Optimization of Preparation Conditions for Corn Cob Based Activated Carbons for the Removal of Remazol Brilliant Blue R dye

Transcription

1 International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: Optimization of Preparation Conditions for Corn Cob Based Activated Carbons for the Removal of Remazol Brilliant Blue R dye Mohd Azmier Ahmad a,*, Evelyn Tan Chai Yun a, Ismail Abustan b, Nazwin Ahmad c, Shamsul Kamal Sulaiman c a School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia b School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia c Mineral Research Centre, Jalan Sultan Azlan Shah, Ipoh, Perak, Malaysia Abstract-- This study investigates the optimal conditions for preparation of corn cob based activated carbon (CCAC) for the removal of Remazol Brilliant Blue R (RBBR) reactive dye from aqueous solution. The CCAC was prepared by using physiochemical activation method which consisted of sodium carbonate (Na 2 CO 3 ) and CO 2 gasification. Central composite design (CCD) was used to determine the effects of the three preparation variables; activation temperature, activation time and Na 2 CO 3 impregnation ratio (IR) on RBBR percentage removal and CCAC yield. Based on the CCD a quadratic model and a two-factor interaction (2FI) model were respectively developed for RBBR percentage removal and CCAC yield. The significant factors on each experimental design response were identified from the analysis of variance (ANOVA). The optimum conditions for CCAC preparation were obtained by using activation temperature of C, activation time of 3h and IR of 2.55 which resulted in 25.18% of RBBR removal and 79.04% of CCAC yield. Index Term Activated carbon, Central composite design, Corn cob, Optimization, Remazol Brilliant Blue R. I. INTRODUCTION Textile industry which is characterized by its high water consumption is one of the largest industrial producers of wastewater with high color and dissolved organic compounds. The report showed that the textile industry contributes about 22% of the total volume of the industrial wastewater generated in Malaysia [1]. The presence of very small amount of dyes in textile effluent wastewater is highly visible and undesirable [2]. The dyes are toxic and even carcinogenic and these pose a serious hazard to aquatic living organisms [3]. In recent years, many processes have been applied for the dyes treatment from wastewater including biological, physical and chemical process. Physical process particularly adsorption is one of the promising techniques for treating dyes wastewater. The most widely used adsorbent is activated carbon. However, commercially available activated carbons are expensive and greater the cost. Recently, various low-cost activated carbons derived from agricultural wastes such as palm shell [4], rattan [5] and mangosteen peel [6] have been investigate intensively for dyes removal from aqueous solution. Corn is a significant crop all around the world. The annual production worldwide is about 520x10 9 kg [7]. Asia is the second major production region. The corn cob is the waste generated during processing corn. Since the ratio between corn grain and corn cob may reach 100:18, a large quantity of corn cob was generated. It is proposed to convert corn cob into activated carbon, which is very useful to treat the dye effluent from wastewater. Currently no study has been done on optimization of the production of activated carbon from corn cob using the central composite design (CCD) approach. CCD has been found to be a useful tool to study the interactions of two or more variables and it helps to optimize the effective parameters with a minimum number of experiments. The goal of this work was to optimize the preparation conditions of activated carbon from corn cob for the removal of Remazol Brilliant Blue R (RBBR) dye. The effects of the preparation variables; activation temperature, activation time and Na 2 CO 3 impregnation ratio were studied simultaneously to obtain the optimum RBBR removal and CCAC yield. II. MATERIALS AND METHODS A. Materials Remazol Brilliant Blue R (RBBR) supplied by Sigma- Aldrich (M) Malaysia was used as an adsorbate. Deionized water was used to prepare all solutions. RBBR has a chemical formula of C 22 H 16 N 2 Na 2 O 11 S 3 with molecular weight of g/mol. The chemical structure is shown in Fig. 1. This work was supported by the Research University (RU) grant provided by Universiti Sains Malaysia. Mohd Azmier Ahmad and Ismail Abustan are the members of Waste Management Cluster of Universiti Sains Malaysia. Evelyn Tan Chai Yun is researcher working on activated carbon. Shamsul Kamal Sulaiman and Nazwin Ahmad are researchers of Mineral Research Centre, Ipoh, Perak. The correspondence author can be contacted via chazmier@eng.usm.my (M.A. Ahmad)

