Optimization of durian peel based activated carbon preparation conditions for ammoniacal nitrogen removal from semi-aerobic landfill leachate

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1 554 Journal of Scientific & Industrial Research J SCI IND RES VOL 70 JULY 0 Vol. 70, July 0, pp Optimization of durian peel based activated carbon preparation conditions for ammoniacal nitrogen removal from semi-aerobic landfill leachate Mohamad Anuar Kamaruddin, Mohd Suffian Yusoff * and Mohd Azmier Ahmad School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, 4300 Nibong Tebal, Penang, Malaysia School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia, 4300 Nibong Tebal, Penang, Malaysia Received March 0; revised 9 May 0; accepted 0 June 0 This study presents optimal conditions for preparation of durian peel based activated carbon (DPAC) for removal of ammoniacal nitrogen ( -N) from semi-aerobic landfill leachate. DPAC was prepared using chemical activation method which consisted of phosphoric acid (H 3 ) treatment and nitrogen gasification. Optimum conditions for DPAC preparation (activation temperature, 400 C; activation time,.7 h; and IR, 3) resulted in DPAC yield (.0%) of and -N removal (46.49%). Keywords: Ammoniacal nitrogen ( -N), Durian peel, Landfill, Leachate, Optimization Introduction Landfill leachate is a high-strength wastewater produced from percolation of rain water and moisture through wastes refuse during solid waste degradation in landfill. Landfill leachate characteristics are varies and greatly depending on landfill age, type of waste, site hydrology, landfill type and landfill operation. Landfill leachate contains high concentration of pollutants [COD, BOD and ammoniacal nitrogen ( -N)]. If not properly treated and safely disposed, landfill leachate could be a potential source of surface and groundwater contamination as it may percolate through soils and subsoils 3. Adsorption is one among treatment methods in leachate treatment system 4. Activated carbon (AC) adsorption has emerged as the most prominent option in leachate treatment considering its superior performance in terms of removal efficiency and high adsorption capacity that significantly reduce pollutants level in leachate. In recent years, focus is on the preparation of low cost AC from agricultural wastes such as tamarind wood 5, mangoesteen peel 6, bamboo waste 7 and orange peel 8. A standard response surface methodology (RSM) design called a central composite design (CCD) has been utilized for optimization of AC production from tamarind wood 5, durian peel 9 and coconut husk 0. *Author for correspondence suffian@eng.usm.my This study presents preparation of AC from durian peel (DP) precursor and utilization of RSM for optimization of durian peel activated carbon (DPAC) preparation parameters [activation temperature, activation time and impregnation ratio (IR)] for optimum DPAC yield and optimal removal of ammoniacal nitrogen ( -N) from semi-aerobic landfill leachate. Experimental Section Semi-Aerobic Landfill Leachate Leachate samples were collected from Pulau Burung Semi-aerobic Landfill Site (PBLS), Byram Forest Reserve, Penang, Malaysia. All samples were collected from influent of detention pond in plastic containers (0 l) using standard method, transported to laboratory and stored at 4 C. Preparation of DPAC DP was collected from durian fruit (Durio zibethinus; Family: Bombacaceae), obtained from a local orchard in Nibong Tebal, Penang, Malaysia. DP was washed and then dried at 05 C for 4 h to remove moisture content. Dried DP was ground and sieved (size, - mm) and loaded in a stainless steel vertical tubular reactor placed in a tube furnace. Dried DP was carbonized at 700 C for h under purified nitrogen (99.99%). Char produced was mixed with phosphoric acid (H 3, 85% wt) with different IR

