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Energy 157 (2018) 843e852

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Energy

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Biodiesel production from castor oil in Egypt: Process optimisation,kinetic study, diesel engine performance and exhaust emissionsanalysis

Omar Aboelazayem a, *, Nour Sh. El-Gendy b, Ahmed A. Abdel-Rehim c, d, Fatma Ashour e,Mohamed A. Sadek a

a Department of Chemical Engineering, The British University in Egypt, Misr-Ismalia Road, El-Shorouk City, 11837, Cairo, Egyptb Department of Process Design and Development, Egyptian Petroleum Research Institute, Nasr City, 11727, Cairo, Egyptc Department of Mechanical Engineering, The British University in Egypt, Misr-Ismalia Road, El-Shorouk City, 11837, Cairo, Egyptd Combustion and Energy Technology Lab, Department of Mechanical Engineering, Shoubra Faculty of Engineering, Benha University, 11629, Cairo, Egypte Department of Chemical Engineering, Cairo University, Giza, 12613, Egypt

a r t i c l e i n f o

Article history:Received 18 December 2017Received in revised form6 April 2018Accepted 31 May 2018Available online 1 June 2018

Keywords:BiodieselCastor oilOptimisationResponse surface methodologyDiesel engine performanceExhaust emissions analysis

List of Abbreviations: M:O, methanol to oil ratio;odology; RCCD, rotatable central composite design;KOH, potassium hydroxide; DI, direct injection; FAAEtriglycerides; CH3OK, potassium methoxide; BSFC, braBTE, brake thermal efficiency; FAME, fatty acids mnumber; GL, glycerol; PM, particulate matter; HC, haromatic hydrocarbons.* Corresponding author. Department of Chemical E

versity in Egypt, El-Shorouk City, Cairo, Egypt.E-mail address: omar.aboelazayem@bue.edu.eg (O

https://doi.org/10.1016/j.energy.2018.05.2020360-5442/© 2018 Elsevier Ltd. All rights reserved.

a b s t r a c t

In this study, biodiesel production from castor oil has been investigated. Five independent variablesincluding; methanol to oil (M:O) molar ratio, catalyst concentration, reaction temperature, time andstirring rate have been chosen to investigate their effect on biodiesel yield. Response Surface Method-ology (RSM) via Rotatable Central Composite Design (RCCD) has been used to evaluate the influence ofthe independent variables on the reaction response. Analysis of variance (ANOVA) has been used toinvestigate the adequacy of the predicted model. The optimum conditions for 97.82% biodiesel yield havebeen achieved at M:O molar ratio of 5.4:1, potassium hydroxide (KOH) catalyst concentration of 0.73%,temperature of 64 �C, time of 2.5 h and stirring rate of 320 rpm. The predicted optimum conditions havebeen validated with 0.59% relative error from experimental results. The kinetic calculations concludedthat reaction is pseudo second order with reaction rate constant, activation energy and frequency factorof 0.16M�1 min�1, 21.95 kJ/mol and 6.02M�1min�1, respectively. Finally, a case study investigating theperformance and emissions on a direct injection (DI) diesel engine fuelled by biodiesel/petro-dieselblends (5, 10, 15 and 20% v:v) has been performed concluding significant reduction of greenhouse andtoxic gases.

© 2018 Elsevier Ltd. All rights reserved.

1. Introduction

Fossil fuels are the main global source of energy, however, thereare many concerns regarding the non-renewable fuels. It is gener-ally accepted that burning fossil fuels is contributing to climatechange. Concerns about global warming resulted from greenhouse

RSM, response surface meth-ANOVA, analysis of variance;, fatty acids alkyl esters; TG,ke specific fuel consumption;ethyl esters; TAN, total acidydrocarbons; PAH, polycyclic

ngineering, The British Uni-

. Aboelazayem).

gases emissions in addition to the instability of the world oil pro-duction have been considered as the main international problemswithin the last years [1,2].

Biodiesel has been considered as a competitive alternativerenewable fuel for petroleum diesel. It has many advantages overpetro-diesel including; biodegradability, greener exhaust emis-sions, lower toxicity, higher flash point and negligible sulphur.Biodiesel could be used as a pure fuel and it could be blended withpetro-diesel at any ratio [3].