2 International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: Fig. 1. Chemical structure of RBBR dye. B. Preparation of CCAC Corn cob was obtained from the local market in Parit Buntar, Perak, Malaysia. Corn cob was firstly washed with water and subsequently dried at 105 C for 24 h to remove the moisture content. The dried corn cob was crushed and sieved to the size of 1-2 mm before loaded in a stainless steel vertical tubular reactor placed in a tube furnace. Carbonization step was carried out at 700 C for 2 h under purified nitrogen (99.99%) flow of 150 ml/min. The char produced was mixed with Na 2 CO 3 pellets with different impregnation ratio (IR) as calculated using following equation: IR = (1) where w KOH is dry weight of Na 2 CO 3 pallets and w char is the dry weight of char. Deionised water was added to dissolve all the Na 2 CO 3 pallets. The mixture was then dehydrated in an oven at 110 C for 24 h to remove moisture. The sample was placed in vertical tubular reactor for activation step. Once the final activation temperature was reached, the gas flow was switched from nitrogen to CO 2 at flowrate of 150 ml/min for different period of time. The sample was then cooled to room temperature under nitrogen flow. The sample was washed with hot dionized water and HCl (0.1 M) until the ph of the washed solution reached Then the sample was dried in an oven at 110 o C for 6 h. The product which is corn cob based activated carbon (CCAC) was then kept in container for further studies. C. Adsorption studies For batch adsorption studies, 0.2 g of CCAC were mixed with 200 ml aqueous dye solutions of 100 mg/l initial concentration in 20 ml Erienmeyer flask. The mixture was agitated at 120 rpm at 30 C until equilibrium was reached. The ph of the solution was natural without any ph adjustment. The concentration of RBBR dye solution was determined using a double UV-Vis spectrophometer (UV Shimadzu, Japan) at maximum wavelength of 590 nm. The percentage removal at equilibrium was calculated by the following equation: Removal (%) = (2) where C o and C e are the liquid-phase dye concentration at initial state and at equilibrium (mg/l), respectively. D. CCAC yield The CCAC yield was calculated based on the following equation: Yield (%) = (3) where w c and w o are the dry weight of final CCAC and the dry weight of char, respectively. E. Design of Experiment In this work, central composite design (CCD) was applied to study the variables for preparing the CCAC. This method can reduce the number of experiment trials needed to evaluate multiple parameters and their interactions [8]. Generally, the CCD consists of 2 n factorial runs, 2(n) axial runs and six center runs, where n is the number of variables. The CCAC was prepared using physiochemical activation method where the three variables studied were activation temperature (x 1 ), activation time (x 2 ) and Na 2 CO 3 :char IR (x 3 ). These variables together with their respective ranges were chosen based on the literature and preliminary studies as given in Table I. Three variables consist of 8 factorial points, 6 axial points and 6 replicates at the center points, indicating that altogether 20 experiments as calculated from Eq. (4): N = 2 n +2n +n c = (3) + 6 = 20 (4) where N is the total number of experiment required. T ABLE I INDEPENDENT VARIABLES AND THEIR CODED LEVELS Variables (factors) Code Coded variable levels -α α Activation temperature, o C x Activation time, h x Impregnation ratio (IR) x In this study, value was fixed at (rotatable). The experimental sequence was randomized in order to minimize the effects of the uncontrolled factor. The two responses were used to develop an empirical model which correlated the responses to the three CCAC preparation variables using a second degree polynomial equation as given by Eq. (5): ( ) (5) where Y is the predicted response, b 0 the constant coefficient, b i the linear coefficients, b ij the interaction coefficients, b ii the quadratic coefficients and x i, x j are coded values of the activated carbon preparation variables. F. Model Fitting and Statistical Analysis The experimental data were analyzed using a statistical software Design Expert software version (STAT-EASE Inc., Minneapolis, USA) for regression analysis to fit the second-degree polynomial equation and also for the evaluation of the statistical significance of the equations developed. G. Characterization of CCAC The surface area, pore volume and average pore diameter of the CCAC prepared under optimum preparation conditions were determined from nitrogen adsorption isotherm at 77 K by using Micromeritics ASAP 2020 volumetric adsorption analyzer. The surface area of the sample was determined using Brunauer-Emmett-Teller (BET) equation. The total pore volume was estimated to be the liquid nitrogen volume at a relative pressure of The surface morphology of the

