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Optimization of the transesterification reaction in biodiesel production

F. Ferella a,*, G. Mazziotti Di Celso b, I. De Michelis a, V. Stanisci c, F. Vegliò a

a Department of Chemistry, Chemical Engineering and Materials, University of L’Aquila, Monteluco di Roio, 67040 L’Aquila, Italyb Department of Food Science, University of Teramo, Via C.R. Lerici 1, 64023 Mosciano Sant’Angelo (TE), Italyc Fox Petroli S.p.A., Via Osca 74, 66054 Vasto (CH), Italy

a r t i c l e i n f o

Article history:

Received 7 December 2008Received in revised form 21 January 2009

Accepted 23 January 2009

Available online 14 February 2009

Keywords:

Biodiesel

Rapeseed oil

Transesterification

ANOVA

a b s t r a c t

In this paper response surface methodology (RSM) was used to study the transesterification reaction of

rapeseed oil for biodiesel production. The three main factors that drive the conversion of triglycerides

into fatty acid methyl esters (FAME) were studied according to a full factorial design at two levels. These

factors were catalyst concentration (KOH), temperature and reaction time. The range investigated for

each factor was selected takinginto account the process of FoxPetroli S.p.A. Analysis of variance (ANOVA)

was used to determine the significance of the factors and their interactions which primarily affect the

first of thetwo transesterification stages. This analysis evidenced thebest operating conditions of thefirst

transesterification reaction performed at Fox’s plant: KOH concentration 0.6% w/w, temperature 50 C

and reaction time 90 min with a CH3OH to KOH ratio equal to 60. Three empirical models were derived

to correlate the experimental results, suitable to predict the behavior of triglyceride, diglyceride and

monoglyceride concentration. These models showed a good agreement with the experimental results,

demonstrating that this methodology may be useful for industrial process optimization.

2009 Elsevier Ltd. All rights reserved.

1. Introduction

The problems that nowadays affect fossil fuels are well known:

increasing price that makes petroleum no longer economically sus-

tainable, emission of very dangerous pollutants for human health,

emission of carbon dioxide that is the main reason of the global

warming. Moreover fossil fuels are non-renewable resources, so

they will last for a limited period of time. In this scenario vegetable

oils are more attractive, because of their renewable nature and

environmental benefits. Biodiesel is said to be carbon neutral, as

biodiesel yielding plants absorb more carbon dioxide than that

added to the atmosphere when used as fuel [1–4]. It is highly bio-

degradable in fresh water as well as in soil. The best part of biodie-

sel (90–98%) is mineralized in 21–28 days under aerobic or

anaerobic conditions [5–7]. Furthermore, the use of biodiesel indiesel engines reduces the emissions of hydrocarbons, carbon

monoxide, particulate matter and sulphur dioxide. Only nitrogen

oxides emission increases: this behavior is due to the oxygen con-

tent of biodiesel [8–14]. However, vegetable oils have some disad-

vantages. First of all, the direct use in internal combustion engines

is problematic. Due to their high viscosity (about 11–17 times

greater than diesel fuel) and low volatility, they do not burn com-

pletely and form deposits in the fuel injectors of diesel engine

[15,16]. An improvement on viscosity can be obtained with transe-

sterification, which seems to be the process that assures best re-sults in terms of lowering viscosity and improving other

characteristics [3]. Besides these technical difficulties, there are

some social problems to be considered, as the extensive use of veg-

etable oils may cause starvation in poor and developing countries.

As regards catalyst, potassium hydroxide has been successful in

producing biodiesel at industrial level [17]. Nevertheless, potas-

sium hydroxide produces soaps by neutralizing the free fatty acid

in the oil or by triglyceride saponification. Thus, biodiesel and glyc-

erine have to be purified by washing with hot distilled water two

or three times, resulting in a high consumption of both time and

water [3]. Unfortunately, due to their polarity, soaps dissolve in

glycerol phase during the separation stage after the reaction, but

they may be separated by means of a simple centrifugation. Forma-

tion of soaps decreases biodiesel yield obtained after the clarifica-tion and separation stages. In addition, the dissolved soaps

increase the methyl ester solubility in glycerol, an additional cause

of yield loss [4]. Several types of vegetable oils can be used for the

biodiesel production. In this paper rapeseed oil was studied, but

there are no technical restrictions to the use of other kinds of veg-

etable oils, although biodiesels coming from some vegetable oils

may not fulfil quality standards [18–22].