2 KAMARUDDIN et al : OPTIMIZATION OF DURIAN PEEL BASED ACTIVATED CARBON PREPARATION 555 Table Independent variables and their coded levels for CCD Variable (factors) Code Units Coded variable levels -a a Activation temperature C Activation time h Impregnation ratio (IR) W H PO [ IR = 3 4, where W char W H 3 H 3 and W char is dry weight (g) of char]. is weight (g) of Mixture was then kept in glass container covered with para-film at room temperature (RT) for 7 h to permit char and H 3 mix homogeneously. Activation was done using similar reactor as in carbonization step. Activated product was then cooled to RT under nitrogen flow. Sample was washed with hot deionized water and 0. M KOH until ph reached to DPAC yield was w calculated as Yield (%) = c 00 where w w c and w o o are dry weights (g) of final DPAC and raw DP, respectively. Adsorption Studies Concentration of N was measured by Nessler Method 3 (Method: 8038) using a Hach s DR 500 spectrophotometer (wavelength at 45 nm). For batch adsorption studies, DPAC (0 g) was mixed with leachate sample (00 ml) of -N (initial conc., 097 mg/l) in Erlenmeyer flasks (50 ml). Mixture was agitated at 30 rpm until it reached equilibrium condition, when final concentration of -N produced similar values from spectrophotometer reading. Removal (%) of -N at equilibrium wascalculated as Ci Cf NH 3 N removal (%) = 00, where C C i and C f are initial and final concentrations of -N (mg/l) respectively. Experimental Design In this study, RSM design CCD was utilized to investigate effects of DPAC preparation variables [activation temperature ( ), activation time ( ) and IR ( )] towards their responses on -N removal (Y ) and DPAC yield (Y ). CCD is ideal for sequential experimentation, as it allows reasonable amount of information to test lack of fit when a sufficient number of experimental values exist 3. These three variables together with their respective ranges were chosen based on preliminary studies (Table ). CCD consists of n factorial runs, n axial runs and 6 center runs, where n is number of factors. For three variables, full design matrix consists of 8 factorial points, 6 axial points and 6 replicates at center points, indicating altogether 0 experiments 0 calculated as N = n + n + n c = 3 + (3) + 6 = 0, where N is total number of experiments required. Low, center and high levels of each variable are designated according to face centered CCD as, 0, and +, respectively. Axial points are located at (±a, 0, 0), (a, ±, 0) and (a, 0, ±), where a (.68) is distance of axial point from center and makes design rotatable. Each response was used to develop an empirical model which correlated response to three activated carbon preparation variables using a seconddegree polynomial equation as 4 Y = b o + n i= bixi + n i= b x ii i + n i= n b x x ij i j= i+ j () where Y is predicted response, b o constant coefficient, b i linear coefficients, b ij interaction coefficients, b ii quadratic coefficients and x i, x j are coded values of AC preparation variables. Characterization of DPAC Surface area, pore volume and average pore diameter of DPAC were determined by using Autosorb AS (Quantachrome, USA) volumetric adsorption analyzer. BET surface area was measured from adsorption isotherms using Brunauer Emmett Teller equation method. Total pore volume was estimated to be liquid volume of nitrogen at a relative pressure of RSM of samples was examined using a scanning electron microscope (SEM) (Leo Supra, 55 VP, Germany).

3 556 J SCI IND RES VOL 70 JULY 0 Table Experimental design matrix for preparation of DPAC Run Level DPAC preparation variables -N removal, DPAC yield Activation Activation IR, Y (%) Y (%) temperature, time, ( C) (h) Proximate analysis was carried out using thermogravimetric analyzer (Perkin Elmer TGA7, USA). Elemental analysis was performed using Elemental Analyzer (Perkin Elmer Series II 400, USA). Results and Discussion Development of Regression Model Equation In complete design matrix (Table ), runs 5-0 at the center point were conducted to determine experimental error and reproducibility of data 6. -N removal and DPAC yield were found as % and %, respectively. Models were selected based on highest order polynomials where additional terms were significant and models were not aliased 7. For -N removal and DPAC yield, quadratic models were suggested by software. Final empirical formula models for -N removal (Y ) and DPAC yield (Y ) in terms of coded factors are given as Y = x () Y = x E (3) Coefficient with one factor represents effect of particular factor, while coefficients with two factors and those with second-order terms represent interaction between two factors and quadratic effect, respectively 6. Regression models obtained for Y and Y were satisfied since values of correlation coefficient, R were relatively high and close to unity. Values of R for Y and Y were and , respectively, indicating that 86.5% and 88.58% of total variation were accurate as explained by the model. Predicted values for Y and Y were closer to actual values for responses. Statistical Analysis In analysis of variance (ANOVA) of predicted response surface quadratic models for -N removal (Table 3), F-value (7.3) and Prob. > F (0.005) imply that model is significant for -N removal. In this case,