3 International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: samples was examined using a scanning electron microscope (JEO, JSM-6460 LV, Japan). III. RESULT AND DISCUSSION A. Development of Regression Model Equation CCD was used to develop a polynomial regression equation in order to analyze the correlation between the CCAC preparation variables to the RBBR removal and CCAC yield. Table II shows the complete design matrixes together with both responses. Run at the center point were conducted to determine the experimental error and the reproducibility of the data. RBBR removal and CCAC yield were found to range from 8.03 to 34.30% and to 91.36%, respectively. For response of RBBR removal, the quadratic model was selected as suggested by the software. On the other hand, for CCAC yield, the two-factor interaction (2FI) model was the best model to correlate the data to the response. T ABLE II EXPERIMENTAL DESIGN MATRIX FOR CCAC PREPARATION Variables Response Run Activation temp, x 1 (⁰C) Activation time, x 2 (h) IR, x 3 RBBR Removal, Y 1 (%) CCAC Yield, Y 2 (%) The final empirical formula models for the RBBR removal (Y 1 ) and CCAC yield (Y 2 ) in terms of coded factors are represented by Eq. (6) and (7), respectively. Y 1 = x x x x x x x 1 x x 1 x 3 (6) Y 2 = x x x x 1 x x 1 x x 2 x 3 (7) The coefficient with one factor represents the effect of the particular factor, while the coefficients with two factors and those with second-order terms represent the interaction between two factors and quadratic effect, respectively. The quality of the models developed was evaluated based on the correlation coefficients, R 2 statistics which is closer to unity as it will give predicted value closer to the actual value for the responses [5]. In this experiment, the R 2 values for Eq. (6) and (7) were and , respectively. This indicated that and 57.91% of the total variation in the RBBR removal and CCAC yield respectively were attributed to the experimental variables studied. The standard deviations for the two models were 5.90 and 9.99 for Eq. (6) and (7), respectively. The R 2 value of for Eq. (6) was considered as moderate to its actual value. The R 2 of for Eq. (7) was considered relatively low, indicating that the predicted value for CCAC yield would be less accurate and further to its actual value. B. Analysis of variance The significance and adequacy of the models were further justified through analysis of variance (ANOVA). In the ANOVA, the mean squares were obtained by dividing the sum of the squares of each of the variation sources the mode and the error variance, by the respective degrees of freedom. The fishers variance ratio, F-value is the ratio of the mean square owing to regression to the mean square owing to error. The higher the F-value, the greater is the significance of the corresponding variable to cause effect. In addition if Prob.>F less than 0.05, the model terms are considered as significant [6]. The ANOVA for the quadratic model for RBBR removal of CCAC is listed in Table III. The model F-value of 3.25 and Prob.>F of implied that this model was significant. In this case, x 3 and x 2 1 factors were significant model terms whereas x 1, x 2, x 2 2, x 2 3, x 1 x 2 and x 1 x 3 were insignificant to the response. Source T ABLE III ANOVA FOR RBBR REMOVAL OF CCAC Sum of Degree of Mean F Prob > F squares freedom square value Model x x x x x x x 1x x 1x x 2x Source T ABLE IV ANOVA FOR CCAC YIELD Sum of Degree of Mean F squares freedom square value Prob > F Model x x x x 1x x 1x x 2x From the ANOVA for CCAC yield as shown in Table IV, the model F-value of 2.98 and Prob.>F of revealed that the model was also significant. In this case, x 1 x 3 and x 2 x 3 were significant model terms whereas x 1, x 2, x 3 and x 1 x 2 were insignificant to the response.

4 International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: C. RBBR removal Based on the F-value as shown in Table III, the IR was found to has significant effect compared to activation temperature and activation time on the RBBR removal. In addition, the quadratic effect of activation temperature was considered high. Fig. 2(a) shows the interaction effects between activation time and activation temperature, whereas Fig. 2(b) shows the three-dimension response surfaces which was constructed to show the interaction effects of the activation temperature and IR on the RBBR removal. activation time and IR were considered high. Fig. 3 shows the three-dimensional response surfaces which were constructed to show the effects of CCAC preparation variables on the yield. Fig. 3(a) illustrates the effect of activation time and activation temperature on the CCAC yield, whereas Fig. 3(b) illustrates the effect of IR and activation temperature on the response. Fig. 2. Three-dimensional response surface plot of RBBR removal; (a) effect of activation time and activation temperature, IR=2.25 (b) activation temperature and IR, t=2h of CCAC. As can be seen from Fig. 2(a), the RBBR removal increased with increased in activation temperature and time. The results obtained were in agreement with the work done by Sudaryanto et al. [9] which reported that both variables gave significant effect on the pore structure of activated carbon. From Fig. 2(b), the highest RBBR removal was obtained when the activation temperature and IR were at maximum within the range studied. Increasing in activation temperature and IR would develop new pores and enlarge the existing pores, which enhanced the adsorption of RBBR. D. Activated carbon yield Referring to F-value shown in Table IV, the activation temperature imposes the greatest effect on CCAC yield followed by IR and activation time. The interaction effects between the activation temperature and IR as well as Fig. 3. Three-dimensional response surface plot of CCAC yield; (a) effect of activation time and activation temperature, IR=2.25, (b) effect of IR and activation temperature, t=2h. E. Process optimization One of the main aims of this study was to find the optimum process parameters which CCAC produced should have a high carbon yield and a high RBBR removal. However, it is difficult to optimize both responses under the same condition because the interest region of factors is different. When adsorption performance increases, carbon yield will decrease and vice versa. Therefore, the function of desirability was applied using Design-Expert software version (STAT-EASE Inc., Minneapolis, USA) in order to compromise between these two responses. In the optimization analysis, the target criteria was set as maximum values for the two responses of RBBR removal (Y 1 ) and CCAC yield (Y 2 ) while the values of the three variables were set in the ranges being studied. The experiment conditions with the highest desirability were selected to be verified. The predicted and