In Italy diesel fuel consumption was about 26 million tons in

2007 [23]. Considering that from every hectare of rape is possible

to obtain around 1.1–1.2 tons of oil [24], the possibility of total

substitution of diesel fuel with biodiesel is unlikely. However, veg-

etables oil can represent a small contribution, if biodiesel and die-

0016-2361/$ - see front matter 2009 Elsevier Ltd. All rights reserved.doi:10.1016/j.fuel.2009.01.025

* Corresponding author. Tel.: +39 0862 43 4265; fax: +39 0862 43 4203.

E-mail address: [email protected] (F. Ferella).

Fuel 89 (2010) 36–42

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sel were blended together. In 2007 469,707 tons of biodiesel were

produced in Italy, of which 202,035 tons were used in domestic

market [25]. Biodiesel is blended at 5% and up to 25% with diesel

in some petrol stations, while it is used unblended for heating pur-

poses. Fox Petroli S.p.A. is one of the most important Italian com-

panies amongst those operating in the production of biodiesel.

The plant is situated in Vasto, Central Italy, and in 2007 it produced

around 130,000 tons of biodiesel [26]. The raw rapeseed oil under-

goes first transesterification, and then a settler lets the glycerine

separate from supernatant, which reaches a reactor for the second

transesterification: the final triglyceride conversion is 98–99%. Fur-

ther glycerine is separated in another settler. A heat exchanger re-

moves part of methanol, whereas the other part is dried off by

vacuum distillation. The raw biodiesel is then washed by water

and centrifuged for removal of last traces of glycerine and soaps.

Water is dried off in an evaporator and a vacuum dryer; finally,

the biodiesel (named BIOFOX) is stored to be distributed and sold.

The glycerine is refined and sold as by-product. Several parameters

affect the transesterification: catalyst concentration, methanol

concentration, temperature, reaction time, pressure and the type

of oil because of different content of triglycerides and phospholip-

ids. In this study three of the most important parameters which af-

fect the yield of the first transesterification reaction were tested,

i.e. catalyst concentration, temperature and time of reaction. More-

over, these are the easiest factors which can be carefully controlled

during the industrial production. All the tests were performed by

using rapeseed oil at atmospheric pressure, since the transesterifi-

cation proceeds very fast even at low pressure: this avoids the in-

crease of costs both in terms of equipment and energy

consumption. As alkaline metal hydroxides are easily the most ac-

tive, KOH is used for the biodiesel manufacturing. The compound

which really reacts with triglycerides is methylate, hence the

amount of methanol also affects the transesterification. The

CH3OH:KOH ratio (w/w) was fixed at 60 for all the tests performed

during the experimental campaign: changing the amount of KOH,

the concentration of methanol automatically changes in different

tests. The total reaction between triglycerides and methanol togive biodiesel is a sequence of three sequential reactions:

Triglyceride þ CH3OH ! Diglyceride þ FAME ð1Þ

Diglyceride þ CH3OH ! Monoglyceride þ FAME ð2Þ

Monoglyceride þ CH3OH ! Glycerol þ FAME ð3Þ

From a stoichiometric point of view three moles of methanol

are required for each mole of triglyceride: however, in order to

maximize ester production, a greater molar ratio is employed, usu-

ally the double [27,28]. The aim of the present work is the determi-

nation of the best operating conditions for the first reaction of the

two-stage transesterification industrial process developed by Fox

Petroli S.p.A.

2. Materials and methods

2.1. ANOVA and regression analysis

The experimental tests were carried out according to a full 23

factorial design where factors (low and high level in parentheses)

were: KOH concentration (0.2%; 0.6% w/w), temperature (50 C;

60 C) and reaction time (30 min; 90 min).

Each test was replicated twice. The above values were chosen

taking into account economic considerations: the range of KOH

concentration (percentage by weight referred to the oil weight)

and the reaction time have been selected around the typical value

used in the industrial production of the company. The higher tem-

perature level was determined by considering the boiling point of methanol (65 C), whereas the lower value is 50 C, since previous

tests carried out by the authors demonstrated unsatisfactory con-

version rates of triglycerides. Moreover at lower temperatures mis-

cibility of methoxide and oil is scarce: this behaviour is due to the

greater polarity of the methoxide molecule with respect to the tri-

glyceride one, which is non-polar and determines the hydrophobic

performance of the oil itself. As a matter of fact, the greater the

temperature, the greater the miscibility of that mixture. Responses

selected to test the yield of the transesterification were triglycer-

ide, diglyceride and monoglyceride concentration (hereafter TC,

DC, MC, respectively). Experimental results were worked out using

ANOVA, which allows to evaluate whether the effect and the inter-

action among the investigated factors are significant with respect

to the experimental error. Yates’ algorithm is a simple technique

for estimating the main effects and interactions among them. The

significance of the main factors and their interactions was assessed

by F -test method with a confidence level of 95% [29,30]. Response

surface methodology (RSM), a mathematical–statistical tool, was

used for modelling TC, DC and MC. These responses of interest

are influenced by the three tested factors and RSM allows the opti-

mization of all these responses. For example, TC can be expressed

as:

TC ¼ f ð A;B;C Þ þ e ð4Þ

where e represents the error observed in the response TC. A low-or-

der model is usually employed, like a first-order model, but if a cur-

vature in the surface is present, a polynomial of higher order must

be used. In this study three second-order polynomials were used to

describe the response surface for TC, DC and MC; the general struc-

ture of that polynomial is the following:

Y ¼ b0 þXk

i¼1

bi xi þX

bii x2i þX

ii< j

X

j

bij xi x j þ e ð5Þ

where Y is the yield of the reaction, bi are the regression coefficients

and xi are the coded factors. Obviously a polynomial can not

approximate all the space of the independent variables, but it usu-

ally fits the real response for a relatively small region. The modelparameters can be estimated by using proper experimental designs

while collecting data. The experimental design for fitting the sec-

ond-order models was orthogonal and rotatable. Orthogonality is

the optimal design property as it minimizes the variance of the

regression coefficients. Rotability is another important property

which implies that the variance of a response at a certain point is

Fig. 1. Central composite design for three factors at two levels.

F. Ferella et al./ Fuel 89 (2010) 36–42 37



only a function of the distance of the point from the design centreand does not depend on direction [30,31]. The central composite de-

sign carried out in this study is shown in Fig. 1.

This central composite design can be rotatable by the choice of

a; its general formula is:

a ¼ ðn f Þ1=4 ð6Þ

where n f is the number of points used in the factorial portion of the

design. In this case, considering eight points, a is equal to 1.68. Table

1 gives the conditions of all the tests developed by the full factorial

design, both in terms of coded and non-coded variables. The central

point test was replicated three times (tests 9–11, Table 1), in order

to have a good estimation of the experimental error. Furthermore,

six axial tests (12–17, Table 1) were also carried out to better under-

stand the shape of the response surfaces.

The regression analysis was used to build three quantitative

models, in which only the significant factors were taken into ac-

count. These models were built in order to predict TC, DC and

MC for many operating conditions that were not directly tested.

It should be noted that the use of the models outside the investi-

gated range is not allowed, since it would lead to wrong results.

2.2. Experimental procedure

The experimental tests were carried out using a 500 ml jacketed

stirred reactor tank. The temperature of oil was controlled by a

thermometer and regulated by an electrically heated water bath

(Colora WK16). The reactor was mechanically stirred at 600 rpm

to assure a good mixing of the reactants.

For each test 200 ml of rapeseed oil (quality control reported inTable 2) were heated at the required temperature, according to the

experimental plan shown in Table 1; in the meantime, KOH (flakes,

assay > 90%, Sigma–Aldrich) was dissolved in a certain volume of

methanol (anhydrous 99.8%, Sigma–Aldrich), in order to have a

fixed ratio CH3OH:KOH equal to 60 (w/w). As said before, this ratio

was kept constant for all the tests. For example, in test 1, 0.37 g of

KOH were dissolved in 22.1 g of methanol, considering the oil den-

sity of 0.92 kg/m3.

Once KOH was completely dissolved, methoxide was added to

the hot solution, and the reaction took place for the required time.

After that, 10 ml of solution were withdrawn and 2 ml of citric acid

were added to stop the transtesterification. Hence, the biodiesel

was separated from glycerine by centrifugation (Thermo Scientific

IEC CL30) and the sample was then ready for analysis.

2.3. Analytical methods

TC, DC and MC were measured by capillary column gas chroma-

tography (Thermo TRACE GC Ultra) equipped with a cold on-col-

umn injection and autosampler apparatus. Analyses were carried

out according to the EN 14105 internal standard calibration;

100 mg of each biodiesel sample were mixed with 100 ll of

1,2,4-butanetriol (1 mg/ml, standard 1) and 100ll of 1,2,3-Tricap-

rinoylglycerol (8 mg/ml, standard 2). Other 100 ll of N -methyl-N -

trimethylsilyltrifluoroacetamide (MTSFA, derivatization grade)

were added to convert both free and total glycerol into volatile

compounds. All reagents were supplied by Sigma–Aldrich. After

15 min, 8 ml of heptane were added as solvent. Final sample

(0.5 ll) were injected into the gas chromatograph analyser forTC, DC, MC determination.

