Energy Fuels 2009, 23, 5219–5227 Published on Web 09/11/2009
: DOI:10.1021/ef900468p
Combination of Fractional Factorial and Doehlert Experimental Designs in Biodiesel Production: Ethanolysis of Raphanus sativus L. var. oleiferus Stokes Oil Catalyzed by Sodium Ethoxide Pedro W. P. A. Valle, Thaı´ s F. Rezende, Ros^angela A. Souza, Isabel C. P. Fortes,* and V^anya M. D. Pasa Laborat orio de Ensaios de Combustı´veis, Departamento de Quı´mica, Instituto de Ci^ encias Exatas, Universidade Federal de Minas Gerais. Av. Ant^ onio Carlos, 6627, Campus Pampulha, 31270-901 Belo Horizonte, Minas Gerais, Brasil Received May 15, 2009. Revised Manuscript Received August 18, 2009
Fodder radish;Raphanus sativus L. var. oleiferus Stokes;oil has been regarded as an interesting option to produce biodiesel, because the oil cannot be used for human consumption, the seeds have high oil content, and the cost of production is low. Furthermore, the plant has been used for green fertilization during the interval between harvests of other crops, due to its rapid development as well as its great ability to recycle nutrients. The content of free fatty acids in the crude oil is less than 0.5%, which makes it appropriate for basic catalyzed synthesis. However, basic catalyzed synthesis is very sensitive to the presence of water in the reaction environment. This study proposes the optimization of biodiesel synthesis using sodium ethoxide (sodium ethylate) as a catalyst, with the purpose of minimizing water formation during reaction, increase efficiency, and thus carry out transesterification in a single step. Ethanol was used instead of the methylic route, aiming at the production of an entirely renewable and environmentally preferable fuel. The experiments were proposed and carried out using a combination of fractional factorial design and Doehlert design, in order to allow an extensive study of the process variables with a minimum of experiments. Very intense levels of agitation and high temperatures proved to be inadequate to reach an effective reaction. At optimum conditions the ester content reached approximately 97.9%, which along with several other physical chemistry assays confirm the good quality of the product and that the synthesis of fodder radish crude oil can be performed in a single step efficiently.
to Rios,7 the production of the seeds, and the content of oil, is approximately 1200 kg/ha, and 40%, respectively. However, Teixeira3 states that 1200 kg may produce approximately 336 L of oil. In addition, fodder radish oil is inedible, develops quickly (90-120 days) and has a low cost of production. These last two characteristics, as well as its great ability to recycle nutrients such as phosphorus and nitrogen, make fodder radish one of the most commonly used species for green fertilization during the interval between harvests of other crops.3,6,8-10 This oilseed develops fairly well in weak and acid soils, and as the plant is fast-growing, it competes with weeds because of its germination and saves in herbicides as well as in weed removal.6 Due to these properties, some ethanol manufacturers in the state of S~ ao Paulo (Brazil) have recently shown interest in using fodder radish during the renovation of their canebrakes.7 There are few references in literature that report the use of fodder radish in biodiesel synthesis,.2,4,5 Recently some reports5 have been done where biodiesel syntheses were carried out in supercritical conditions, using no catalysts.
1. Introduction The use of vegetable oils;such as palm, soybean, sunflower, peanut, and olive oil;as alternative fuels for diesel engines dates back to the beginning of the twentieth century. Today, due to the rapid decline of oil reserves, various countries emphasize the use of vegetable oils for diesel fuel.1 In Brazil, castor oil, soybean, sunflower, red palm oil, maize, fodder radish, and Barbados nut;to name a few;are some of the oil seeds used to produce biodiesel. At present, the diesel commercialized in gas stations in Brazil is a mixture containing 4% w/w of biodiesel and the annual biodiesel consumption is over one billion liters. Fodder radish (Raphanus sativus L. var. oleiferus Stokes) has been regarded as an interesting oil seed option to produce biodiesel,2-6 due to the high oil content and because it can be easily extracted through cold pressing of the seeds. According *To whom correspondence should be addressed. Phone: 55 31 34095756. Fax: 55 31 3409-6650. E-mail:
[email protected]. (1) Srivastava, A.; Prasad, R. Renew. Sustain. Energy Rev. 2000, 4, 111–133. (2) Ferrari, R. A.; d’Arce, M. A. B. R.; Ribeiro, F. L. F. Biodiesel de leo de Raphanus sativus L. In: II Congresso Brasileiro de Plantas o Oleaginosas; Oleos, Gorduras e Biodiesel: Varginha, Brasil, 2005. (3) Teixeira, L. C. Informe Agropecu ario 2005a, 26 (229), 18–27. (4) Domingos, A. K.; Saad, E. B.; Wilhelm, H. M.; Ramos, L. P. Bioresour. Technol. 2008, 99, 1837–1845. (5) Valle, P. W. P. A.; Velez, A.; Hegel, P.; Brignole, E. A. Biodiesel production using supercritical alcohols and different vegetable oils in batch and continuous reactors. In: 11th European Meeting on Supercritical Fluids - “New Perspectives in Supercritical Fluids: Nanoscience, Materials and Processing”, Barcelona, Spain, 2008. (6) BiodieselBR. Available on-line at: http://www.biodieselbr.com/. r 2009 American Chemical Society
(7) Rios, M. Nabo forrageiro e opc-~ao na reforma de canaviais. J. Cana 2008, 178, 30. Available on-line at: http://www.jornalcana.com.br/ pdf/178//proddadnot.pdf. (8) Crochemore, M. L.; Piza, S. M. T. Pesquisa Agropecu aria Brasileira 1994, 29, 667–668. (9) Adubac-~ao verde, o solo agradece. Sociedade Nacional de Agricultura 1996. Available on-line at: http://biblioteca.sna.agr.br/artigos/artitecadubacao.htm. (10) Kochhann, R. A.; Santos, H. P.; Voss, M.; Denardin, J. E. Rendimento de gr~ aos de trigo cultivado em sequ^ encia ao adubo verde nabo forrageiro. Comunicado Tecnico On-line 116 do Ministerio da Agricultura, Pecuaria e Abastecimento MAPA; Passo Fundo, Brasil, 2003. Available online at: http://www.cnpt.embrapa.br/biblio/co/p_co116.htm.
