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Factorial design

esti. Ftim60n pOHiononeste

scaling-up the process by predicting its reaction yield within a 95% of condence level.2014 International Energy Initiative. Published by Elsevier Ltd. All rights reserved.

easingolicy freing bi(Ouachl., 2013has 12

ted, an additional in-rd is a by-product ofedstock in the animalase had a detrimental

Energy for Sustainable Development 23 (2014) 110114

Contents lists available at ScienceDirect

Energy for Sustainacooking oils, animal fats and grease range from 0.3 to 0.7, 2 to 7, 5 to30 and 40 to 100%, respectively. However, the high amounts of FFAs inthese low cost biodiesel feedstocks make them unsuitable for base-

effect on the use and the prices of these animal origin raw materials.Therefore, nowadays biodiesel industry can provide a stable and securealternative market for these rawmaterials, which can contribute also toble and rened oil products (Alamu et al., 2007; Che et al., 2012;Sarantopoulos et al., 2009), which are used to produce a cost-competitive product.

The free fatty acid (FFA) contents of crude vegetable oils, used

waste management schemes will be implemencrease in the production is expected. Waste lapork meat industry and until now is used as fefeed industry. However, the risk of animal diseproduction cost (Atabani et al., 2012). In recent years, systematic ef-forts have been made to produce biodiesel through the utilizationof waste oils, such as used cooking oils (Wyse-Mason and Beckles,2012; Math et al., 2010; Talebian-Kiakalaieh et al., 2013; Lee et al.,2002), animal fats (Kleinov et al., 2011; Berrios et al., 2009), vegeta-

process for biodiesel production from oils with high FFA content(Talebian-Kiakalaieh et al., 2013).

Waste lard is a by-product of hog industry with limited other usesand low market price. Availability is already high, the hog industry inGreece consumes about 120M kg of pork (GMRDF, 2014), and if proper Corresponding author. Tel.: +30 2821037825.E-mail address: theocharis.tsoutsos@enveng.tuc.gr (T.

http://dx.doi.org/10.1016/j.esd.2014.08.0050973-0826/ 2014 International Energy Initiative. Publish% lower energy content,4% during combustion,tion stability and higher

FFAs and triglycerides (TGs) to FAME by esterication, before thetransesterication process. This method is known as two-step acidbase catalyzed transesterication and is designed as an alternativehigher NOx emissions in the range of 101needs higher injector pressure, lower oxidaIntroduction

Recently, biodiesel has received incrto the existing sustainable energy ppresents unique advantages such as bdomestic production and non-toxicTsoutsos et al., 2010; van Stralen et acomparison to petrodiesel, biodieselattention worldwide dueamework. Moreover, itodegradable, suitable forab and Tsoutsos, 2013;). On the other hand, in

catalyzed transesterication, due to the soap formation between FFAsand the base catalyst (Vicente et al., 2004). The presence of soap canprevent the separation of biodiesel from glycerol and cause the forma-tion of emulsions (Endalew et al., 2011; Marchetti et al., 2007). Thisleads to an increase in the product viscosity and in the joint cost ofseparation and purication. The saponication can be avoided throughthe acid-catalyzed process because it can simultaneously transformOptimization of biodiesel production fromtransesterication process under mild con

Ioannis Sarantopoulos a, Efthalia Chatzisymeon b, Spyra School of Environmental Engineering, Technical University of Crete, Polytechneioupolis, GR-7b Institute for Infrastructure and Environment, School of Engineering, The University of Edinbu

a b s t r a c ta r t i c l e i n f o

Article history:Received 17 September 2013Revised 5 July 2014Accepted 12 August 2014Available online xxxx

Keywords:Waste lardBiodieselTwo-step transesterication

The aim of thiswork is to invlow cost biodiesel productioniables, namely estericationtransesterication time (30typically affect the productiothe reaction time and the MeLikewise, the transestericataction time, KOH concentratilution of the two-step transTsoutsos).

