=25 3 292. 18 97. 39333 0. 44263 ANOVA Source of Variation Between Groups Within Groups Total SS 165. 14407 7. 11613 172. 2602 df 4 10 14 MS 41. 28602 0. 71161 F 58. 01749 P-value 6. 97E-07 F crit 3. 47805 From the ANOVA table 4. 4, F which is equal to 58. 01749 is a lot greater than the critical value for F (3. 7805), therefore there is a significant difference in the average % COD Reduction at varying H2O2. Hydrogen Peroxide concentration is a contributing factor for the Color removal. Page 6 of 13 Duncan’s Multiple Range Test for COD NOTE: This test controls the type I comparison wise error test, not the experiment wise error rate. Alpha Error Degrees of Freedom Error Mean Square 0. 05 8 1. 739154 Number of Means Critical Range 2 2. 483 3 2. 588 4 2. 646 5 2. 681 Duncan Grouping B B A A C C Mean 17. 810 16. 342 14. 215 12. 442 N 3 3 3 3 Ratio 25 20 10 15 D 7. 05 3 5 *Means with the same letter are not significantly different 4. 2 VARYING FERROUS SULPHATE HEPTAHYDRATE Similarly, ANOVA tables 4. 6 and 4. 8 shows F values > Fcrit, which signifies that at least one group for the % Color removed and %COD reduced at varying Fe2SO4•7H2O levels is different at 5% alpha level. 4. 2. 1 Dependent Variable: Color Table 4. 5 Effect of Fe2SO4•7H2O on the Color Removal from STW Average color removal (%) Fe2SO4•7H2O, Average color mL removal (%) Trial 1 Trial 2 Trial 3 1 100. 00 100. 00 100. 00 100. 00 2 99. 31 98. 60 99. 31 99. 08 3 98. 60 99. 31 99. 31 99. 08 4 97. 2 97. 92 98. 60 98. 15 5 98. 60 97. 92 97. 92 98. 15 *300 ppm simulated textile wastewater Table 4. 6 One Way ANOVA for the Effect of Varying Fe2SO4•7H2O on the Color Removal from STW SUMMARY Groups Count Sum Average Variance Fe2SO4•7H2O=1 3 300 100 0 Fe2SO4•7H2O=2 3 297. 22 99. 07333 0. 16803 Fe2SO4•7H2O=3 3 297. 22 99. 07333 0. 16803 Fe2SO4•7H2O=4 3 294. 44 98. 14667 0. 15413 Fe2SO4•7H2O=5 3 294. 44 98. 14667 0. 15413 Page 7 of 13 ANOVA Source of Variation Between Groups Within Groups Total SS 7. 21317 1. 28866 8. 50184 df 4 10 14 MS 1. 80329 0. 12887 F 13. 99348 P-value 0. 00042 F crit 3. 47805Duncan’s Multiple Range Test for Color NOTE: This test controls the type I comparison wise error test, not the experiment wise error rate. Alpha Error Degrees of Freedom Error Mean Square Number of Means Critical Range 2 0. 3341 0. 05 8 0. 031482 3 0. 3481 4 0. 3560 5 0. 3607 Duncan Grouping A A B B C 4. 2. 2 Mean 0. 8889 0. 8889 0. 4444 0. 4444 0. 0000 N 3 3 3 3 3 Ratio 5 4 3 2 1 *Means with the same letter are not significantly different Dependent Variable: COD Table 4. 7 Effect of Fe2SO4•7H2O on the COD Reduction Fe2SO4•7H2O, mL 1 2 3 4 5 Average COD removal (%) Trial 1 96. 63 92. 96 92. 50 91. 3 93. 42 Trial 2 97. 85 94. 49 94. 34 92. 65 93. 42 Trial 3 97. 70 92. 50 92. 96 92. 19 90. 50 Average COD removal (%) 97. 40 93. 31 93. 26 92. 19 92. 45 *300 ppm simulated textile wastewater Table 4. 8 One Way ANOVA for the Effect of Varying Fe2SO4•7H2O on the COD Reduction SUMMARY Groups Fe2SO4•7H2O=1 Fe2SO4•7H2O=2 Fe2SO4•7H2O=3 Fe2SO4•7H2O=4 Fe2SO4•7H2O=5 Count 3 3 3 3 3 Sum 292. 18 279. 95 279. 8 276. 57 277. 34 Average 97. 39333 93. 31667 93. 26667 92. 19 92. 44667 Variance 0. 44263 1. 08543 0. 91693 0. 2116 2. 84213 Page 8 of 13 ANOVA Source of Variation Between Groups Within Groups Total SS 53. 714 10. 9975 64. 4689 df 4 10 14 MS 13. 36786 1. 09975 F 12. 15540 P-value 0. 00074 F crit 3. 47805 Duncan’s Multiple Range Test for COD NOTE: This test controls the type I comparison wise error test, not the experiment wise error rate. Alpha Error Degrees of Freedom Error Mean Square Number of Means Critical Range 2 3. 625 0. 05 8 3. 707414 3 3. 778 4 3. 863 5 3. 914 Duncan Grouping A A B C D Mean 25. 667 23. 444 16. 889 13. 000 5. 667 N 3 3 3 3 3 Ratio 1 2 3 4 5 *Means with the same letter are not significantly different 4. 3 VARYING SIMULATED TEXTILE WASTEWATER CONCENTRATION 4. 