5219
pubs.acs.org/EF
Energy Fuels 2009, 23, 5219–5227
: DOI:10.1021/ef900468p
Valle et al.
The worldwide commercial process employed to produce biodiesel is the transesterification of vegetable oils, which uses a short-chain alcohol, mainly methanol or ethanol, and a basic catalyst or acid.11,12 In this study, transesterification is done using ethylic alcohol obtained from sugar cane fermentation, since Brazil;the largest international producer (16 billion L/y);has a large supply of this biofuel. Compared to methanol, biodiesel synthesis with ethanol has slower kinetics but has the advantage of producing entirely renewable biodiesel. One of the tools employed to optimize processes is the use of experimental designs, a task that should precede the execution of the experiments and is regarded as a very important statistical activity.13,14 For the authors,13,14 the development of experimental research (working from little or no information on the system under study until the optimization of the variables of the process) may be achieved through the successive application of specific statistical techniques. These techniques are: fractional factorial design (FFD), factorial design (FD), least-squares fittings, and response surface methodology (RSM). For Montgomery,14 if the purpose of the experimental design is to determine the effects of the process factors or variables and the interaction between them on the outcome of the experiment, factorial is the most efficient design. The reasons for Montgomery’s opinion are based on the fact that, in general, only two levels of factors are used in this type of design: a lower factor represented by the sign (-) and a higher, represented by the sign (þ), and all the possible combinations of these two levels are investigated. The number of experiments needed in a FD with two levels is given by 2k, where 2 and k represent the levels and number of variables, respectively. However, when the number of factors to be investigated is high, FD may be fractionized in order to reduce the number of experiments. With the reduction of experiments, some information on the effects of the interaction between variables over the result of the experiment is affected, but the main effects of the isolated variables are not. More information on the FD, the possibilities of fractionation, and the use of repetitions in the central point of the experimental domain (which allows to determine the uncertainty associated with the experimental repetitions) may be obtained in the references mentioned.13,14,17-19 In the final stage of the optimization work, only the factors that most influence (in general no more than three) the outcomes of the experiments are selected to compose a new structure of design which may be, basically, of three types: central composite design (CCD), Box-Behnken design, and Doehlert design. The detailing of each of these plans is beyond the scope of this work, but may be consulted in the suggested bibliography.13-19 Among the three designs of experiments, the Doehlert is regarded as the most efficient because it demands fewer experiments, as indicated in Table 1.19
Response surface methodology (RSM) has been applied to optimize the synthesis of biodiesel.4,5,20-23 In a recent publication,4 the authors used NaOH as a catalyst in biodiesel synthesis of fodder radish oil, and central composite design (CCD) to optimize the process. Three factors were studied: the molar ratio of ethanol/oil, the catalyst concentration, and temperature. The level of agitation and the time of reaction were not studied. To provide an efficient reaction, and in order to meet current Brazilian legislation parameters for biodiesel,24 the synthesis was divided into two steps. The conditions of the first step were: molar ratio of ethanol/oil = 11.7:1; NaOH concentration=0.4% w/w; temperature=38 °C, and vigorous agitation for 60 min. In the second step, molar ratio and catalyst concentration were reduced to 6:1 and 0.03% w/w, respectively. The other variables were maintained. Basic-catalyzed synthesis is very sensitive to the presence of water in the reaction environment.11 According to Knothe et al.,25 to maximize the efficiency of a transesterification reaction minimizing hydrolysis of the esters formed and the formation of soaps, the alcohol used should be free from humidity, and the free fatty acids (FFA) in the vegetable oil should not exceed 0.5% w/w. To reduce the production of water during the reaction, our work proposes the use of CH3CH2ONa (sodium ethylate) in the ethanolysis of fodder radish crude oil, in order to improve the efficiency of the reaction and thus carry out transesterification in a single step. To select the most influential variables in the synthesis of biodiesel, and later optimize the synthesis conditions with this catalyst, two different statistical designs were used: FFD followed by Doehlert design. The successful results show that the transesterification catalyzed by sodium ethylate is very efficient. In addition, by using an oilseed that is inappropriate for human consumption as well as beneficial to agriculture, as mentioned above, this study answers (at least in part) the current accusation that largescale development of biofuels endangers worldwide food production. 2. Experimental Section Material and Methods. The reagents used in the synthesis of biodiesel were fodder radish crude oil, analytical standard ethylic alcohol (Cromoline Quı´ mica Fina Ltd.a.), and sodium ethylate (21% in ethanol solution, from Sigma-Aldrich). Fodder radish oil was extracted from seeds provided by G^enesis Sementes Armazens Gerais Ltd.a. using cold-pressing, with a 15 T Charlott press. Suspension materials were separated using a centrifuge (Precision Scientific Petroleum Instruments). The oil was characterized by physicochemical assays, such as: specific gravity, viscosity, humidity, and acid number. The specific gravity was obtained at 20 °C, according to ASTM D 4052,26 (20) Vicente, G.; Coteron, A.; Martı´ nez, M.; Aracil, J. Ind. Crops Products 1998, 8, 29–35. (21) Ghadge, S. V.; Raheman, H. Bioresour. Technol. 2006, 97, 379– 384. (22) Vicente, G.; Martı´ nez, M.; Aracil, J. Bioresour. Technol. 2007a, 98, 1724–1733. (23) Vicente, G.; Martı´ nez, M.; Aracil, J. Bioresour. Technol. 2007b, 98, 1754–1761. (24) Ag^encia Nacional de Petr oleo, Gas Natural e Biocombustı´ veis. ANP Resoluc-a~o n. 7; ANP: Brasília, Brasil, 2008. Available on-line at: http:// www.anp.gov.br/petro/legis_biodiesel.asp. (25) Knothe, G.; Gerpen, J. V.; Krahl, J. Manual de Biodiesel; Traduc-~ao de Luiz Pereira Ramos; Blucher, E. Ed.: S~ao Paulo, Brasil, 2006. Original title: The biodiesel handbook. (26) American Society for Testing and Materials. ASTM D 4052 - 96 (Reapproved 2002): Standard Test Method for Density and Relative Density of Liquids by Digital Density Meter; ASTM International: West Conshohocken, United States, 1996.