ed by Elsevier Ltd. All rights reserveaste lard by a two-steptions

Foteinis a, Theocharis Tsoutsos a,Chania, Greece

Edinburgh EH9 3JL, United Kingdom

gate a two-step homogenous catalyzedwaste lard transesterication reaction foror this purpose, two 23 full factorial design of experiments was applied. Six var-e (60120 min), H2SO4 concentration (2040 wt.%), MeOH:FFA (15:123:1),min), KOH concentration (12 wt.%), and MeOH:triglycerides (6:19:1), thatrocess were studied. The esterication step is signicantly affected mostly by:FFA ratio value. Specically, their increase brings a reduction of the FFA acidity.step is positively affected primarily by three independent variables, namely re-andMeOH:triglyceride ratio. Furthermore two empirical models describing evo-rication reaction were developed. They can become useful tools for further

ble Developmentthe sustainability of the biodiesel fuel use.Hence, the aim of this work is to optimize the two-step homoge-

neous catalyzed transesterication, with the ultimate purpose ofproducing biodiesel from a low-cost feedstock, such as the waste lard,under mild operating conditions. For this purpose, a factorial design of

d.

experiments (DOE) methodology was utilized and six variables thattypically affect the two-step homogeneous catalyzed transestericationreaction were investigated.

Materials and methods

Raw material

Dehydrated lard was provided by a meat industry company locatedin Rethymnon, Crete, Greece. The feedstockwas used as it was obtained

high for animal fats. This may be attributed to the presence of various

mersed in hot water bath at 50 1 C. The reactor was preserved

Biodiesel quality determination

After the two-step transesterication process and optimal operatingparameters, the produced biodiesel was analyzed in terms of its qualityparameters. Therefore, this biodiesel sample was further puried toachieve highest quality characteristics. For this purpose crude biodieselwas centrifuged at 4000 rpm for 5min and the supernatant glycerolwasremoved. Afterwards biodiesel was submitted to wet washingwith dis-tilled water of ~50 C to remove any impurities. Further puricationtook placewith a commercial dry washing processwithmagnesium sil-icate as solid absorbent. A 2wt.% ratio ofmagnesium silicate to biodiesel

Palmitoleic acid C16:1 1.5

111I. Sarantopoulos et al. / Energy for Sustainable Development 23 (2014) 110114Stearic acid C18:0 11.5Oleic acid C18:1 40.1Linoleic acid C18:2 21.7Linolenic acid C18:3 1.5Others 2.3under a water-cooled condenser for recovering methanol vapors andunder continual magnetic stirring.

Transesterication methodTransesterication tests were conducted in a 500 mL spherical

three-neck reaction ask, which was immersed in hot water bath at50 1 C and 50 g of the esteried lard and the appropriate KOHamount were loaded in it. The reactor was preserved under a water-cooled condenser for recovering methanol vapors and under continualmagnetic stirring. Periodically (at 30 and 60 min) samples of 5 mLwere collected and their methyl-ester content was measured.

Table 1Fatty acid prole of waste lard raw material.

Fatty acid Class wt.%

Myristic acid C14:0 1.0Palmitic acid C16:0 21.1vegetable oils, with a C18:2 content higher than 20% (Mamat et al.,2005), in the lard products.

Two-step batch process

Acid esterication stepFeedstock with acidity above 1% cannot be converted efciently to

biodiesel with the conventional alkaline process. If FFA content isabove this limit, soap formation decreases catalyst activity and inhibitsthe reaction rate and time for completion. Moreover, water formationis causing more soap formation by hydrolyzing the triglycerides tofree fatty acids. Increased soap content in reaction mixtures alsodecelerates glycerol separation and increases settling time for adequateglycerol removal.