3. Results and discussion

3.1. ANOVA

The response of the factorial design is reported in Table 3. Each

result is expressed as arithmetic mean of two replications.

Looking at Table 3, test 6 gives the best simultaneous results in

terms of triglycerides, diglycerides and monoglycerides, as their

concentrations (0.05%, 0.09% and 0.36% w/w, respectively) are the

lowest among those of the whole experimental plan. These results

were obtained by 0.6% w/w of KOH, 50 C and 90 min of reaction.Comparing these values with the EN 14214 specifications (see

Table 1

Test conditions of the full factorial design.

Test Treatment Coded factors A B C

combination A B C KOH (% w/w) Temperature (C) Time (min)

1 (1) 1 1 1 0.2 50 30

2 a 1 1 1 0.6 50 30

3 b 1 1 1 0.2 60 30

4 ab 1 1 1 0.6 60 30

5 c 1 1 1 0.2 50 90

6 ac 1 1 1 0.6 50 90

7 bc 1 1 1 0.2 60 90

8 abc 1 1 1 0.6 60 90

9 0 0 0 0 0.4 55 60

10 0 0 0 0 0.4 55 60

11 0 0 0 0 0.4 55 60

12 – 0 1.68 0 0.4 46.6 60

13 – 0 1.68 0 0.4 63.4 60

14 – 1.68 0 0 0.064 55 60

15 – 1.68 0 0 0.736 55 60

16 – 0 0 1.68 0.4 55 9.5

17 – 0 0 1.68 0.4 55 110.5

Table 2

Quality control of the rapeseed oil.

Parameter Unit Value Method

Acidity (as oleic acid) % <0.10 NGD C10-1976

Cloud point C 3.8 EN 23015

Density at 20 C kg/m3 0.92 EN ISO 3675

Iodine number gI2/100g 113 ISO 5508

Lecithin (as phosphorus) ppm <10 UNI 22038-2001

Erucic acid % <0.20 ISO 5508

Sulphur content ppm <2 EN ISO 20846

Total contamination % <0.05 NGD C7-1976

Viscosity at 20 C mm2/s 77.5 EN ISO 3104

Water content % <0.10 KARL FISHER

38 F. Ferella et al./ Fuel 89 (2010) 36–42



Table 4), the biodiesel fulfils the quality requirements regarding

TC, DC and MC. At the moment, the biodiesel produced by Fox Petr-oli after the first transesterification has the following composition:

TC 5.26%, DC 2.48%, MC 1.21% w/w; as expected, methanol and free

glycerine content exceeds the limits indicated in the European

specifications. Table 4 also shows the analysis of biodiesel pro-

duced by that plant at the end of the process, as it is put on the

market [26].

Fig. 2 shows the main effects and interactions among factors,

which mainly influence the final concentration of triglycerides.

Effects with a statistical significance lower than 95% were not

reported, according to the F -test utilized. Catalyst concentration

KOH, factor A, has a strong negative effect on the TC (16%): as ex-

pected, increasing the amount of catalyst the concentration of tri-

glycerides decreases. In this particular case, as the response

variables (TC, DC, MC) represent the reactants, a negative effect

is advisable, because of a very low TC. This means that a greater

amount of triglycerides reacts with methylate, increasing the yield

of the transesterification.

Reaction time, factor C, has a slight negative effect, so the yieldof the process increases if the reaction time is prolonged from 30 to

90 min.

The interaction AB has a slight negative effect too; if the amount

of KOH increases together with temperature, the TC reduces, while

the final conversion to FAME grows.

Temperature, factor B, seems to have a small positiveeffect, even

though it is not statistically significant with respect to the experi-

mental error determined by replications of the central point test.

Results of ANOVA for concentration of diglycerides are shown in

Fig. 2. As for TC, catalyst concentration has an important negative

effect on DC in the range studied, whereas interaction AB between

KOH and temperature is also negative but less determinant. Time is

not significant: this could be explained by a greater reaction rate of

the second reaction (Eq. (2)) compared to the first one (Eq. (1)).

Table 3

Results obtained in terms of TC, DC and MC.