(11) Kusdiana, D.; Saka, S. Bioresour. Technol. 2004, 91, 289–295. (12) Teixeira, L. C. Informe Agropecu ario 2005b, 26, 79–86. (13) Neto, B. B.; Scarminio, I. S.; Bruns, R. E. Como fazer experimentos: pesquisa e desenvolvimento na ci^ encia e na ind ustria, 2a ed.; UNICAMP: Campinas, Brasil, 2003. (14) Montgomery, D. C. Design and Analysis of Experiments, 6th ed.; John Wiley & Sons, Inc.: New York, 2005. (15) Doehlert, D. H. Appl. Statistics 1970, 19, 231–239. (16) Box, G. E. P.; Behnken, D. W. Technometrics 1960, 2, 455–475. (17) NIST/SEMATECH e-Handbook of Statistical Methods. Available on-line at: http://www.itl.nist.gov/div898/handbook/index.htm. (18) Ferreira, S. L. C.; Santos, W. N. L.; Quintella, C. M.; Neto, B. B.; Sendra, J. M. B. Talanta 2004, 63, 1061–1067. (19) Te ofilo, R. F.; Ferreira, M. M. C. Quim. Nova 2006, 29, 338–350.
5220
Energy Fuels 2009, 23, 5219–5227
: DOI:10.1021/ef900468p
Valle et al.
Table 1. Comparison of Experimental Designsa number of experiments number of variables k 2 3 4 5 6 a
efficiency of the model
number of terms in the model
central composite
Box-Behnken
Doehlert
p
N = 2k þ 2k þ C
N = 2k - 2k þ C
N=k þkþC
6 10 15 21 28
9 15 25 43 77
13 25 41 61
7 13 21 31 43
2
central composite
2
Box-Behnken
Doehlert
p/k 0.67 0.67 0.60 0.49 0.36
0.86 0.77 0.71 0.68 0.65
0.77 0.60 0.51 0.46
Adapted from Te ofilo and Ferreira.19 This table only takes into consideration one experiment in central point C.
Figure 1. Chromatogram of Raphanus sativus L. var. Oleiferus Stokes oil fatty acids analyzed as methyl esters by gas chromatography using a capillary column (DB-Wax, 30 m 0.25 mm i.d., 0.25 μm, J&W Scientific). Detector (FID) and injector (split 1:100) at 260 °C. The FAMES were identified by comparing retention times to a standards mix (Supelco37). Table 2. Raphanus sativus L. var. oleiferus Stokes Oil Fatty Acids Composition
using a digital densimeter, Anton Paar DMA 4500. The kinematic viscosity was obtained according to ASTM D 44527 by a viscosimeter, Precitech Instrumental Ltd.a. The current humidity in the oil and the acid number were determined by gravimetry and potenciometric titration with standardized NaOH solution, respectively, according to the methods recommended by Instituto Adolfo Lutz28 and ASTM D 664.29 The molar mass of fodder radish oil was calculated as 900.32 g/ mol based on its fatty acid composition shown in Figure 1 and Table 2 and on the acid number (0.7182 mgKOH/goil) determined by potenciometric titration. The transesterification reactions were carried out in a 1 L glass Kettle reactor, under mechanical agitation (Fisatom Stirrer 713T),
fatty acid
CN:DBa
ret. time (min)
composition (% w/w)
palmitic stearic oleic linoleic linolenic arachidic gadoleic erucic lignoceric nervonic
16:0 18:0 18:1 18:2 18:3 20:0 20:1 22:1 24:0 24:1
9.4 12.0 12.2 12.7 13.5 14.5 14.7 17.1 19.1 19.3
7.0 3.6 27.9 7.6 4.6 2.2 11.2 33.3 0.6 2.0
a
(27) American Society for Testing and Materials. ASTM D 445 - 06: Standard Test Method for Kinematic Viscosity of Transparent and Opaque Liquids (and Calculation of Dynamic Viscosity); ASTM International: West Conshohocken, United States, 2006. (28) Pregnolatto, W.; Pregnolatto N. P. (coordinators). Normas Analı´ticas do Instituto Adolfo Lutz: M etodos quı´micos e fı´sicos para an alise de alimentos, 3a ed.; Instituto Adolfo Lutz: S~ao Paulo, Brasil, 1985. (29) American Society for Testing and Materials. ASTM D664: Standard Test Method for Acid Number of Petroleum Products by Potentiometric Titration; ASTM International: West Conshohocken, United States, 2006. (30) British Standard. European Standard EN14103 for Fat and oil derivatives. Fatty acid methyl esters (FAME). Determination of ester and linolenic acid methyl ester contents; Brussels, Belgium, 2003 (31) American Society for Testing and Materials. ASTM D93: Standard Test Method for Flash-Point by Pensky-Martens Closed Cup Tester; West Conshohocken, United States, 2006.
CN = carbon No.; DB = double bond
and with heating (Fisatom heating mantle). Temperature control was performed by a microprocessor controller (CONTEMP CTM45). All the experiments were carried out with 60 g of oil. Sodium ethylate was added to the ethanol, and the solution was mixed with the oil, previously heated in the kettle reactor to reaction temperature. The system was kept at constant temperature and under agitation throughout the reaction. At the end of the reaction, the products were put in a separator funnel, for a (32) American Society for Testing and Materials. ASTM D6371: Standard Test Method for Cold filter Plugging Point of Diesel and Heating Fuels; West Conshohocken, United States, 2006.
5221
Energy Fuels 2009, 23, 5219–5227
: DOI:10.1021/ef900468p
Valle et al.
