Production of Activated Carbon From Coconut Shell Optimization Using Response Surface Methodology

Production of activated carbon from coconut shell: Optimization using response surface methodology M.K.B. Gratuito a , T. Panyathanmaporn b , R.-A. Chumnanklang b , N. Sirinuntawittaya b , A. Dutta a, * a Energy Field of Study, School of Environment, Resources and Development, Asian Institute of Technology, P.O. Box 4, Klongluang, Pathumthani 12120, Thailand b National Metal and Materials Technology Center (MTEC), 114 Thailand Science Par
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  Production of activated carbon from coconut shell: Optimizationusing response surface methodology M.K.B. Gratuito  a , T. Panyathanmaporn  b , R.-A. Chumnanklang  b ,N. Sirinuntawittaya  b , A. Dutta  a,* a Energy Field of Study, School of Environment, Resources and Development, Asian Institute of Technology, P.O. Box 4, Klongluang,Pathumthani 12120, Thailand  b National Metal and Materials Technology Center (MTEC), 114 Thailand Science Park, Paholyothin Road, Klongluang, Pathumthani 12120, Thailand  Received 16 May 2007; received in revised form 11 September 2007; accepted 15 September 2007Available online 13 November 2007 Abstract The production of activated carbon from coconut shell treated with phosphoric acid (H 3 PO 4 ) was optimized using the responsesurface methodology (RSM). Fifteen combinations of the three variables namely; impregnation ratio (1, 1.5, and 2); activation time(10, 20, and 30 min); and activation temperature (400, 450, and 500   C) were optimized based on the responses evaluated (yield, bulkdensity, average pore diameter, small pore diameter, and number of pores in a unit area). Pore diameters were directly measured fromscanning electron microscope (SEM) images. Individual second-order response surface models were developed and contour plots weregenerated for the optimization analysis. The optimum range identified for impregnation ratio was from 1.345 to 2, while for the activa-tion time was from 14.9 to 23.9 min. For the activation temperature it was from 394 to 416   C. The optimum points are 1.725, 19.5 min,and 416   C, respectively. The models were able to predict well the values of the responses when the optimum variable parameters werevalidated as proven by the generally acceptable values of the residual percentages. Direct characterization of the pores using the SEMwas found to be a good technique to actually see the pores and get actual measurements. Additionally, RSM has also proven to be a goodtool in optimization analysis to get not only optimum production condition points but ranges, which are crucial for the flexibility of theproduction process, as well.   2007 Elsevier Ltd. All rights reserved. Keywords:  Activated carbon; Chemical activation; Scanning electron microscope; Optimization; Response surface methodology 1. Introduction Activated carbon can be produced from different rawcarbon resources like lignite, peat, coal, and biomassresources such as wood, sawdust, bagasse, and coconutshells (Ioannidou and Zabaniotou, 2006). However, theabundant supply of coconut shell as a waste-product fromthe coconut oil and desiccated coconut industry makes pro-duction of activated carbon from this material more finan-cially viable since using grain or coal as raw materials foractivated carbon will require manufacturers extra amountof money for procurement. Furthermore, besides beingan amorphous form of carbon that can absorb many gases,vapors, and colloidal solids, coconut shell activatedcarbons are advantageous over carbons made from othermaterials because of its high density, high purity, and vir-tually dust-free nature. These carbons are harder and moreresistant to attrition.Production of activated carbon can either be throughphysical or chemical activation. In physical activation,the material is carbonized under inert atmosphere andthen activated at high temperature using either steam orcarbon dioxide as the activating reagent while in chemicalactivation, the precursor is treated with chemicals tohelp with the initial dehydration. In most cases, chemical 0960-8524/$ - see front matter    2007 Elsevier Ltd. All rights reserved.doi:10.1016/j.biortech.2007.09.042 * Corresponding author. Tel.: +662 524 5403; fax: +662 524 5439. E-mail address: (A. Dutta).  