Esterication catalyzed by homogeneous acid catalyst is a commonway for converting FFA to esters. After this pretreatment step, thereaction mixture has a low acidity and can be followed by a commonalkaline transesterication so as to convert triglycerides to the desirednal product. For the experimental tests, 50 g of rawmaterialwere load-ed into a 500 mL spherical three-neck reaction ask, which was im-without any further rening treatment prior to the experimentalprocess. This waste lard had an initial acid value of 13.06 mg KOH g1

corresponding to a FFA level of 6.56% and its fatty acid composition, asdetermined by gas chromatography, is shown at Table 1. It should benoted that in the specic industry vegetable oils are used as animal fatsubstitutes and are incorporated into the preparation process of meat-based products to improve their avor and their dietary content(Domazakis, 2010). This is reected in lards' fatty acid content present-ed in Table 1. For example, C18:2 are 21.7% in lard, which is relativelywas stirred for 15 min at 60 C. Finally, biodiesel was ltered through1 m lters to remove magnesium silicate remnants and other solidimpurities present in the fuel.

Analytical measurements

Gas chromatography (GC) was performed on a Shimadzu (GC-17A),equipped with ame ionization detector (FID) and mounted with aMega Biodiesel 103 0.2 mm 0.25 m 30 m column. Helium wasused as carrier gas. Injector and detector temperatures remained con-stant at 300 C. Temperature program of the column oven started at200 C, then to 240 C at a rate of 5 C/min, held at 240 C for 5 min,to 260 at a rate of 5 C/min and nally, held at 260 C for 5 min.

The acid value of the lard, expressed asmg KOH required to neutral-ize 1 g of biodiesel, was obtained according to the ofcial standardmethod for the determination of acid value and acidity in vegetableand animal oils and fats of the European Committee for Standardization(EN 660:1999).

Total and free glycerol andmono-, di- and triglyceride contentsweremeasured by gas chromatography according to EN 14105 analyticalstandard. Analysis was performed in GC-FID SRI 8610C instrumentmounted with Restek MXT 500 biodiesel gas chromatography columnwith an internal diameter of 0.53 mm, a lm thickness of 0.15 m anda length of 15 m, with helium as carrier gas.

Ester content, linolenic acid methylester and polyunsaturated (N_4double bonds) methyl ester contents were measured according to EN14103 analytical standard procedure. Analysis was performed in aShimadzu GC-FID 17A gas chromatograph, mounted with gas chroma-tography column Mega Biodiesel 103 with an internal diameter of0.32 mm, a lm thickness of 0.25 m and a length of 30 m.

The density at 15 Cwasmeasured with a density meter instrumentAnton Paar DMA 38, calibrated and performed according to ASTMD4052.

Water evaporation from the nal biodiesel product was performedon a rotary evaporator Heidolph Laboretta 4011-digital.

Factorial design and analysis of the experiments

A statistical approach was chosen, based on a factorial experimentaldesign, to infer about the effects of the variables on process perfor-mance. Six independent variables (X1 to X6) that typically affect thetwo-step biodiesel production process efciency were taken intoaccount. Each received a high (+) and a low () value, as seen in

Table 2Independent variables of the two different 23 factorial designs of experiments for eachprocess step.

Esterication step Transesterication step

Value level X1 X2 X3 X4 X5 X6

Time,min

H2SO4, wt %of FFAs

MeOH:FFA

Time,min

KOH, wt %of oils

MeOH:triglyceride

60 20 15:1 30 1 6:1+ 120 40 23:1 60 2 9:10 90 30 19:1 45 1.5 7.5:1

than the reference decision threshold. The effects of the three variables4, X5 and X6 are the most signicant, affecting positively the efciencyof the transesterication reaction. Besides, the effects of the two-order

Table 3Design matrix of the two individual 23 factorial experimental designs and the observedresponse factors Y1 and Y2, during the esterication and the transesterication stepprocesses, respectively.