Presently, edible oils are considered the main resources forbiodiesel production. However, this inclusive dependency on edibleoils has a negative impact on food security where food prices haveincreased as a result of the competition between fuel and foodindustries [4]. Thus, the focus has been shifted towards non-edibleoil crops that are not used for nutrition process and could standharsh conditions in barren lands. Toxic compounds exist in oils

mailto:omar.aboelazayem@bue.edu.eghttp://crossmark.crossref.org/dialog/?doi=10.1016/j.energy.2018.05.202&domain=pdfwww.sciencedirect.com/science/journal/03605442http://www.elsevier.com/locate/energyhttps://doi.org/10.1016/j.energy.2018.05.202https://doi.org/10.1016/j.energy.2018.05.202https://doi.org/10.1016/j.energy.2018.05.202

O. Aboelazayem et al. / Energy 157 (2018) 843e852844

derived from these types of plants are considered the main reasonbehind not using them for human nutrition [5].

Biodiesel is defined as mixture of fatty acid alkyl esters (FAAEs)of long chain fatty acids derived from alcoholysis of triglycerides(TG) from vegetable oils, animal fats and recently from algae. FAAEoccurs via a stoichiometric reversible reaction of 3mol of alcoholswith 1mol of TG, producing; 3mol of FAAEs and 1mol of glycerol(GL). This reaction is usually catalysed using chemical catalystsinclude acidic, alkaline and/or biological catalysts include enzymessuch as lipase. In addition, non-catalytic reaction have been re-ported with high production yield in relatively short time at su-percritical conditions of methanol [6].

Castor plant is one of the promising potential oil resource forbiodiesel production. It can easily grow in different soil and climaticconditions. Regarding the plantation cost, castor oil would beplaced on the top of non-edible oils [7]. Moreover, castor tree isgaining huge attention due to its low maintenance and fewer re-quirements of crop husbandry management practices required [8].The oil yield of the castor seed is about 40e60%. Zahran and Wills[9] have reported that Ricinus Communis is naturally growing as anassociate species in different habitats of the Nile river and desertregions in Egypt. It is planted in Egypt with a total area of about2,000,000 ha, and the annual seeds production is above 250,700tonnes. It is located mainly in southern and southwestern regionsof Egypt in addition to Sinai. However, few studies have beenpublished concerning the optimisation of the transesterification ofcastor oil to biodiesel [10,11]. There are frequent studies on thetransesterification reaction in the literature, however, only fewresearchers have studied kinetics of transesterification of castor oil[12,13].

Castor oil is readily soluble in alcohol at lower temperaturecompared to other vegetable oils. However, its viscosity is higherthan that of other vegetable oils with up to 7-times as it is mainlycomposed of ricinoliec acid (12-hydroxy-9-cis-octadecenoic acid),which is very unique fatty acid that includes a hydroxyl group in itsstructure [12]. Accordingly, the viscosity of castor based biodiesel isexpected to be higher than the usual biodiesel produced from othervegetable oils. Blending biodiesel produced from castor oil withpetro-diesel is one of themethods to overcome the aforementionedproblem [14].

Hasan and Rahman [15] have reviewed several studies on theeffect of biodiesel/petro-diesel blend on engine performance. Theyhave reported the occurrence of ausual increase of brake specificfuel consumption (BSFC) with the increment of biodiesel ratio inthe blend. Parvaneh et al [16] and Das et al [17] have also reportedan increasing effect in BSFC while increasing biodiesel percentagederived from castor oil to biodiesel/petro-diesel blend. Valente et al[18] reported increasing range of BSFC by 3.2e7.1% with increasingthe castor oil based biodiesel ratio in the biodiesel/petro-dieselblends. But, it is worth to mention that, increasing biodiesel inbiodiesel/petro-diesel blend has slightly decreasing effect of brakethermal efficiency (BTE). According to the lower heating value ofbiodiesel compared to petro-diesel, the output of combustion isreduced which consequently increase the fuel consumption. Hence,BTE remains constant or slightly decrease [18]. Hasan and Rahman[15] have reported a slight decrease in BTE while increasing bio-diesel to biodiesel/petro-diesel blend. They explained this phe-nomenon due to delay of biodiesel ignition due to its lower calorificvalue, higher viscosity and poor spray properties.