Available online at Bioresource Technology 99 (2008) 4887–4895  activation is preferred over physical activation as the lattergenerally results to lower yields due to the mass loss asso-ciated with oxidation (Toles et al., 2000). In addition,chemical activation, which is commonly used for biomassprecursor mainly because it achieves higher yield and largersurface areas, also requires lower operating and energycosts as lower temperatures are used.However, like in most production processes, the produc-tion of quality activated carbon involves balancing the pro-duction conditions to get the desired characteristics of theoutput. More often than not, this balancing becomes com-plicated as there are more than one characteristic that hasto be considered. Also, the desirable characteristics notonly refer to the requirements of the end-users such as highsurface area, appropriate porosity, and high bulk densitybut also refer to the producer side. Higher yield obtainedat lower operating and energy cost is always desired butcareful balancing is a must to avoid overlooking othercharacteristics. The goal of this study was to find the opti-mum production conditions for making activated carbonfrom coconut shell by simultaneously considering theimpregnation ratio, activation time, and activation temper-ature. Desirable production outputs based on yield andbulk density were considered as responses. Furthermore,direct measurements of the pores using images from thescanning electron microscope (SEM) were also taken asresponses.As far as known to the authors, no study has been doneon optimization of the production of activated carbonfrom coconut shells using the response surface methodol-ogy (RSM) approach. Though RSM is a popular tool inprocess optimization, its application in activated carbonproduction is very rare. RSM has just recently been usedfor the optimization of coconut husk (Tan et al., 2007),Turkish lignite (Karacan et al., 2007) and olive-waste cakes(Bacaoui et al., 2001). Additionally, the use of SEM imagesfor direct pore size measurements and the inclusion of itsresults as a response for RSM are also new and unexplored. 2. Important parameters for activation The succeeding sections will explain the complexity of balancing the production conditions for the chemical acti-vation as each independent variable has its own effect onboth physical and adsorption characteristics of carbonsas well as in production outputs such as yield and bulkdensity.  2.1. Chemical/impregnation ratio In chemical activation process, it is well known that theimpregnation ratio, the ratio of the weights of the chemicalagent and the dry precursor, is one of the variables thathave major effect on the characteristics of the final carbonsproduced. The chemical agents used are dehydrating agentsthat penetrate deep into the structure of the carbon causingtiny pores to develop. Thus, aside from affecting the devel-opment of the pores, particularly the size, it also affects theresulting surface area as generally smaller pores will resultto larger surface areas. The surface area of untreated  Arun-do  canes was observed to increase from 38 to 1151 m 2 /gafter chemical treatment using phosphoric acid (Verners-son et al., 2002). The general trend for all precursors is thatas concentration is increased the surface area also increasesthough an optimum concentration is evident in most cases.Larger pores, which correspond to smaller surface area,develop as more acids are used. When pores reach a partic-ular size (in the range from mesopore to macropore), theydo not contribute to the surface area significantly (La´zaroet al., 2007). For peach stones, it was found that anincrease of impregnation ratio resulted to an increase inthe volumes of micropores and mesopores (Molina-Sabioet al., 1995) but for powdered peanut hulls the maximumarea was attained only at an impregnation ratio of 1 (Girgiset al., 2002). Furthermore, only a 35% phosphoric acidsolution was determined ideal for sorghum as it was bestboth for surface area and porosity development (Diaoet al., 2002). The degree of weakening of the sorghum grainstructure was also minimized, thus the hardness and bulkdensity of the resulting product were not compromised.Progressive development of porosity, especially of largerpores, occur as more acid is incorporated until a limit isreached, of which a larger excess leads to collapsed poreslikely due to structural weakness caused by the intensifieddilation. High ratios led to a reduction in pore volumesand a marked decrease in surface area for  Arundo  cane(Vernersson et al., 2002).  2.2. Activation duration The duration of the activation has a significant effect onthe development of the carbon’s porous networks. Thetime should just be enough to eliminate all the moistureand most of the volatile components in the precursor tocause pores to develop. Since the end of the volatile evolu-tion marks the formation of the basic pore structure, acti-vation should be limited up to that point. Longer durationscause enlargement of pores at the expense of the surfacearea. Also, the control of the activation time is of economicinterest since shorter times are generally desired as itequates to reduction in the energy consumption. In a studyconducted for grain sorghum by Diao et al. (2002), initialexperiments of activation at 450   C resulted to smoke evo-lution ceasing only after 8 min thus the minimum activa-tion time was set at 10 min and 15 min was observed tobe the optimum for porosity development (Diao et al.,2002). For the case of   Arundo  cane, zero activation timewhich actually means that the sample has just reachedthe desired activation temperature but was already exposedto heat progression while attaining the desired temperatureyielded the highest surface area and micropore volume(Vernersson et al., 2002). Maximum surface area wasattained at 45 min for the activation of rubber woodsawdust and was reported to decrease with further increase 4888  M.K.B. Gratuito et al. / Bioresource Technology 99 (2008) 4887–4895  in activation time (Srinivasakannan and Zailani AbuBakar, 2004). Yield also appeared to be dependent on theactivation duration as it dropped upon reaching an opti-mum point. Low activation time resulted to an incompleteburn-off thus resulting in higher yields.  2.3. Activation temperature The application of heat to an impregnated material fur-ther accelerates the thermal degradation and the volatiliza-tion process. This leads to development of pores, increaseof surface area and the subsequent mass loss. The selectionof the activation temperature is based on several factorswhich include the type of precursor and the chemical agentused. Activation temperature for different biomass precur-sors range from 400 to 800   C (Diao et al., 2002) while for coal-based materials can go as high as 900   C (Karacanet al., 2007).The optimum activation temperature for higher surfacearea was found to be 450   C for coconut shells impregnatedwith phosphoric acid (Laine et al., 1989) and 500   C forrubber wood sawdust (Srinivasakannan and Zailani AbuBakar, 2004), though yield was at the lowest for the latter.For impregnated acorns, 800   C offered the highest adsorp-tion capacity as reported by Lafi (2001). Temperatureslower than 500   C for grain sorghums produced micropo-rous carbons but with small surface areas while tempera-tures higher than 600   C yielded mesoporous carbonswith high surface areas (Diao et al., 2002). For  Arundo cane, lower activation temperature (400   C) was found togive rise to a microporous carbons, while higher tempera-tures (500–550   C) resulted in carbons with larger pores(Vernersson et al., 2002). 3. Imaging the pore structure of activated carbons Activated carbons are traditionally characterized usingindirect methods such as gas adsorption. The Langmuirand its extension, the BET equation, are commonly appliedto the adsorption isotherm to calculate the specific surfacearea and further analysis can lead to obtaining the porediameters. Very few have characterized activated carbonsusing direct methods such as utilizing actual imagesobtained using high magnification microscopes. High-reso-lution transmission electron microscope (HRTEM) wasused to have a visualized observation of pores in activatedcarbon fibers (Endo et al., 1998) and anthracite (Lillo-Rod- enas et al., 2004). The two-dimensional fast Fourier trans-form (FFT) was used to carry out the frequency analysis toobtain the pore size distribution. Image analysis of acti-vated anthracite provided de-averaged data on fringeslength, interlayer spacings, and number of stacked layerswhich were helpful in describing the structures of the pores(Lillo-Rodenas et al., 2004). Scanning tunneling micros-copy (STM) techniques were also used for comparativeinvestigation of different activated carbons of ultramicro-porous, supermicroporous, and mesoporous types (Paredeset al., 2006). The morphologies of these carbons, whichinclude pore diameter and pore shape were analyzed usingthe STM images. Scanning electron microscopy (SEM) wasused by Vernersson et al. (2002) to see the influence of acti-vation temperature and impregnation ratio on the porestructure, particularly the shape and diameter of the poresof   Arundo donax  cane activated using phosphoric acid. Thesurface morphology of the optimally prepared activatedcarbon from coconut husk was also analyzed using imagesfrom the SEM by Tan et al. (2007). The images obtainedwere compared against images taken from the raw material(raw coconut husk) wherein very little pores were seen onthe surface. The activated carbon showed many large poresin honeycomb shape. 4. Methods 4.1. Preparation of the activated carbon Clean, fiber-free, and soil-free coconut shells were milledto reduce the size down to mesh 8 (2.4 mm diameter). Phos-phoric acid solutions were prepared to the required impreg-nation ratios of 1.0, 1.5 and 2.0, defined as the ratio of dryweight of H 3 PO 4  to the weight of the coconut shell basedon a study by Molina-Sabio et al. (1995). Ten grams of the coconut shells were used per sample. Soaking timewas fixed at 12 h. Since most literatures reported that opti-mum activation temperature for most biomass materialsgenerally falls between 400 and 500   C (Srinivasakannanand Zailani Abu Bakar, 2004), temperatures of 400, 450,500   C were evaluated for this study. Activation times of 10, 20, and 30 min were also assessed. These