Esterication step Transesterication step

Run order X1 X2 X3 Y1 Run order X4 X5 X6 Y2

1 + + 92.41 1 76.42 + 84.43 2 + 84.33 + 77.84 3 77.24 + + + 92.33 4 + + 93.95 0 0 0 89.11 5 + + + 97.26 + 75.23 6 + + 93.97 0 0 0 89.42 7 + 83.28 + 86.27 8 + + 91.49 + + 89.88 9 + + 90.810 + + + 94.20 10 + 86.211 + + 88.96 11 + + 91.212 72.47 12 + + + 96.813 + 86.43 13 + 86.914 + + 88.57 14 + + 9115 71.70 15 + 85.316 + + 93.63 16 + 84.1

BC

ABC

AB

B

AC

C

A

2520151050

Term

Standardized Effect

a-level

A X1B X2C X3

Factor Name

Fig. 1. Pareto chart of the effects for the esterication step for the full 23 factorial design.

112 I. Sarantopoulos et al. / Energy for Sustainable Development 23 (2014) 110114Table 2. Analytically, biodiesel production process performance wasevaluated in terms of % transformation yield (response factors Y1 andY2 for the esterication and transesterication steps, respectively). Forthis purpose, two individual 23 full factorial designs were performed.The design matrix of the experiments and their statistical analysiswere performed bymeans of the software packageMinitab 16. Analysisof the response factors Y1 and Y2 involves the estimation of the averageeffect, the main effects of each individual variable, as well as their twoand higher order interaction effects (Box et al., 1978). The presentstudy was done for a condence interval of 95%. An estimate of thestandard error was obtained by repeating all the experimental runs ofthe 23 factorial designs. Although the entire reproducibility of the facto-rial design is time-consuming, it is the safest practice to accurately esti-mate the standard error of the experimental values. Therefore, allexperimental runs were performed in duplicates so as to minimizeexperimental error and increasemodel accuracy. In this case, identica-tion of the important effects was carried out by computing the corre-sponding p-values of the experimental data. If the p-value is 0.05then that effect is not statistically signicant, at the 95% condencelevel. Likewise, the alpha (a-level) value corresponds to the maximumacceptable level of risk for rejecting a true null hypothesis. Hence, alleffects greater than the a-level value, in absolute values, are consideredas statistically signicant. Duplicate experiments were also conductedat the center point of the factorial design (at zero level) to further ensurethe repeatability of the process.

Results and discussion

Effect of operating parameters on process efciency

Table 2 shows the selected parameters and their respective high andlow values for these two DOEs. These parameters were reaction time(X1), H2SO4 concentration (X2) and MeOH-to-FFA ratio (X3), for theacid esterication step. The response was the percentage of the acidityreduction of the FFAs (Y1), according to Eq. (1).

Y1 AVinAV f

AVin 100 1

Where AVin is the initial acidity of the waste lard (mg KOH/g), andAVf is the acidity (mg KOH/g) of the nal mixture after the end of theesterication step.

During the transesterication reaction process variables underinvestigation were reaction time (X4), KOH concentration (X5) andMeOH-to-oil ratio (X6). At this point it should be mentioned that theaverage molecular weight of the lard was estimated based on its FFA'sprole shown at Table 1. In this factorial design the response factor isthe percentage of methyl ester production (Y2), as determined by gaschromatography after sample purication. The above parameters werechosen, as these are typical parameters that affect biodiesel processproduction. However, the effect of all the above parameters and theircombination has not been investigated yet for the two-step biodieselproduction form waste lard. Table 3 shows all the experimental runsperformed in duplicate of the two 23 factorial designs.