Table 3. 2(5-1) Fractional Factorial Design: Variable Selection a
identification of the experiment FFD -15 FFD - 3 FFD -18 FFD - 6 FFD - 4 FFD - 7 FFD - 6 FFD - 17 FFD -13 FFD - 5 FFD - 19 FFD - 10 FFD - 9 FFD - 8 FFD - 11 FFD - 14 FFD - 2 FFD - 12 FFD - 1 a
T (°C)
t (min)
MR (EtOH/Oil)
speed (rpm)
cat. (% w/w)
ester content (% w/w)
36 60 36 60 36 60 36 60 36 60 36 60 36 60 36 60 48 48 48
10 10 110 110 10 10 110 110 10 10 110 110 10 10 110 110 60 60 60
6 6 6 6 14 14 14 14 6 6 6 6 14 14 14 14 10 10 10
500 500 500 500 500 500 500 500 3200 3200 3200 3200 3200 3200 3200 3200 1850 1850 1850
1.5 0.9 0.9 1.5 0.9 1.5 1.5 0.9 0.9 1.5 1.5 0.9 1.5 0.9 0.9 1.5 1.2 1.2 1.2
88.10 93.90 90.70 98.90 89.40 94.70 87.70 94.40 94.70 92.00 86.70 77.10 86.60 85.70 91.30 89.50 92.90 91.50 92.70
FFD - No. refers to the random experiment in the fractional factorial design.
minimum of 4 h, after which the ester phase was recovered and purified using the process described in the following paragraph. To purify the esters, a variable amount of alcoholic solution of citric acid 0.5 mol 3 L-1 was added to the biodiesel in a separator funnel and the solution was vigorously stirred. After that, the product was washed several times, sprinkling hot distillated water on the surface until the water was clear and translucent. Then, the solution was centrifuged until the esters phase became translucent. Finally, the purified product was collected, weighed, stored in nitrogen atmosphere, and refrigerated until analysis. All the analyses were carried out in triplicate, and the average was reported as the result. The ester content in the samples was determined by chromatography as follows: approximately 100 mg of biodiesel were weighed in a calibrated analytical balance. A stock solution (19.2 mg/L-1) of internal standard methyl heptadecanoate (>99%, Sigma-Aldrich) was prepared. Then 500 μL of the stock solution were added to the sample. After that, n-heptane was added to the vial until totaling a mass of 5 g. The biodiesel samples were analyzed by gas chromatography using GC-17 Shimadzu equipment, with a flame ionization detector (FID). The capillary column used was a Carbowax (30 m 0.32 mm 25 μm) of poliethylenoglycol stationary phase. Helium gas carrier flow was 1.45 mL/min. Chromatographic analyses were performed under the following conditions: split ratio = 50; injector temperature = 230 °C; detector temperature = 240 °C; initial column temperature=180 °C; slope of 7 °C/min until 230 °C and the final temperature was maintained for 9 min. Experiments. The variables of the process that affect the synthesis of biodiesel catalyzed by sodium ethylate are: temperature, time, molar ratio alcohol/oil, concentration of the catalyst, and level of agitation of the reagents (in this work, agitation was provided by a mechanical stirrer). During the first stage of the work, these five variables were studied in a 2(5-1) FFD. A total of 19 experiments were carried out randomly: 16 experiments of the design structure plus 3 repetitions in the central point, as shown in
Table 4. Effect of Variables on Ester Content in Synthesis Products, According to the Fractional Factorial Design effectsa SG
SG
SG SG SG SG
average T T MR speed cat Tt T MR T speed T cat t MR t speed t cat MR speed MR cat speed cat
90.45 1.38 -1.10 -0.35 -4.28 0.87 -0.50 0.95 -5.13 5.13 2.73 -2.50 1.45 1.00 -1.45 0.63
uncertainty
tcalb
P-value
(0.17 (0.38 (0.38 (0.38 (0.38 (0.38 (0.38 (0.38 (0.38 (0.38 (0.38 (0.38 (0.38 (0.38 (0.38 (0.38
520.68 3.63 2.91 0.92 11.29 2.31 1.32 2.51 13.54 13.54 7.20 6.60 3.83 2.64 3.83 1.65
3.69 10-06 0.068 0.101 0.453 0.008 0.147 0.317 0.129 0.005 0.005 0.019 0.022 0.062 0.118 0.062 0.241
a SG indicates that the effect of the factor is significant. b tcal represents the t-student variable calculated with the ratio between the coefficient and its uncertainty. These values are used by the software to determine the corresponding P-values. A P-value lower than 0.05 means that the effect is significant, in the statistical hypothesis test.
Table 3. In this table, the ester content (% w/w) in the reaction products was used as the response of the design. The experimental domain was defined to enable a broad study of these variables, encompassing the amplitude of variation commonly found in the literature on the synthesis of biodiesel catalyzed by base1,2,4,20,22,25 and after some exploratory experiments in the laboratory. Temperature varied from 36 to 60 °C; time varied from 10 to 110 min, the molar ratio of ethanol/oil varied from 6:1 to 14:1; stirrer speed varied from 500 to 3200 rpm, and catalyst concentration varied from 0.9 to 1.5% w/w. Table 4 points out all those significant factors that most affected the ester content in the products. To conclude that the factor effect is significant, the P-value criterion was used in the statistical hypothesis test. To perform the hypothesis test, the software uses the tcal (calculated t-student variable) in determining the corresponding P-value and compares it with the uncertainty level (R, usually fixed at 5%) of the statistical hypothesis test. (tcal is calculated by dividing the effect of the coefficient by the uncertainty associated with its determination. In this case, the statistical hypothesis test verifies whether the coefficient effect is significantly greater than the uncertainty. The determination of the uncertainty is not part of the scope of this work, but it can be verified in the references listed13,19.)
(33) American Society for Testing and Materials. ASTM D4530: Standard Test Method for Determination of Carbon Residue (Micro Method); West Conshohocken, United States, 2006. (34) American Society for Testing and Materials. ASTM D130: Standard Test Method for Corrosiveness to Copper from Petroleum Products by Copper Strip Test; West Conshohocken, United States, 2006. (35) Associac-~ ao Brasileira de Normas Tecnica. NBR15556 para Pro dutos derivados de oleos e gorduras - Esteres metı´licos/etı´licos de acidos graxos - Determinac-a~o do teor de s odio, pot assio, magn esio e c alcio por espectrometria de absorc-a~o at^ omica; Rio de Janeiro, Brasil, 2008 (36) British Standard. European Standard EN1411 for Fat and oil derivatives. Fatty acid methyl esters (FAME). Determination of iodine value; Brussels, Belgium, 2003
5222
Energy Fuels 2009, 23, 5219–5227
: DOI:10.1021/ef900468p
Valle et al.