3. Dependent Variable: Color Average color removal (%) Trial 1 100. 00 99. 31 98. 60 98. 60 98. 60 Trial 2 100. 00 99. 31 98. 60 98. 60 98. 60 Trial 3 100. 00 98. 60 98. 60 98. 60 98. 60 Table 4. 9 Effect of Varying STW concentration on the Color Removal STW concentration, ppm 300 400 500 600 700 Average color removal (%) 100. 00 99. 08 98. 60 98. 60 98. 60 * Ratio: 1mL Fe2SO4•7H2O : 25 mL H2O2 : 25 mL simulated textile wastewater Page 9 of 13 Table 4. 10 One Way ANOVA for the Effect of Varying STW concentration on the Color Removal SUMMARY Groups Count Sum Average Variance STW conc. 300 3 300 100 0 STW conc. =400 3 297. 22 99. 07333 0. 16803 STW conc. =500 3 295. 8 98. 6 3. 02923 STW conc. =600 3 295. 8 98. 6 3. 02923 STW conc. =700 3 295. 8 98. 6 3. 02923 ANOVA Source of Variation Between Groups Within Groups Total SS 4. 44651 0. 33607 4. 78257 df 4 10 14 MS 1. 11163 0. 03361 F 33. 07756 P-value 9. 68E-06 F crit 3. 47805 Duncan’s Multiple Range Test for Color NOTE: This test controls the type I comparison wise error test, not the experiment wise error rate. Alpha Error Degrees of Freedom Error Mean Square 0. 05 8 0. 007407 Number of Means Critical Range 2 0. 1620 3 0. 1689 4 0. 727 5 0. 1750 Duncan Grouping A A A B C 4. 3. 2 Mean 0. 66667 0. 66667 0. 66667 0. 44444 0. 00000 N 3 3 3 3 3 Ratio 7 6 5 4 3 *Means with the same letter are not significantly different Dependent Variable: COD Table 4. 11 Effect of Varying STW concentration on the COD Reduction Average COD removal (%) STW concentration, ppm Trial 1 Trial 2 Trial 3 300 400 500 600 700 96. 63 93. 42 89. 89 82. 39 79. 63 97. 85 93. 42 90. 96 86. 98 82. 39 97. 70 93. 57 89. 43 85. 15 78. 26 Average COD removal (%) 97. 40 93. 47 90. 10 84. 84 80. 09 * Ratio: 1mL Fe2SO4•7H2O : 25 mL H2O2 : 25 mL simulated textile wastewaterPage 10 of 13 Table 4. 12 One Way ANOVA for the Effect of Varying STW concentration on the COD Reduction SUMMARY Groups Count Sum Average Variance STW conc. =300 3 292. 18 97. 39333 0. 44263 STW conc. =400 3 280. 41 93. 47 0. 0075 STW conc. =500 3 270. 28 90. 09333 0. 61623 STW conc. =600 3 254. 52 84. 84 5. 3391 STW conc. =700 3 240. 28 80. 09333 4. 42523 ANOVA Source of Variation Between Groups Within Groups Total SS 564. 30064 21. 6614 585. 96204 df 4 10 14 MS 141. 07516 2. 16614 F 65. 12744 P-value 4. 0146E-07 F crit 3. 478055 As can be seen from the ANOVA tables 4. 10 and 4. 2, F values > Fcrit, therefore it can be said that at least one group for the % Color removed and %COD reduced at varying STW concentration levels is different at 5% significance level. Duncan’s Multiple Range Test for COD NOTE: This test controls the type I comparison wise error test, not the experiment wise error rate. Alpha Error Degrees of Freedom Error Mean Square 0. 05 8 3. 340745 Number of Means Critical Range Duncan Grouping A A A A B 2 3. 441 3 3. 586 4 3. 667 N 3 3 3 3 3 5 3. 716 Ratio 6 7 5 4 3 Mean 17. 000 16. 444 14. 667 14. 556 5. 667 *Means with the same letter are not significantly different . SUMMARY AND CONCLUSIONS The efficacy of Fenton’s reagent in treating a simulated textile wastewater in terms of color and COD was studied. The treatment was divided into three levels which include the varying of the amount of hydrogen peroxide, amount of iron catalyst, and concentration of the simulated textile Page 11 of 13 wastewater. The effects of H2O2, iron catalyst, and simulated textile wastewater concentration on the color removal and COD reduction were studied. The simulated textile wastewater was initially characterized in terms of pH, color, and COD content.The values obtained are 2. 87, 48 PCU, and 217. 