A key element in the factorial design statistical procedure is the de-termination of the signicance of the estimated effects. A very usefulpictorial presentation of the estimated effects and their statistical im-portance can be accomplished using the Pareto chart of the effects.This chart displays the absolute value of the effects in a bar chart anddraws a reference line on the chart. Any effect that extends past this ref-erence line (corresponding to -level value) is potentially important.On the other hand, all other effects whose values are lower than thereference can be attributed to random statistical error. The Paretochart of the effects for the esterication step (response Y1) is shown inFig. 1. There are ve effects that are greater than the reference decision

threshold. Among them, the twomost signicant ones are the time (1)and the MeOH:FFA ratio (3), revealing a positive effect on treatmentefciency. This means that an increase in their level brings an increaseon esterication process performance, thus increasing the acidity reduc-tion percentage of the FFAs. Alternatively, it is concluded that the ester-ication is favored at increased treatment time (i.e. 120 min) and highMeOH:FFA ratios, such as 23:1. The variable of H2SO4 concentration af-fects positively but not as high as the other two variables estericationefciency. Moreover, interactions of X1X3 and X1X2 affect negatively Y1the response value. The three latter variables yield a substantiallylower effect on esterication efciency if compared with the X1 and X3effects in absolute values. Hence, research interest should be further fo-cused on the increase of the variables of treatment time andMeOH:FFAratio. Based on the Pareto chart it is derived that the duplicate runs 4and 10 (shown at Table 3) reveal the optimal operating conditions forthe esterication as these were conducted at elevated treatment time,MeOH:FFA ratio and H2SO4 concentration, which effects favor processperformance.

The Pareto chart of the effects for the transesterication step (re-sponse Y2) is shown in Fig. 2. It is observed that six effects are greater

17 + + 88.5018 + 83.74White bars: positive effects; black bars: negative effects.

with the signicant effects can be developed. Based on thesemodels op-

where Y1 is the FFA acidity reduction percentage (%), Xi are the trans-formed forms of the independent variables according to:

Xi Zi

Zhigh Zlow2

ZhighZlow2

3

and Zi are the original (untransformed) values of the variables.The coefcients that appear in Eq. (2) are half the calculated effects,

since a change of X=1 to X= 1 is a change of two units along the X

Term

Standardized Effect

ABC

BC

AC

AB

A

B

C

2520151050

a-level

A X4B X5C X6

Factor Name

113I. Sarantopoulos et al. / Energy for Sustainable Development 23 (2014) 110114timal operating yields can be obtained during the two-step biodieselproduction process. The main effects of the independent variables andtheir two and higher order interactions of the two factorial designs ofexperiments are shown at Table 4.

Based on the variables and interactions that are statistically signi-cant, a model describing the experimental response Y1 for estericationperformance was constructed as follows:interaction variables are statistically signicant but undoubtedly lowerthan the effect of the independent variables, namely treatment time(X4), KOH wt % (X5) and MeOH:triglyceride ratio (6). Hence, it is con-cluded that the transesterication step is favored at the increased valuesof these variables (i.e. 60 min, 2% wt KOH, and MeOH:oil ratio equal to9:1), which have been applied at runs 5 and 12 of the transestericationfactorial design.

Optimization of process efciency

The aforementioned statistical analysis allows inferring about thevariables and their interactions that are statistically signicant. Whatis more, empirical models describing the reaction evolution in function

Fig. 2. Pareto chart of the effects for the transesterication step for the full 23 factorial de-sign. White bars: positive effects; black bars: negative effects.Y1 85:4129:7612

X1 9:0712

X3 2

Table 4Average and main effects of the independent variables and their two and higher order in-teractions of the two 23 factorial designs on the response factors Y1 and Y2.

Esterication step Transesterication step

Effect Value ofeffect Y1

Effect Value ofeffect Y2

Average effect 85.412 Average effect 88.1125Main effects Main effectsX1 9.761 X4 6.65X2 3.054 X5 6.8X3 9.071 X6 6.975Two-factor interactions Two-factor interactionsX1 X2 1.839 X4 X5 1.775X1 X3 3.371 X4 X6 1.55X2 X3 0.264 X5 X6 0.9Three-factor interactions Three-factor interactionsX1 X2 X3 0.706 X4 X5 X6 0.225-level 2.26 -level 2.31axis. Themodel predicts a linear dependency of the acidity reduction onthe operating variables. Therefore, Eq. (2) can be transformed to give Y1prole as follows:

Y2 49:2269 0:1627t 1:1339MeOH 4

Where t is the reaction time (min), and MeOH is the MeOH:FFAratio.