Thus, themain aim of the presentwork is to provide full detailedstudy on production of biodiesel from Egyptian castor oil including;process optimisation, kinetic study and practical implementationon diesel engine to analyse the performance and emissions ofbiodiesel/petro-diesel blends. RSM using RCCD has been used fordesigning of experiments, modelling and optimisation of the most

significant variables affecting biodiesel yield; M:O molar ratio,catalyst concentration, temperature, time and stirring rate, tomaximise production of biodiesel from castor oil. Regressionanalysis has been performed to develop a numerical model torepresent the process variables function in the process response.ANOVA has been used to check the significance of the developedmodel. Kinetic data of the overall transesterification reaction hasbeen also analysed. Finally, diesel engine performance and exhaustemission analysis have been investigated using different blends ofbiodiesel/petro-diesel, to figure out the optimum bio/petro-dieselblend with best performance.

2. Material and methods

2.1. Experimental setup

The transesterification reaction took place in a 500mL 3-neckround glass batch reactor fitted with reflux condenser. Castor oilwas heated via a controlled magnetic heat stirrer to the desiredtemperature. KOHwith a specific weight was mixed with methanolto produce potassium methoxide (CH3OK). Once oil reached thetargeted temperature, CH3OK was mixed with oil. The point atwhich the reactants mixture reach the targeted temperature wasdefined as the reaction starting point (t¼ 0). Temperature wasmonitored and controlled using a thermocouple fitted by the digitalmagnetic heat stirrer.

After achieving the prescribed reaction time, the mixture wasthen cooled and transferred to a separating funnel, where glycerolin the bottom layer was separated from biodiesel. Settling separa-tion time was 120min for efficient separation for the mixture.Glycerol was then separated, and biodiesel was proceeded forcatalyst separation step.

Further, biodiesel was washed with warm water to remove theunreacted dissolved KOH catalyst. Unreacted methanol in biodieselwas recovered using simple distillationwhere biodiesel was heatedto 80 �C for 30min. Purified biodiesel was dried and the productweight was measured for yield calculations using Equation (1) [6].

Yieldð%Þ ¼ Total weight of pure biodieselTotal weight of oil used

� 100 (1)

2.2. Experimental design

Five independent variables were chosen for the analysisincluding; M:O molar ratio, KOH concentration, temperature, timeand stirring rate, which were labelled as A, B, C, D and E, respec-tively. Five levels for each variable have been coded as -a, �1,0, þ1, þa, as shown in Table 1.

Rotatable Central Composite Design (RCCD) was used togenerate an uncertainty matrix of experiments for analysing therelationship between reaction variables and reaction responses.Twenty-six experimental runs were constructed in the uncertaintymatrix. Minimal points design was used for constructing therandomised experimental matrix with 21 non-centre points and 5centre points. The experimental runs were performed in a rando-mised order where the responses were calculated using theexperimental results for each experiment. Design Expert 10 soft-ware (Stat-Ease Inc., Minneapolis, MN, USA) was used to identifyand operate the experimental design procedures. Table 2 shows theexperimental runs variables and both experimental and predictedresults of biodiesel yield.

Table 2Statistical combination of independent variables with the experimental and pre-dicted responses.

Run A B C D E Biodiesel

X1 X2

1 9 0.9 70 1 200 68.15 69.202 3 0.3 70 3 400 62.20 63.253 6 0.6 41.78 2 300 49.00 47.094 6 0.6 60 3.82 300 87.90 85.995 3 0.9 70 1 400 56.25 57.306 6 0.6 60 2 300 85.00 86.297 6 0.053 60 2 300 55.00 53.098 9 0.3 70 3 200 74.10 75.159 3 0.3 50 1 200 16.00 18.1010 6 0.6 60 0.17 300 50.00 48.0911 9 0.3 70 1 400 67.00 68.0512 6 0.6 78.21 2 300 88.00 .86.0913 0.53 0.6 60 2 300 35.00 33.0914 6 0.6 60 2 482.11 87.20 85.2915 6 0.6 60 2 300 85.00 86.2816 3 0.9 70 3 200 74.15 75.2017 6 0.6 60 2 300 85.00 86.2818 9 0.3 50 3 400 67.40 68.4519 6 1.14 60 2 300 87.00 85.0920 3 0.9 50 3 400 45.85 46.9021 6 0.6 60 2 117.88 65.00 63.0922 9 0.9 50 1 400 61.45 62.5023 11.46 0.6 60 2 300 87.90 86.0024 9 0.9 50 3 200 68.55 69.6025 6 0.6 60 2 300 85.00 86.2826 6 0.6 60 2 300 85.00 86.28

Where A, B, C, D and E represent the reaction independent variables as M:O molarratio, KOH concentration (wt%), reaction temperature (�C), reaction time (h) and

O. Aboelazayem et al. / Energy 157 (2018) 843e852 845

2.3. Statistical analysis

Model equation was defined to fit the experimental responsewith the independent variables. Where, it represented the responsevariable as function of the independent variables. Generally, RSMuse full-quadratic equation or a derived form of this equation, todefine the model. General second order model was defined asshown in Equation (2).