5 International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: experimental results of RBBR removal and CCAC yield obtained at optimum conditions are listed in Table V. T ABLE V MODEL VALIDATION Act. Act. IR, RBBR removal (%) CCAC yield (%) temp., time, x 3 Err. Err. x 1 ( o C) x 2 (h) Pred. Exp. Pred. Exp. (%) (%) The optimum CCAC was obtained by using activation temperature, activation time and IR of C, 3 h and 2.55, respectively. The optimum CCAC showed RBBR removal of 25.18% and CCAC yield of 79.04%. It was observed that the experimental values obtained were in good agreement with the values predicted from the models, with relatively small errors between the predicted and the actual values, which was only 4.91% and 4.21% for RBBR removal and CCAC yield, respectively. F. Characterization of CCAC Figs. 4(a) and (b), respectively show the SEM images of the precursor and CCAC. The corn cob surface textures were rough with very little pores were presence. After activation process, almost homogeneous type pores structure were distributed on the surface of the CCAC as shown in Fig. 4(b). This result revealed that the combination activation process of Na 2 CO 3 and CO 2 were effective in creating well-developed pores, resulting to large surface area of CCAC with good porous structure. Fig. 4. Scanning electron micrographs; (a) precursor and (b) CCAC (magnification 500x). The BET surface area, total pore volume and average pore diameter of the CCAC were found to be m 2 /g, 0.31 cm 3 /g and 2.84 nm, respectively. The average pore diameter of 2.84 nm indicates that the CCAC prepared was in the mesopores region. The physiochemical activation process has contributed to the high surface area and total pore volume of the prepared CCAC. IV. CONCLUSIONS CCD was successfully used to investigate the effects of activation temperature, activation time and IR, on the percentage removal of RBBR and CCAC yield. The optimum CCAC preparation conditions were obtained using o C activation temperature, 3 h activation time and 2.55 IR resulting in 25.18% of RBBR removal and 79.04% of carbon yield. The CCAC prepared demonstrated high surface area and well-developed porosity. REFERENCES [1] B.H. Hameed, M.I. El-Khaiary, Removal of basic dye from aqueous medium using a novel agricultural waste material: Pumpkin seed hull, J. Hazard. Mater., 155, 2008, [2] I.M. Banat, P. Nigam, D. Singh, R. Marchant, Microbial decolorization of textile-dyecontaining effluents: A review. Bioresource Technol., 58, 1996, [3] G. Crini, Non-conventional low-cost adsorbents for dye removal: A review, Bioresource Technol., 97, 2006, [4] D. Adinata, W.M.A.Wan Daud, M.K. Aroua, Preparation and characterization of activated carbon from palm shell by chemical activation with K 2CO 3, Bioresource Technol., 98, 2007,

6 International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: [5] A.A. Ahmad, B.H. Hameed, A.L. Ahmad, Removal of disperse dye from aqueous solution using waste-derived activated carbon: Optimization study, J. Hazard. Mater., 170, 2009, [6] M.A. Ahmad, R. Alrozi, Optimization of preparation conditions for mangosteen peel-based activated carbons for the removal of Remazol Brilliant Blue R using response surface methodology, Chem. Eng. J., 165, 2010, [7] Q. Cao, K.C. Xie, Y.K. Lv, W.R. Bao, Process effects on activated carbon with large specific surface area from corn cob, Bioresource Technol. 97, 2006, [8] I.A.W. Tan, A.L. Ahmad, B.H. Hameed, Optimization of preparation conditions for activated carbons from coconut husk using response surface methodology, Chem. Eng. J., 137, 2008, [9] Y. Sudaryanto, S.B. Hartono, W. Irawaty, H. Hindarso, S. Ismadji, High surface area activated carbon prepared from cassava peel by chemical activation, Bioresource Technol., 97, 2006, [10] A.C. Lua, T. Yang, Effect of activation temperature on the textural and chemical properties of potassium hydroxide activated carbon prepared from pistachio-nut shell, J. Coll. Interf. Sci., 274, 2004,