Table 5. Doehlert Design with Three Variables expected valuesb product a
Identification of the experiment D3V - 3 D3V - 11 D3V - 7 D3V - 2 D3V - 13 D3V - 9 D3V - 8 D3V - 4 D3V - 10 D3V - 14 D3V - 15 D3V - 6 D3V - 12 D3V - 1 D3V - 5
(R = 5%)
t
T
cat
(g)
(ester % w/w)
(g)
1 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 -1 0 0 0 0 0
0 0.866 0.289 -0.866 -0.289 0.866 0.289 -0.866 -0.289 0 0.577 -0.577 0 0 0
0 0 0.817 0 -0.817 0 0.817 0 -0.817 0 -0.817 0.817 0 0 0
61.50 50.48 59.31 58.70 60.58 56.11 54.41 58.18 63.54 60.51 61.31 60.08 62.67 63.22 62.08
93.37 89.23 85.17 97.41 86.38 86.51 85.02 90.07 83.10 87.10 85.03 86.71 87.94 89.10 88.68
62.66 52.32 60.18 60.24 61.75 55.39 56.25 57.17 65.68 62.66 62.11 61.47 62.66 62.66 62.66
(R = 5%)
(R = 5.9%)
(ester % w/w) 91.83 89.65 85.59 94.38 87.17 86.40 82.34 91.13 83.92 85.32 83.78 86.94 88.57 88.57 88.57
91.83 88.49 85.21 95.54 87.56 87.55 82.73 89.97 83.53 85.32 83.78 86.94 88.57 88.57 88.57
a D3V - No. refers to the random experiment in the Doehlert design with three variables. b R refers to the critical uncertainty level in the statistical hypothesis test; conditions: MR ethanol/oil = 6:1; stirrer speed = 500 rpm.
Table 6. Correspondence between Coded and True Values in the Doehlert Design with Three Variables negative levels variables
1
t (min) T (°C) cat (% w/w)
6
-0.866
-0.817
-0.577
PC -0.5
-0.289
0
37
38 40 1.3
22 30
33 0.9
Every time the P-value is lower than R, the statistical hypothesis test is considered significant. In statistical hypothesis test, the P-value can be understood as the maximum uncertainty in assuming that the test is significant. Therefore, the lower than R, P-value is, the more significant the statistical hypothesis test is. To better understand the statistical hypothesis test and the use of P-value in determining if the test is significant or not, some references should be consulted.13,14,17,19 In the FFD, time, temperature, and concentration of the catalyst were selected and then reorganized in Table 5, according to the structure proposed by the Doehlert design with three variables, in order to obtain a response surface and to determine the optimum synthesis conditions in the domain. For the second stage, the Doehlert design was chosen because its structure demands fewer experiments than other designs (see Table 1) and allows studying each variable in different levels. The limits of variation of the selected factors suffered minor adjustments compared to the variation limits used in the first stage, so that this new experimental domain could comprise a region of satisfactory results. The new ranges of the variables are shown in Table 6: time varied from 6 to 70 min; temperature varied from 30 to 50 °C, and the catalyst concentration varied from 0.9 to 1.7% w/w, with a central point at 38 min, 40 °C, and 1.3 (% w/w). The molar ratio of ethanol/oil and the speed of the mechanical stirrer were kept at 6:1 and 500 rpm, respectively. In Table 5, the levels of the factors are coded to make recognition of the structure of the Doehlert design easier for the reader. Equation 1 shows the functional relationship between coded and true values of the variables, in which: C= coded value, X= true value, X0 = true value at the central point, ΔX = difference between maximum true value and central point, and R = maximum positive coded value. The correspondence between coded and true values of the variables is listed in Table 6. ð1Þ C ¼ ððX - X0 Þ=ΔXÞ R
positive levels 0.289
0.5
0.577
0.817
0.866
54 43
1 70
47
50 1.7
Table 7. Significance of the Quadratic Model Coefficients Created for the Recovered Mass of Purified Product coefficientsa
uncertainty
average 62.66 (0.33 t -0.15 (0.29 SG T -2.80 (0.29 SG C -2.37 (0.28 2 -1.65 (0.52 t -8.50 (0.52 SG T2 -1.64 (0.49 C2 SG tT -3.55 (0.66 SG tC 6.07 (0.74 SG TC -4.16 (0.74 value of R in the test of hypothesis: SG
tcalb
P-value
190.35 0.52 9.83 8.32 3.17 16.34 3.32 5.39 8.25 5.65
2.76 10-05 0.654 0.010 0.014 0.087 0.004 0.080 0.033 0.014 0.030 0.05
a SG indicates that the coefficient is significant. b tcal represents the t-student variable calculated with the ratio between the coefficient and its uncertainty. These values are used by the software to determine the corresponding P-value. A P-value lower than 0.05 means that the coefficient is significant in the statistical hypothesis test.
purified, recovered, and weighed.) of purified product in each synthesis, and the ester content in these products, similar to what Vicente et al.20 did. To test the predicting ability of the created models, a new synthesis was performed under the following conditions (different from the conditions of the experiments used to build the models): temperature = 30 °C; time = 70 min; catalyst = 1.3% w/w; molar ratio = 6:1, and stirrer speed = 500 rpm. The amounts of reagents used in this synthesis were: 60 g of fodder radish oil, 3.9632 g of sodium ethylate solution (21% in ethanol), and 15.2899 g of absolute ethylic alcohol. From the total sodium ethylate solution put in the reactor, 0.2489 g were used to neutralize the natural acidity (0.7182 mgKOH/goil) of the fodder radish oil. To determine the total mass of ethanol in the reactor, the 15.2899 g of ethanol need to be added to 3.1309 g of ethanol contained in the sodium ethylate solution used. Due to the lack of statistical software that applies to the Doehlert design, the data was treated in electronic spreadsheets developed
In this stage, two answers were chosen for the construction of two regression models: the recovered mass (the product of the reaction (biodiesel) after being separated from the glycerin, 5223
Energy Fuels 2009, 23, 5219–5227
: DOI:10.1021/ef900468p
Valle et al.