4 KAMARUDDIN et al : OPTIMIZATION OF DURIAN PEEL BASED ACTIVATED CARBON PREPARATION 557 Table 3 Analysis of variance (ANOVA) for response surface quadratic model for -N removal of DPAC Source Sum of squares Degree of Mean square F-Value Prob. > F Comment freedom (DF) Model significant Residual Lack of fit not significant Predicted -N removal, % Predicted DPAC yield, % Experimental -N removal, % Fig. Predicted versus experimental -N removal of DPAC,, and factors were significant model terms whereas,,, and factors were insignificant model terms to the response. In ANOVA for DPAC yield (Table 4), F-value (8.6) and Prob. > F (0.00) indicates that model is significant. In this case,,,,, and were significant model terms whereas, and were insignificant model terms to the response. Thus response surface model constructed for predicting N removal and DPAC yield were adequate and within the range of variables studied. From plots of predicted values versus experimental values for -N removal (Fig. ) and DPAC yield (Fig. ), it is further confirmed that models Experimental DPAC yield, % Fig. Predicted versus experimental yield of DPAC developed were successful in capturing correlation between DPAC preparation variables to the responses when predicted values obtained were quite close to experimental values. Lack of fit test for -N removal was statistically insignificant as P values (0.06) more than 0.05 shows that model satisfactorily fits the data. Compared to DPAC yield, P values (0.0004) indicates that there may be some systematic variation unaccounted for in hypothesized model 3. -N Removal Activation temperature was the most significant factor to -N removal followed by IR and activation time (Table 3). Quadratic effects of activation

5 558 J SCI IND RES VOL 70 JULY 0 Table 4 Analysis of variance (ANOVA) for response surface quadratic model for DPAC yield Source Sum of squares Degree of Mean square F-Value Prob. > F Comment freedom (DF) Model significant Residual Lack of fit significant NH3-N removal (%) DPAC yield (%) X3: IR X : Temperature, C X3: IR X: Temperature, C Fig. 3 3D response surface plot of -N removal of DPAC (effect of temperature and IR, t= h) Fig. 4 3D response surface plot of DPAC yield (effect of temperature and IR, t= h) temperature and IR show comparable effects to the response. Using 3D response surface curve 7, it was observed that highest -N removal was achieved when IR and activation temperature were at low level (Fig. 3) keeping activation time fixed at zero level (t= h). However, as temperature increased from 500 to 600 C, -N uptake shows slight reduction from 40% to 37%. Higher IR content in DPAC during carbonization might contribute to reduction of -N uptake. Increasing IR allows oxidation process, by which burn-off rate increases. Therefore, available active surface on char diminished slowly as IR and activation temperature increased. DPAC Yield Quadratic effect of activation temperature significantly contributes to the response compared to activation time and IR (Table 4). Using 3D response surface curve 7, it was observed that highest DPAC yield was achieved when IR and activation temperature were at low level (Fig. 4) keeping activation time fixed at zero level (t= h). DPAC yield was found to decrease with

6 KAMARUDDIN et al : OPTIMIZATION OF DURIAN PEEL BASED ACTIVATED CARBON PREPARATION 559 Table 5 Proximate and elemental contents of DP precursor, char and DPAC Sample Proximate analysis, % Elemental analysis, % Moisture Volatile Fixed Ash C H S (N+O) a Matter Carbon DP precursor Char DPAC a Estimated by differenc a) b) Fig. 5 SEM images: a) Raw DP; b) DPAC (magnification 000 x) increase in activation temperature as a consequence of a deeper dehydration of carbonaceous structure of precursor 5. During activation process, H 3 reacted with sample and volatile matter and was released from carbon surface. Therefore, with high IR, gasification of surface carbon became predominant, leading to an increase in weight loss and a low carbon yield 6. This behavior is also in agreement with reported result 7. Process Optimization Optimization process was carried out to determine optimum value of -N removal and DPAC yield using Design Expert software. In this case, it is difficult to optimize both responses because interest region of factors is different. Therefore, when -N removal increases, DPAC yield will decrease and vice versa. Hence, function of desirability was applied to compromise between responses. In this work, desired goal for variables were chosen within the range while responses set at maximum values. The program combines individual desirabilities into a single number, and then searches to maximize this function 3. Optimum DPAC preparation conditions were obtained by using the following values: activation temperature, 400 C; activation time,.7 h; and IR, 3. Optimum DPAC showed -N removal of 46.4% (predicted 47.4%) and DPAC yield of.0% (predicted.8%). Thus experimental values were in good agreement with predicted values from the models, with relatively small errors: -N removal,.58; and DPAC yield, 3.6 %. Characterization of Optimized DPAC Generally, porosity of ACs not only depends on experimental conditions of carbonization and activation steps but also preponderantly on original nature and structure of involved material 8. DPAC had: BET surface area, m /g; total pore volume, 0.50 cm 3 /g; and average pore diameter,.60 nm. Average pore diameter (.60 nm) indicated that DPAC was in mesoporous region. High surface area and pore volume of DPAC were due to activation process, which involved N and H 3. Increasing amount of H 3 led to enlarge micropore walls of mesopore structure 6. Looking into proximate