Test Response

TC (% w/w) DC (% w/w) MC (% w/w)

1 16.42 11.28 6.74

2 1.45 0.87 0.72

3 18.50 12.48 7.17

4 0.43 0.33 0.80

5 14.10 10.58 5.57

6 0.05 0.09 0.36

7 16.34 12.44 6.73

8 0.01 0.09 0.84

9 3.32 2.07 0.93

10 2.47 1.46 0.90

11 2.95 1.80 1.09

12 6.65 3.63 1.26

13 2.50 1.51 1.02

14 96.71 3.23 0.07

15 0.01 0.04 0.60

16 6.84 4.58 2.72

17 2.74 1.63 0.83

Table 4

Analysis of biodiesel produced at Fox Petroli and European specification for FAME used in diesel engines.

Parameter Unit Value Specification EN 14214 Method

Min Max

Acid value mgKOH/g 0.27 0.5 EN 14104

Ash sulphated % m/m <0.01 0.02 ISO3987

Carbon residue % m/m 0.10 0.30 EN ISO 10370

Cetane number 53 51.0 EN ISO 5165

Cold filter plugging point C 6 EN116

Cloud point C 0 -20 EN 23015

Methanol content % m/m <0.05 0.2 EN 14110

Monoglyceride content % m/m 0.69 0.8 EN 14105Diglyceride content % m/m 0.08 0.2 EN 14105

Triglyceride content % m/m 0.1 0.2 EN 14105

Free glycerol % m/m 0.006 0.02 EN 14105

Total glycerol % m/m 0.20 0.25 EN 14105

Copper strip corrosion (3 h at 50 C) Rating Class 1 Class 1 EN ISO 2160

Density at 15 C kg/m3 883.5 860 900 EN ISO 3675

Ester content % m/m 97.3 96.5 EN 14103

Flash point C 183 120 EN ISO 3679

Iodine number gI2/100g 119 120 EN 14111

Linolenic acid methyl ester % m/m 7.3 EN 14103

Alkali content mg/kg <2 5 EN 14108 (Na)

EN 14109 (K)

Phosphorus content mg/kg 1.4 10 EN 14107

Oxidation stability, 110 C hours 7.2 6 EN 14112

Sulphur content mg/kg <2 10 EN ISO 20846

Total contamination mg/kg 9 24 EN 12662

Viscosity at 40 C mm2/s 4.46 3.5 5 EN ISO 3104

Water content mg/kg 236 500 EN ISO 12937

-18

-16

-14

-12

-10

-8

-6

-4

-2

0

2 A B C AB AC

E f f e

c t ( % )

TC

DC

MC

Fig. 2. Effect of factors and of their combinations on TC, DC and MC (A: KOH; B:

temperature; C: reaction time).


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Furthermore temperature alone (factor B) has a slight positive

effect on the final DC, nevertheless, this factor does not have the re-

quired significance if compared to the variance of the experimental

error.

As regards monoglycerides (see Fig. 2), there are four main fac-

tors and interactions that influence their concentration at the end

of the reaction. As usual, factor A has a significant negative effect,

even though not so important as for triglycerides and diglycerides.

Temperature, factor B, seems to have a positive effect on the fi-

nal MC, but this means that it plays a negative role in the conver-

sion of monoglycerides to methyl esters in the range from 50 to

60 C. Reaction time (factor C) has a negative effect, so it will be

useful to extend the time to achieve a greater yield of FAME.

The interaction AC has a very small positive effect: increasing

simultaneously both concentration of KOH and reaction time the

total conversion of monoglycerides to methyl esters lightly

decreases.

3.2. Regression analysis

Experimental results were fitted by empirical models according

to RSM [30,31], in order to predict TC, DC and MC by weight under

different operating conditions. The complete models suitable to fit

the experimental data are second-order models, having the follow-

ing general structure:

Y ¼ a0 þ a1 X 1 þ a2 X 2 þ a3 X 3 þ aÞ12 X 1 X 2 þ a13 X 1 X 3 þ a23 X 2 X 3

þ a11 X 21 þ a22 X

22 þ a33 X

23 ð7Þ

where all the independent variables are in the coded form and all

coefficients a0, a 1, a 2, a3, a12, a 13, a23, a11, a 22, a33 were estimated

by linear regression procedures. Coded variables can be obtained

from the real ones by the following expressions:

X 1 ¼ ð A 0:4Þ=0:2 ð8Þ

X 2 ¼ ðB 55Þ=5 ð9Þ

X 3 ¼ ðC 60Þ=30 ð10ÞA first regression analysis was carried out in order to obtain an

useful tool, able to calculate the final TC, DC, MC of the first transe-

sterification reaction, even for different KOH concentration, tem-

perature and time.