Similarly, experimental models were developed for thetransesterication step, thus the response Y2 can be estimated by utiliz-ing to Eq. (5), which is based on the most statistical signicant effectsaccording to Fig. 2.

Y2 88:11256:652

X4 6:82

X5 6:9752

X6 5

where Y2 is the methyl-ester production percentage (%), Xi are thetransformed forms of the variables.

Based on Eq. (3), Y2 is transformed into the following empiricalmodel (6) that describes the formation of methyl-ester (Y2) duringthe transesterication reaction step.

Y2 50:5 0:2217t 6:8Cat 2:325MeOH 6

Where t is the reaction time (min), Cat is the KOH catalyst concen-tration (% wt), and MeOH is the CH3OH:triglycerides ratio. Hence, theempirical models (4) and (6) can be a useful tool to further scale-upthe two-step biodiesel production process, within the operating pointsutilized in these two full factorial designs of experiments.

Quality control

The development of biodiesel's industrial production was triggeredby extensive eld tests in running engines and vehicles so as todetermine the limits of various fuel properties. In Europe, the qualityof biodiesel is assessed under the provisions and the requirements ofquality standard EN 14214 that should be met from each biodieselsample so as to be a suitable substitute fossil diesel in transport sector.Quality control was performed for the biodiesel produced under the op-timumprocess conditions specied above (i.e. runs 4, 10 and 5, 12 for theesterication and transesterication step, respectively), based on theEuropean Standard EN 14214:2003. Density at 15 C, acidity number,methyl ester content, content of monoglycerides (MGs), diglycerides

Table 5Quality control of produced biodiesel.

Properties Produced biodiesel Limits

Methyl ester content, % 99.4 N96.5Density at 15 C, kg/m3 877 860900Monoglyceride content, % 0.0156 b0.8Diglyceride content, % 0.0005 b0.2Triglyceride content, % 0.0040 b0.2Free glycerol, % 0.0031 b0.02Total glycerol, % 0.0075 b0.25Acid value, mg KOH/g 0.42 b0.5

(DGs) and TGs and total and free glycerol, were determined and mea-sured (Table 5). It should be noted that for further scaling up of biodieselproduction and its promotion in the market, all quality characteristicsspecied by the European standard EN 14214 should be determined.

Conclusions

In this work, a two-step homogeneous catalyzed waste lardtransesterication reaction for biodiesel production was investigated.For this purpose, a factorial design methodology of experiments wasutilized and six variables that typically affect the production processwere examined. The statistical signicant variables, their effects andthe higher order interaction effects on process efciency were identi-ed. The main results drawn from this study are concluded below.

The esterication step is signicantly affectedmostly by the reactiontime and the MeOH:FFA ratio value. Specically, an increase in theirlevel brings an increase in the FFA acidity reduction percentage. Like-wise, the transesterication step is positively affected by three indepen-dent variables, namely reaction time, KOH concentration and MeOH:triglyceride ratio, that typically affect its performance. Furthermore,based on the statistically signicant variables, two empirical models de-scribing evolution of the two-step biodiesel production process weredeveloped. This is of major importance since they are valuable tools tofurther scale-up the process by computing its reaction yield. Finally, ithas to be noted that by applying the proposed two-step process, biodie-sel, conformed to the European Community quality standards, can beproduced from a low cost feedstock, namely waste lard.

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Optimization of biodiesel production from waste lard by a two-step transesterification process under mild conditionsIntroductionMaterials and methodsRaw materialTwo-step batch processAcid esterification stepTransesterification method

Biodiesel quality determinationAnalytical measurementsFactorial design and analysis of the experiments

Results and discussionEffect of operating parameters on process efficiencyOptimization of process efficiencyQuality control

ConclusionsReferences