67 ppm, respectively. Though the initial color was already an acceptable value based on the DAO 35, the pH was highly acidic and the COD value is higher compared to the standard. In the first level of treatment, the H2O2 was varied at 5, 10, 15, 20, and 25 mL. The amount of Fe2SO4 •7H2O and dye solution was fixed at 1 mL and 25 mL, respectively. The amount of H2O2 that produced the highest COD reduction was used in the next level of treatment where the amount of Fe2SO4 •7H2O was varied at 1, 2, 3, 4, and 5 mL.Moreover, the amount of Fe2SO4•7H2O that produced the highest COD reduction was held constant for the last level. The STW concentration was varied at 300, 400, 500, 600, and 700 ppm wherein the ratio used was 1 mL Fe2SO4•7H2O : 25 mL H2O2 : 25 mL STW concentration, which basically produced the highest COD reduction. From the study, it was found out that increasing the amount of H2O2, increases the percent COD removal. Moreover, the highest percent COD reduction was obtained at the smallest amount of iron catalyst which is 1 mL.However, the increase in the amount of the iron catalyst returned insignificant effect on the percent COD removal of the dye wastewater. In addition, the COD removal decreased as the concentration of the dye solution increased. In terms of color removal, the results obtained at the different levels of experiment showed that the color was removed almost completely after the reaction with Fenton’s reagent. All in all, it can be said that the Fenton’s reagent was very effective in treating the simulated textile wastewater containing Reactive Turquoise Blue KNG dye. The iron-catalyzed hydrogen peroxide has been efficient in removing color and educing the chemical oxygen demand content of a textile wastewater based on the statistical analyses conducted. 6. RECOMMENDATION However, it is recommended to conduct additional experiments such as using the amount of H2O2 that produced the three highest COD reduction instead of using only one (the highest among the five volumes) as well as the three highest COD reduced using the three amounts of Fe2SO4•7H2O. In this way, interaction between the three factors using a three-way ANOVA can be obtained thereby coming up with the most effective H2O2- Fe2SO4•7H2O-STW concentration combination in terms of % COD and color removal.In addition, a multiple linear regression can also be constructed for the parameter interaction which will be a useful tool in predicting the % COD reduction specific for the textile industry. Page 12 of 13 7. REFERENCES Copper Blue 2B dye using Fenton’s process. Undergraduate Thesis. CEAT, University of the Philippines Los Banos. AFRICA, V. J. L. (2005). Color and COD removal from a simulated textile wastewater containing Direct MARAVILLA, J. T. (2003). Adsorption of Basic Auramine Orange dye from synthetic textile mill effluent using char and activated carbon from sugarcane bagasse. Undergraduate Thesis.CEAT, University of the Philippines Los Banos. FREEMAN, H. M. (1995). Industrial Pollution Prevention Handbook. Mc-GrawHill, Inc. 829-843 p. HALL, A. J. (1965). The Standard Handbook of Textiles. New York: Chemical Publishing Co. , Inc. ABRAHART, E. N. (1968). Dyes and their Intermediates. London: Pergamon Press Ltd. BIGDA, R. J. (1995). Consider Fenton’s chemistry for wastewater treatment. Chemical Engineering Progress. 62-66. EATON, A. D. , L. S. CLESCERI and A. E. GREENBERG. ed. (1995). Standard Methods for the Examination of Water and Wastewater. Baltimore, Maryland: United Book Press, Inc.HOLLEN, N. et al. (1988). Textiles. New York: Macmillan Publishing Company. 336-340 p. KUO, W. G. (1992). Decolorizing dye wastewater with Fenton’s reagent. Water Research. 26(7): 881886. SNOWDEN-SWAN. (1995). Pollution Prevention in the Textile Industries. Industrial Pollution Prevention Handbook. New York: Mc-Graw-Hill, Inc. SUMALAPAO, E. P. (2005). Color removal and Chemical Oxygen Demand (COD) reduction from simulated textile wastewater containing Basic Methylene Blue using Fenton’s Reagent. Undergraduate Thesis. CEAT, University of the Philippines Los Banos. Page 13 of 13

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