Three statistical models were developed as a consequence of

the first regression analysis. However, these models did not show

a good agreement with the experimental results of the whole fac-

torial design, because correlation coefficients of the regression

were not satisfactory and the significance of all the model param-

eters was rather low.

The regression analysis was repeated, removing the results ob-

tained from tests 14 and 16. The poor agreement between experi-

mental results and factorial design is particularly strong in case of

mono and diglyceride data fitting: this behaviour is probably due

to their trend as a function of the extent of reaction, as shown in

Fig. 3.

A non satisfying data fitting was obtained for triglycerides as

well, although they showed a different trend as a function of extent

of reaction.

Fig. 3 allows to define as critical tests 14 and 16, because their

experimental data are located in the rise side of the curve. This area

is particularly difficult to fit for the second-order model, which, of

course, is defined by a parabolic trend.

This problem is guessable from a chemical point of view: if the

catalyst to triglycerides molar ratio is at least equal to the stoichi-

ometric one, triglycerides react quickly facilitating monoglycerides

formation, but this is going to decrease in case of lower catalyst

concentrations. This is the case of test 14, where monoglyceride

concentration is very low, because triglyceride species are poorly

converted. Thus, test 14 shows experimental results which are

not fitted with success by the second-order model.

A similar situation happens when the reaction time is too low:

in this case triglyceride species is poorly converted with conse-

quently scarce mono and diglyceride concentration, as they have

not had enough timeto form(case of test 16). Results of the second

regression analysis for TC are given in Table 5.

According to this analysis, the following equation was then used

in order to predict TC, as a function of catalyst concentration, tem-

perature and reaction time:

TCð%w=wÞ ¼ 4:49 8:13 X 1 0:85 X 3 þ 3:58 X 21 ðR2 ¼ 0:97Þ

ð11Þ

Eq. (11) was utilized for the optimization of the first transeste-

rification reaction as concerns FAME production. The adequacy of

the mathematical model obtained by the regression analysis was

confirmed by a scatter diagram (Fig. 4), where experimental data

for TC were reported as a function of the calculated ones.

As it is possible to note, all points are disposed close to the

straight line, confirming a good agreement between the experi-

mental results and those ones calculated by the model.

Eq. (11) was also employed to represent the best-fitting re-

sponse surface as a function of KOH concentration and reaction

time. Dependence on temperature misses, because of low signifi-

cance of the coefficients a2, a12, a23, a22.

As shown in Fig. 5, conversion of triglycerides is rather low

when KOH concentration is below 0.45%. In case of higher values,

KOH enhances the chemical reactions, reaching more satisfactory

conversion values. At the same time, keeping constant KOH con-

Fig. 3. Mono, di and triglyceride trends as a function of extent of reaction.

Table 5

Estimated values for parameters in Eq. (5).

Coefficient (%) s.d Significance

a0 4.49 0.65 100

a1 8.13 0.49 100

a2 n.s.

a3 0.85 0.46 90

a12 n.s.

a13 n.s.

a23 n.s.

a11 3.58 0.61 100

a22 n.s.

a33 n.s.

R2 = 0.96




centration on high values, reaction time shows a positive effect on

triglyceride conversion. A statistical model was also developed for

DC by a different regression analysis, according to the following

equation:

DCð%Þ ¼ 2:64 5:91 X 1 2:98 X 21 ðR2 ¼ 0:96Þ ð12Þ

In this equation there are only three significant coefficients, a0,

a1, a11, as result of a strong dependence on catalyst concentration,

which mainly drives the reaction between diglycerides and meth-

anol. The scatter diagram of the experimental DC versus those val-

ues obtained by Eq. (12) is reported in Fig. 4: a good agreement

between the two set of data can be observed, confirming that the

model interprets the experimental range studied adequately. As a

result, the mathematical model which is able to predict the DC in

the range of the investigated factors (0.2% < A < 0.6%; 50 C < B <60 C; 30 min< C < 90 min) is a simple second-order curve, as

shown in Fig. 6.

The trend reported in Fig. 6 suggests that the minimum catalyst

concentration required to be sure that all diglycerides react with

methanol is 0.53–0.55% w/w.