Table 8. ANOVA of the Quadratic Model Created for the Recovered Mass of Purified Product source of variation
SSa
SG regression 168.98 residual 8.87 lack of adjustment 8.22 experimental uncertainty 0.65 total 177.84 % of explained variance: max. % of explainable variance:
DFb 9 5 3 2 14
MSc
Fcald
18.78 10.59 1.77 2.74 8.43 0.33
Table 9. Significance of the Quadratic Model Coefficients Created for Ester Content in Biodiesel coefficientsa
P-value
uncertainty
average 88.57 (0.34 t 3.25 (0.29 T -2.73 (0.29 C 0.49 (0.29 1.66 (0.54 t2 2.42 (0.54 SG T2 -6.02 (0.51 SG C2 tT -2.67 (0.68 tC -0.97 (0.76 TC -1.08 (0.76 value of R in the test of hypothesis:
0.009
SG SG SG
0.108
95.01 99.63
a SS=sum of squares b DF=degree of freedom. c MS=mean square. Fcal=F variable calculated with the ratio between mean squares. These values are used by the software to determine the corresponding P-values. A P-value lower than 0.05 means that the source of variation is significant in the statistical hypothesis test. In the ANOVA it is expected that the lack of adjustment is not significant and the regression is. d
tcalb
P-value
261.21 11.08 9.31 1.67 3.10 4.52 11.85 3.93 1.28 1.42
1.5 10-05 0.008 0.011 0.238 0.090 0.046 0.007 0.059 0.328 0.290 0.05
a SG indicates that the coefficient is significant. b tcal represents the t-student variable calculated with the ratio between the coefficient and its uncertainty. These values are used by the software to determine the corresponding P-value. A P-value lower than 0.05 means that the coefficient is significant in the statistical hypothesis test.
by the Laboratory of Chemometry in Analytical Chemistry LAQQA/UNICAMP.19 Analysis of the Variance (ANOVA). In the ANOVA, the maximum explainable variance is determined by the difference between the total sum of squares and the sum of squares of the experimental uncertainty, and this difference is divided by the total sum of squares. The maximum explainable variances equal to 99.63% in Table 8 and equal to 99.62% in Table 10 were determined this way. The part of this maximum explainable variance, which can be explained by the regression model, is obtained by dividing the sum of squares of the deviations of the estimated values (regression model) by the total sum of squares. This way, we obtained the explained variance equal to 95.01 and 93.73%, respectively, shown in Tables 8 and 10. The variance explained by the regression model is defined as coefficient of determination (R2). The coefficients of determination are also indicated in Figures 2a and 2b. In the ANOVA, two hypotheses tests are performed. In the first one, the variance or mean square (sum of squares divided by the degree of freedom), which represents the lack of adjustment of the regression model, is compared with the variance that represents the experimental uncertainty. If the test is significant, the variance of the lack of adjustment is significantly higher than the variance of the experimental uncertainty and, therefore, the regression model does not fit well to experimental data. In that case it is expected that the test will not be significant. The second hypotheses test compares the variance of the values estimated by the regression model and the total variance. Similar to the first test, if the result is significant, this means that the variance of the values estimated by the regression model is significantly higher than the residual variance and, therefore, the model will be able to estimate values within the experimental domain researched. In the second test, as opposed to what was expected in the first test, we expect the test to be significant. To carry out the hypothesis test in ANOVA, the P-value was also used as a criterion. In this case, the software uses the value of Fcal (ratio between two variances being compared) to calculate the corresponding P-value and compares it with the uncertainty level R for the test (usually fixed at 5%). If the P-value is less than R, the software will conclude that the test is significant. This means that the ratio between the two variances being compared is significantly large. For Montgomery,14 the use of P-value in the hypothesis tests in the ANOVA is more advantageous than the other criterion also used in hypothesis tests, in which the analyst compares Fcal with the corresponding listed value of F. This superiority of the use of P-value in hypotheses tests is due to the fact that it represents the maximum uncertainty in stating that the test is significant.
Table 10. ANOVA of the Quadratic Model Created for Ester Content in Biodiesel source of variation
SSa
SG regression 170.45 residual 11.41 lack of adjustment 10.72 experimental uncertainty 0.69 total 181.85 % of explained variance: max. % of explainable variance:
DFb 9 5 3 2 14
MSc
Fcald
18.94 8.30 2.28 3.57 10.35 0.34
P-value 0.016 0.089
93.73 99.62
a SS=sum of squares. b DF=degree of freedom. c MS=mean square. Fcal=F variable calculated with the ratio between mean squares. These values are used by the software to determine the corresponding P-values. A P-value lower than 0.05 means that the source of variation is significant in the statistical hypothesis test. In the ANOVA it is expected that the lack of adjustment is not significant and the regression is. d
Figure 2. Expected values vs observed values: (a) model obtained for recovered mass of purified product and (b) model obtained for ester content in the reaction products.
quantities at a time. By using it, 22% of the oil in the fodder radish seeds was recovered. The analyses carried out with this oil presented the following results: moisture content equaled 0.079%; acid number equaled 0.7182 mgKOH/goil (which corresponds to approximately 0.37% w/w of free fatty acids);
3. Results and Discussion A manual 15 T Charlott press showed to be suitable for laboratory use because it enabled the extraction of small 5224
Energy Fuels 2009, 23, 5219–5227
: DOI:10.1021/ef900468p
Valle et al.