times werebased on an initial experimental run at 400   C, whereinsmoke evolution ceased after about seven (7) min. It waspresumed that at this time, moisture and most of the vola-tiles were eliminated from the precursor. Thus, the mini-mum time evaluated was set at 10 min. A steady supplyof nitrogen was provided for the whole activation time tohave an inert environment for the activation process.Washing then followed to remove traces of acid in the acti-vated carbon. The activated samples were repeatedlywashed with about 100 ml of distilled water. The acidityof the wash liquor was monitored until the pH reading isat 6–7. On the average, 7–8 washings were able to neutral-ize the sample. The activated carbon was then againwashed with 0.1 M sodium hydroxide solution and finallywith distilled water. The washed activated carbon sampleswere then placed in an electric oven with temperature set at105   C for drying. 4.2. Experimental design and statistical analysis A 3 3 (impregnation ratio, activation temperature, andactivation time) fractional factorial experimental designbased on Box and Behnken (Myers and Montgomery,2002) with three center runs was used, giving a total of  M.K.B. Gratuito et al. / Bioresource Technology 99 (2008) 4887–4895  4889  15 experimental runs. The dependent variables (responses)analyzed were yield, bulk density, average diameter of common pores, average diameter of small pores, and thenumber of pores per unit area. 4.3. Evaluation of the responses The carbon yield was the ratio of the weight of the acti-vated and dried carbon to the initial weight of the coconutshell taken for activation, both based on dry basis andexpressed as percentage.The bulk density was determined using the method of Lima and Marshall (2005) by filling a 10-ml tube withthe sample, 1 ml at a time, capping and tapping to a con-stant (minimum) volume. The bulk density was taken asthe ratio of the weight (in grams) and the volume (in cm 3 ).Representative samples from each experimental runwere used for the image analysis using the scanning elec-tron microscope (SEM) of the National Metals and Mate-rials Technology Center (MTEC) at Thailand SciencePark. Images at 500 ·  magnification were taken for the dig-ital image analysis using the Image Pro Plus version 5.1 forwindows. The average pore diameter, small pore diameterand the total area in a plane occupied by the pores weredetermined from the images. The average pore diameterswere taken from the normally distributed readings asshown in a sample experiments in Fig. 1. The number of pores per unit area was determined using the average diam-eter of the pores and the total area covered by the pores.The simple formula is shown below: n ¼ Total area of all pores in a planeaverage area of the pores  ð 1 Þ 4.4. Regression and optimization analysis The resulting data were regressed to derive a suitableequation for each response. All variable parameters andtheir interactions were considered for a model for eachresponse. A statistical analysis software was used to solvethe coefficients of the second-order model with three vari-ables for each response as shown below: Y    ¼ b 0 þ b 1  X  1 þ b 2  X  2 þ b 3  X  3 þ b 11  X  21 þ b 22  X  22 þ b 33  X  23 þ b 12  X  1  X  2 þ b 13  X  1  X  3 þ b 23  X  2  X  3  ð 2 Þ where  b 0 ,  b 1 ,  b 2 ,  b 3 ,  b 11 ,  b 22 ,  b 33 ,  b 12 ,  b 13  and  b 23  are theregression coefficients;  X  1 ,  X  2 , and  X  3  are the coded inde-pendent variables/regressor; and  Y   is the particular re-sponse evaluated. Predicted values were solved from thederived equations for each of the response. These valueswere plotted to obtain contour plots that were used forthe optimization process. Boundary conditions were ap-plied to the contour plots. For yield, the boundary condi-tion was set at 50% (the region corresponding to lesservalues were not accepted). This value is higher than theyields reported for carbonization and activation processesof dried coconut shell which are around 32.46% (Bhattach-arya et al., 1989) and 40% (FFTCAPR, 2004), respectively. As for the bulk density, the boundary condition was set at460 kg/m 3 , and the region corresponding to lesser values of bulk densities were not accepted. Bulk densities of agricul-tural-based raw materials are generally low. Activated car-bons from sugarcane bagasse and pecan shells have bulkdensities around 470 and 440 kg/m 3 , respectively, (Ahmed-na et al., 2000). For the average pore diameter, values high-er than 10  l m were not accepted. For the small porediameter, the boundary condition was 1.2  l m (1200 nm). Exp 3 = 93*1*normal(x, 9.5044, 3.6204) 0 2 4 6 8 10 12 14 16 18 micrometer  024681012    N  o  o   f  o   b  s Fig. 1. Pore diameter readings shown in a normal distribution curve for a sample experiment.4890  M.K.B. Gratuito et al. / Bioresource Technology 99 (2008) 4887–4895
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