The second-order mathematical model resulting from the

regression analysis is as follows:

MC ð%Þ ¼ 1:26 3:07 X 1 0:38 X 3 þ 1:82 X 21 þ 0:30 X 23 ðR2 ¼ 0:97Þ

ð13Þ

MC calculated by Eq. (13) was reported in Fig. 4 together with

MC obtained by the experimental tests. A very good R2 value justi-

fies the choice of the model above, so it can be used in a proficient

way to understand the behaviour of MC for different experimentalconditions which were not directly tested.

Eq. (13) can be represented as dimensional surface and contour

plot: this surface shows the predicted MC for the experimental

range both of reaction time and initial catalyst concentration of ra-

peseed oil. The tri-dimensional surface reported in Fig. 7 is the

most useful approach in terms of visualization of the reaction sys-

tem, because it gives the simultaneous dependence from the two

most significant parameters (KOH concentration and time) which

affect the production of FAME. As Fig. 7 highlights, behaviour of

monoglycerides is similar to that of triglycerides: it is rather low

when KOH concentration is below 0.40–0.45% whilst, in case of

higher values, KOH catalyst improves the chemical reaction en-

abling more satisfactory reaction rates. At the same time, keeping

constant KOH concentration on high values, reaction time shows apositive effect on monoglyceride conversion: in particular, after 60

min the conversion to esters is complete.

4. Conclusions

In this paper a study of the optimization of the rapeseed oil

transesterification reaction parameters was carried out by means

of Response Surface Methodology (RSM). In particular KOH catalyst

effect, temperature and reaction time were investigated consider-

ing the influence on triglyceride, diglyceride and monoglyceride

concentration. The analysis of variance (ANOVA) showed that to

obtain satisfactory triglyceride conversion, the higher catalyst con-

centration studied was needed, together with a reaction time of

about 90 min. Moreover, increasing both temperature and KOHconcentration higher conversion rate could be achieved. Similar re-

0

5

10

15

20

0 5 10 15 20

Experimental (% w/w)

C a l c

u l a t e d ( % w

/ w )

Tri

Di

Mono

Fig. 4. Scatter diagram of the experimental TC, DC and MC vs those concentrations

calculated by Eqs. (11)–(13).

Fig. 5. Response surface of TC vs. catalyst concentrationand reaction time, obtained

by Eq. (11).

0

2

4

6

8

10

12

0 0.2 0.4 0.6 0.8

KOH (% w/w)

D C ( % w

/ w )

Fig. 6. Response surface of the DC using Eq. (12).

Fig. 7. Response surface of MC vs. catalyst concentration and reaction time.




sults were obtained for diglyceride species; time does not seem so

significant as in previous case: this is probably due to the greater

reaction rate of diglyceride compared to the triglyceride one. As re-

gards monoglycerides, their behaviour is very close to that of tri-

glycerides; however, the increase of both catalyst concentration

and time leads to lower ester conversions. The same negative effect

is reached in case of higher temperature values. The statistical

models developed for predicting TC, DC and MC showed a good

agreement between experimental and calculated yields (R2P

0.96), demonstrating the usefulness of regression analysis as a tool

for optimization purposes. In conclusion, results evidenced the

essential role of the catalyst, taking into account the methanol to

catalyst ratio that remained constant; a very good conversion of

triglycerides, diglycerides and monoglycerides into FAME was ob-

tained by 0.6% w/w KOH, 60 methanol to KOH ratio by weight at

50C after 90 min of reaction. The final concentrations were

0.05% triglicerides, 0.09% diglycerides and 0.36% monoglycerides.

This suggests further work which will be aimed at the optimization

of the second transesterification reaction, achieving a full optimi-

zation of the Fox Petroli’s process.

Acknowledgements

Authors are very grateful to Ing. Francesca Forgione for her pre-

cious collaboration during the experimental work at Fox Petroli’s

laboratories.

References

[1] Antolin G, TinautFV, Briceno Y, Castano V, Perez C, Ramirez AL.Optimization of

biodiesel production by sunflower oil transesterification. Bioresour Technol

2002;83:111–4.

[2] Lang X, Dalai AK, Bakhshi NN, Reaney MJ, Hertz PB. Preparation and

characterization of bio-diesels from various bio-oils. Bioresour Technol

2001;80:53–62.

[3] Sharma YC, Singh B, Upadhyay SN. Advancements in development and

characterization of biodiesel: a review. Fuel 2008;87:2355–73.

[4] Vicente G, Martìnez M, Aracil J. Integrated biodiesel production: a comparisonof different homogeneous catalysts systems. Bioresour Technol

2004;92:297–305.