specific gravity at 20 °C equal to 911.8 kg/m , and kinematic viscosity at 40 °C equal to 41.059 mm/s2. Moisture content and the percentage of free fatty acids are in line with the suggestions given by Knothe et al.,25 for the use of basic catalysts in transesterification reactions. However, the use of sodium ethylate has an advantage over the use of hydroxides: it reduces the production of water in the reactor and it renders the chemical reaction more efficient. According to the results in Table 4, we observed that stirrer speed was the variable with greater negative effect (-4.28) on the ester content in the product of the reaction, switching stirrer speed from 500 to 3200 rpm. In addition, because the P-value (0.008) is lower than 0.05, the statistical hypothesis test confirms that this effect is significant or, in other words, the effect of stirrer speed is significantly greater than the uncertainty associated with its determination. This negative effect was repeated in a significant manner in the interaction of the stirrer speed with temperature (-5.13) and with reaction time (-2.5), thus suggesting that an extremely vigorous agitation should be avoided to ensure high performance in biodiesel synthesis. The uncertainty in stating that the effects of these interactions are significant is low, because the respective P-values (0.005 and 0.022) are lower than 0.05. On the other hand, molar ratio of ethanol/oil was the variable that least (-0.35) influenced the result, varying from 6:1 to 14:1, and this effect is not significant, because the P-value (0.453) is much greater than 0.05. Despite the fact that the effect of the interaction of the molar ratio with time (2.73) is great and the P-value (0.019) is lower than 0.05, the effect of the interaction with temperature (0.95) is not important, and the P-value (0.129) is greater than 0.05. Among the remaining variable combinations, the interaction between temperature and catalyst concentration had a great positive influence (5.125) on the performance of the reaction (P-value=0.005). Consequently, the three variables selected to be studied in the Doehlert design in order to search for the optimum synthesis conditions were as follows: temperature (T), reaction time (t) and catalyst concentration (C). At this stage of the work, molar ratio ethanol/oil and the stirrer speed were kept unchanged at levels 6:1 and 500 rpm, respectively. The results of the Doehlert design in Table 5 were used by the electronic spreadsheets (LAQQA/UNICAMP) and, as a result, eqs 2 and 3 as well as Tables 7-10 were obtained. Equation 2 describes the mathematical relation between the recovered mass (g) of purified product and the coded (see eq 1) process variables, whereas eq 3 describes the same functional relation for ester content (% w/w) in these products. (In eq 3, the term -2.667tT was included, because the P-value (0.059) is only slightly greater than 0.05.) 3
Figure 3. Response surfaces based on significant coefficients of the models for sodium ethylate concentration equal to 1.3% w/w: (a) model obtained for recovered mass of purified product; (b) model obtained for ester content in the products.
and for ester content) on the models. The negative effects persisted when the temperature interacted with the time (-3.55 and -2.67) or catalyst concentration (-4.16 and -1.08). This phenomenon may indicate that the increase in temperature tends to favor reactions concomitant with the oil transesterification, creating undesirable products, such as soaps, and thus reducing the efficiency in the biodiesel synthesis. According to Table 9, the increase in reaction time had a positive effect (3.25) on the ester content in the products with P-values equal to 0.008. The term representing the quadratic influence of the catalyst is very negative (-6.02), and the P-value is 0.007. The negative influence of a quadratic term in a model suggests that there is a specific value of the variable for maximum response of the model. Solving the homogeneous differential equations system (created by the partial derivates of eq 3) we attain an optimal concentration of sodium ethylate equal to 1.3% w/w. On the other hand, as shown in Table 9, the concentration of catalyst was the variable that least affected (0.49) the result of the model developed for the ester content in reaction products, and the P-value was greater than 0.05. Therefore, in order to represent the response surfaces obtained from the models in a graph, the concentration of the catalyst was fixed at 1.3%, as shown in Figures 3a and b.
Mass ¼ 62:66 - 2:80T - 2:37C - 8:50T 2 - 3:55tT þ 6:07tC - 4:16TC
ð2Þ
Ester % ¼ 88:573 þ 3:254t - 2:733T þ 2:422T 2 - 6:020C 2 - 2:667tT
ð3Þ
Tables 7 and 8, respectively, introduce the significance of the coefficients or terms and the ANOVA of the lineal regression quadratic model obtained for the recovered mass of purified product, whereas Tables 9 and 10 show the same content regarding the model obtained for the ester content in that product. Analyzing Tables 7 and 9, it can be observed that the increase in temperature had negative effects (-2.80 and -2.73, respectively, for recovered mass of purified product 5225
Energy Fuels 2009, 23, 5219–5227
: DOI:10.1021/ef900468p
Valle et al.
Figure 4. Intersection of level curves based on expected values by significant coefficients of the Doehlert design models, for sodium ethylate concentration equal to 1.3% w/w. The curves in (g) represent the recovered mass of purified product and the curves in (%) represent the ester content (% w/w) in the reaction product. Table 11. Expected Values and Results of the Synthesis Performed to Test Modelsa recovered mass
ester content
model (a = 5%) (g)
experiment (g)
model (a = 5.9%) (% w/w)
experiment (% w/w)
61.78
>60.5 b
98.3 c
97.9
Conditions: T = 30 °C; t = 70 min; C = 1.3%; MR = 6:1; rot. = 500 rpm. b Due to a slight accidental loss of product during recovery; c For R = 5%, estimated content reduces to 96%. a
3.9632 g of sodium ethylate solution (21% in ethanol), and 15.2899 g of absolute ethanol. The recovered mass of biodiesel after purification was slightly greater than 60.5 g. The gas chromatography analysis of a sample of this biodiesel showed approximately 97.9% of ester content. These results agreed well with the values predicted by the models: 98.3% (for R = 5.9%) and 96% (for R = 5.0%) of ester content and a recovered mass equal to 61.78 g of purified biodiesel (see Table 11). Table 12 shows some physical chemistry properties of the biodiesel from fodder radish oil obtained in our experiments that were performed according to the respective standard methods. All these analyses were carried out in triplicate, and the reported values are the average of the results. These results are in accordance with Brazilian legislation for biodiesel commercialization in the country and are strong evidence of the good quality of the product. The high ester content in the purified product (97.9%) shows that the reaction was very efficient, and it is important for the high performance of diesel engines. The specific gravity of 872.1 kg/m3 and the kinematic viscosity of 5.511 mm2/s guarantee that the fuel flows smoothly through the engine; it avoids excessive compression in the fuel injection pump and provides appropriate atomization in the combustion chamber, thus contributing to an efficient burning and to the low carbon deposits in the combustion area. This is confirmed by the result (0.03% w/w) of the carbon residue test. The flash point was 157.2 °C. It indicates the temperature at which fuel vapors create a flammable mixture with atmospheric air, and it is important for safety in transport, storage and handling of fuel. According to Brazilian laws, when flash point value is higher than 130 °C, the content of alcohol does not need to be determined. In this work, although the alcoholic content was not determined, the low values of Na (1.774 mg/kg) and