7 560 J SCI IND RES VOL 70 JULY 0 and elemental analysis of raw DP, char and optimized DPAC (Table 5), increment of carbon content was due to pyrolytic effect at high temperature 8. Volatile matter content reduced to 7.03% in DPAC sample. From SEM images, surface texture of raw DP (Fig. 5a) was uneven, rough and undulating with very little pores available on the surface. After activation process, resulted DPAC produced large and well-developed pores on its surface demonstrating almost heterogeneous and randomly distributed pore structure (Fig. 5b). Similar observations are reported on AC from bamboo waste 7, tamarind wood 5 and coconut shell 8. Conclusions Effects of DPAC preparation variables (activation temperature, activation time and IR) were studied on -N removal and DPAC yield by using CCD. Through analysis of response surface, activation temperature imposed greatest effect on -N removal and DPAC yield. Process optimization was carried out and experimental values were found comparable with predicted values. Optimum DPAC preparation conditions (activation temperature, 400 C; activation time,.7 h; and IR, 3) resulted in optimal -N removal (46.49%) and DPAC yield (.0%). Optimized DPAC sample demonstrates high BET surface area, and almost heterogeneous and randomly distributed pore structures. Acknowledgements Authors gratefully acknowledge Universiti Sains Malaysia for research grant provided under Postgraduate Research Grant and fellowship scheme. Authors also thank Mineral and Geological Department of Malaysia for facilities provided throughout the research period. References Guo, J S et al, Treatment of landfill leachate using a combined stripping, Fenton, SBR, and coagulation process, J Haz Mater, 78 (00) Ince M et al, Further treatment of landfill leachate by nanofiltration and microfiltration-pac hybrid process, Desalination, 55 (00) Tatsi A A et al, Coagulation-flocculation pretreatment of sanitary landfill leachates, Chemosphere, 53 (003) Kurniawan T A, Lo W & Chan G, Physico-chemical treatments for removal of recalcitrant contaminants from landfill leachate, J Haz Mater, 9 (006) Sahu J N, Acharya J & Meikap B C, Optimization of production conditions for activated carbons from Tamarind wood by zinc chloride using response surface methodology, Biores Technol, 0 (00) Ahmad M A & Alrozi R, Optimization of preparation conditions for mangosteen peel-based activated carbons for the removal of Remazol Brilliant Blue R using response surface methodology, Chem Engg J, Ahmad A A & Hameed B H, Effect of preparation conditions of activated carbon from bamboo waste for real textile wastewater, J Haz Mater, 73 (00) Ahmad A A, Hameed B H & Ahmad A L, Removal of disperse dye from aqueous solution using waste-derived activated carbon: Optimization study, J Haz Mater, 70 (009) Nuithitikul K, Srikhun S Hirunpraditkoon S, Influences of pyrolysis condition and acid treatment on properties of durian peelbased activated carbon, Biores Technol, 0 (00) Tan I A W, Ahmad A L & Hameed B H, Optimization of preparation conditions for activated carbons from coconut husk using response surface methodology, Chem Engg J, 37 (008) APHA and W. AWWA, Standard methods for the examination of water and wastewater (American Public Health Association, Washington) 99. Wai W W, Alkarkhi A F M & Easa A M, Optimization of pectin extraction from durian rind (Durio zibethinus) using response surface methodology, J Food Sci, 74 (009) C637-C64. 3 Bashir M J K et al, Application of response surface methodology (RSM) for optimization of ammoniacal nitrogen removal from semi-aerobic landfill leachate using ion exchange resin, Desalination, 54 (00) Singh K P et al, Experimental design and response surface modeling for optimization of Rhodamine B removal from water by magnetic nanocomposite, Chem Engg J, 65 (00) Rosas J M et al, HEMP-derived activated carbon fibers by chemical activation with phosphoric acid, Fuel, 88 (009) Patnukao P & Pavasant P, Activated carbon from Eucalyptus camaldulensis Dehn bark using phosphoric acid activation, Biores Technol, 99 (008) Haimour N M & Emeish S, Utilization of date stones for production of activated carbon using phosphoric acid, Waste Mgmt, 6 (006) Mohd Din A, Hameed B & Ahmad A, Batch adsorption of phenol onto physiochemical-activated coconut shell, J Haz Mater, 6 (009) 5-59.