[5] Makareviciene V, Janulis P. Environmental effect of rapeseed oil ethyl ester.

Renew Energ 2003;28:2395–403.

[6] Pasqualino JC, Montane D, Salvado J. Synergic effects of biodiesel in the

biodegradability of fossil-derived fuels. Biomass Bioenerg 2006;30:874–9.

[7] ZhangX, PetersonC, Reece D, Moller G, Haws R. Biodegradability of biodiesel in

aquatic environment. Trans Am Soc Agric Eng 1998;41:1423–30.

[8] Canakci M, Erdil A, Arcaklioglu E. Performance and exhaust emission of a

biodiesel engine. Appl Energ 2006;83:594–605.

[9] Dorado MP, Ballesteros E, Gomez JM, Lopez FJ. Exhaust emissions from a diesel

engine fuelled with transesterified waste olive oil. Fuel 2003;82:1311–5.

[10] Dorado MP, Ballesteros E, Arnal JM, Gomez J, Gimenez FJL. Testing waste olive

oil methyl ester as a fuel in a diesel engine. Energ Fuel 2003;17:1560–5.

[11] Labeckas G, Slavinskas S. The effect of rapeseed oil methyl ester on direct

injection diesel engine performance and exhaust emissions. Energ Conver

Manage 2006;47:1954–67.

[12] Monyem A, GerpenJH. The effect of biodiesel oxidation onengine performance

and emission. Biomass Bioenerg 2001;20:317–25.[13] Turrio-Baldassarry L. Emissions comparison of urban bus engine fuelled with

diesel oil and biodiesel blend. Sci Total Environ 2004;327:147–62.

[14] Usta N. An experimental study on performance and exhaust emissions of a

diesel engine fuelled withtobacco seed oil methyl ester. Energ Conver Manage

2005;46:2373–86.

[15] Demirbas A. Biodiesel fuels from vegetable oils via catalytic and non-catalytic

supercritical alcohol transesterifications and other methods: a survey. Energ

Conver Manage 2003;44:2093–109.

[16] Ramadhas AS, Jayaraj S, Muraleedharan C. Biodiesel production from high FFA

rubber seed oil. Fuel 2005;84:335–40.

[17] Vicente G, Martìnez M, Aracil J. Optimisation of integrated biodiesel

production. Part II: A study of material balance. Bioresour Technol

2007;98:1754–61.

[18] Demirbas A. Biodiesel production from vegetable oils via catalytic and non-

catalytic supercritical methanol transesterification methods. Prog Energ

Combust 2005;31:466–87.

[19] Demirbas A. Biodiesel production via non-catalytic SCF method and biodiesel

fuel characteristics. Energ Conver Manage 2006;47:2271–82.

[20] Ghadge SV, Raheman H. Biodiesel production from mahua (Madhuca indica) oil

having high free fatty acids. Biomass Bioenerg 2005;28:601–5.

[21] Meher LC, Dharmagadda VSS, Naik SN. Optimization of alkali-catalyzed

transesterification of Pongamia pinnata oil production of biodiesel. Bioresour

Technol 2006;97:1392–7.

[22] Sarim R, Sharma M, Sinharay S, MalhotraRK. Jatropa-palmbiodiesel blends: an

optimum mix for Asia. Fuel 2007;86:1365–71.

[23] Federconsumatori, 2008. <http://www.federconsumatori.it/news/upl/

Upload_Picture_bv.asp?key=59>.

[24] Bona S, Mosca G, Vamerali T. Oil crops for biodiesel production in Italy. Renew

Energ 1999;16:1053–6.

[25] Assocostieri, Unione Produttori Biodiesel, 2008. <http://www.

assocostieribiodiesel.com/statistiche.asp>.

[26] Fox Petroli S.p.A., 2008. <http://www.biofox.com>.

[27] Freedman B, Pryde EH, Mounts TL. Variables affecting the yields of fatty esters

from transesterified vegetable oils. J Am Oil Chem Soc 1984;61:1638–43.

[28] Sharma YC, Singh B. Development of biodiesel from karanja, a tree found in

rural India. Fuel 2007;87:1740–2.

[29] Himmelblau DM. Process analysis by statistical methods. 3rd ed. NewYork: Wiley; 1978.

[30] Montgomery DC. Design and analysis of experiments. 3rd ed. New

York: Wiley; 1991.

[31] Davies OL, Goldsmith PL. Statistical methods in research and production. 4th

ed. Edinburgh: ICI; 1972.


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