In accordance with the variance analyses of these models, shown in Tables 8 and 10, the P-values equal to 0.108 and 0.089 indicate that the hypothesis test applied to the lack of adjustment in both models is not significant. In other words, this means that the obtained models are aligned with the experimental data. This is confirmed in Figures 2a and 2b, where the expected values versus observed results presented coefficients of determination R2 equal to 0.9501 and 0.9373, respectively. (Although some terms of the models were not significant in the statistical hypothesis test, the electronic spreadsheets used all of them in adjusting lines between expected values and experimental results.) The good fit of the models, together with the low experimental random uncertainty (represented by sum of squares equal to 0.65 and 0.69) contributed to high significance of the regressions. In the hypothesis test comparing the variances (mean squares) of the values estimated by the regression models with the residual variances, the electronic spreadsheets obtained P-values equal to 0.009 and 0.016. This means that the variances of the regressions are significantly greater than the residual variances, thus giving good abilities to models to estimate results within the researched experimental domain. In the experimental domain researched, the optimum synthesis conditions were established by analyzing Figure 4, which shows the intersection of the level curves obtained from the models. The small area on the upper left of the graph (over the level curve of 98% w/w) suggests the best results, indicating that ethanolysis of fodder radish oil, using sodium ethylate as a catalyst, may be carried out under the following conditions: 1.3% w/w of catalyst, 30 °C and 70 min. As it was stated before, the experiment performed to test the optimum conditions predicted by the lineal regression quadratic models was carried out as follow: temperature = 30 °C; time = 70 min; sodium ethylate catalyst = 1.3% w/w; molar ratio ethanol/oil = 6:1, and stirrer speed = 500 rpm. The amounts of reagents used were: 60 g of fodder radish oil, 5226
Energy Fuels 2009, 23, 5219–5227
: DOI:10.1021/ef900468p
Valle et al.
Table 12. Phisical Chemistry Properties of Biodiesel from Fodder Radish Oil properties aspect ester content specific gravity at 20 °C kinematic viscosity at 40 °C flash point cold filter plugging point carbon residue copper corrosion, 3 h at 50 °C Na þ K Ca þ Mg P acid number iodine value
Brazilian specification
results a
(% w/w) (kg/m3) (mm2/s) (°C) (°C) (% w/w) (mg/kg) (mg/kg) (mg/kg) (mg KOH/g) (g/100 g)
minimum 96.5% 850-900 3.0 - 6.0 minimum 100b maximum 19 0.05 maximum 1 maximum 5 maximum 5 maximum 10 maximum 0.5 report
LII 97.9 872.1 5.511 157.2 5 0.03 1a 1.774c 1.509 0.446 0.21 96
methodology visual EN 14103 ASTM D 4052 ASTM D 445 ASTM D 93 ASTM D 6371 ASTM D 4530 ASTM D 130 ABNT NBR 15556 ASTM D 664 EN 14111
a Limpid and free from impurities. b If the flash is higher than 130 °C, it will not be necessary to determine the ethanol content. c This figure refers to Ca, because K was not determined.
Ca þ Mg (1.509 mg/kg), as well as of the acid value (0.21 mg KOH/g) confirm the efficiency of the purification process, which aimed at removing the excess of alcohol, acid, and salts, as well as soaps and catalyst residues in the biodiesel. Copper corrosion test (1a) showed the low corrosiveness of the fuel, hence guaranteeing the integrity of the metallic parts of the engine. This result reveals the absence of sulfur components in the biodiesel Finally, the cold filter plugging point is a measure of the performance of fuel at low temperatures, and it is used as a handling indicator at low temperatures in the big flow tanks and in refinery and terminal pipelines. The result equal to 5 °C obtained for biodiesel from oilseed radish was far below the maximum limit (19 °C) established by Brazilian legislation.
results of the previous stage. The selected variables were studied in different levels in the Doehlert design of experiments and it showed to be very efficient (because it demanded few experiments) to find the optimum conditions (within the experimental domain studied) for the transesterification of fodder radish crude oil. High temperatures, together with high concentrations of the catalyst and/or extended reaction times, tended to reduce the ester content in the products of the transesterification reaction. One of the reasons for this observation may be that these conditions can promote the formation of unwanted products (such as soaps), due to the occurrence of reactions that compete with the transesterification. The optimum synthesis conditions obtained within the researched experimental domain were: reaction time = 70 min, reaction temperature = 30 °C, catalyst concentration= 1.3% w/w, with a molar ratio of ethanol/oil = 6:1, and a stirrer speed of 500 rpm. A very high stirring speed (over 500 rpm) was disadvantageous for the efficiency of the reaction. In these conditions, the ester content in biodiesel was 97.9% w/w. This ester content and all the other physical chemistry properties are in conformity with Brazilian regulation for commercial biodiesel and are strong evidence that the synthesis of biodiesel from fodder radish crude oil can be carried out efficiently in a single step.
4. Conclusions On the basis of the results achieved, it may be concluded that the use of sodium ethylate should be considered an interesting catalyst to the ethanolysis of fodder radish crude oil (Raphanus sativus L. var. oleiferus Stokes) because it reduces water production during the reaction and increases the efficiency of the transeseterification. All the variables that affect the synthesis of biodiesel were studied: temperature, time, molar ratio, stirrer speed, and catalyst concentration. In the first stage of the work, the use of the 2(5-1) FFD showed that it is efficient in helping to select the most important variables, and allowed the reduction of the number of experiments at this stage. For the next stage, time, temperature, and catalyst concentration were selected and the other two factors were fixed at levels defined based on the
Acknowledgment. The authors are grateful to G^enesis Sementes Armazens Gerais Ltda for providing the fodder radish seeds used in the experiments, to FINEP (CTPetro) and FAPEMIG (Soldiesel) for financing the infrastructure, and to Laborat orio de Ensaios de Combustı´ veis of the Universidade Federal de Minas Gerais (LEC/UFMG), for the research grant.
5227