Y ¼ bo þXn

i¼1bixi þ

Xn

i¼1biix

2i þ

Xn�1

i¼1

Xn

j> i

bijxixj þ ε (2)

where Y is the dependent response, bo is the model coefficientconstant, bi, bii and bij, are coefficients for intercept of linear,quadratic and interactive terms respectively, while Xi and Xj areindependent variables (isj).

The model adequacy was checked by coefficient of correlation(R2), adjusted coefficient of determination (R2adj) and the predictedcoefficient of determination (R2pred). Statistical significance wasinvestigated using ANOVA by calculating the Fisher's F-test at 95%confidence level. Statistical significance of the results was pre-sented by p-value, where the result is significant when p-value isless than 0.05. Lack-of-fit analysis is one of the important analysisby ANOVA, which measures the failure of the regression model torepresent experimental data points. Model fitting to the experi-mental data can be concluded from the significance of the modelregression analysis and the non-significance of the model lack-of-fit [19].

stirring rate (rpm), respectively. While X1 and X2 represent experimental and pre-dicted results of biodiesel yield, respectively.

2.4. Reaction kinetics

In transesterification overall reaction, biodiesel is produced byreacting TG with methanol to produce fatty acid methyl esters(FAME) and GL. The rate equation of transesterification reactionwith respect to FAME formation can be written as shown inEquation (3).

r ¼ �d½TG�dt

¼ k1½TG�a½MeOH�b � k2½GL�l½FAME�m (3)

where [TG], [MeOH], [GL] and [FAME] represent the concentration oftriglycerides, methanol, glycerol and fatty acid methyl esters,respectively. While, a, b, l and m represent the reaction order co-efficients for concentration of triglycerides, methanol, glycerol andfatty acid methyl esters, respectively. Also, k1 and k2 represent thereaction rate constants for both forward and backward reactions,respectively.

By considering that the reaction is irreversible, and that meth-anol concentration is constant (as it has been used in excess) in thereaction, backward reaction rate and methanol concentration wereneglected in the reaction rate equation as shown in Equations (4)and (5).

Table 1Experimental design variables.

Factor Code Levels

-a �1 0 þ1 þaM:O (molar ratio) A 0.53 3 6 9 11.46KOH concentration (wt%) B 0.053 0.3 0.6 0.9 1.14Temperature (�C) C 41.78 50 60 70 78.21Time (h) D 0.17 1 2 3 3.82Stirring rate (rpm) E 117.88 200 300 400 482.11

r ¼ �d½TG�dt

¼ k1½TG�a (4)

d½TG�½TG� ¼ �k1dt (5)

In order to calculate the conversion without analysing the finalconcentration of the triglycerides, the fact which states that themolecular weight of the TG is three times that of FAME should beconsidered using Equation (6) [20].

X ¼ ½FAME�3½TG�o

¼ mFAME=MFAME3mTGo

�MTG

¼ mFAME=3MFAMEmTGo

�MTG

¼ mFAMEmTGo

¼ Y

(6)

where [FAME] is the concertation of the fatty acids methyl esters,mFAME is the mass of fatty acids methyl esters, MFAME is the mo-lecular mass of fatty acids methyl esters. While Y and X representyield of FAME and conversion of TG, respectively.

2.5. Physiochemical properties of the produced biodiesel

Raw castor oil, biodiesel and the blended biodiesel/petro-dieselsamples were analysed for investigating their physicochemicalproperties. The analysed properties were replicated twice, and thefinal results were obtained as an average of the two results. Table 3shows the properties of the crude castor oil which represents thefeedstock for biodiesel production.

2.6. Engine setup

The experiments were performed on a naturally aspirated direct

O. Aboelazayem et al. / Energy 157 (2018) 843e852846

injection (DI) diesel engine (model SJ-65 Peter type) with a bowl-in-piston combustion chamber. The engine is coupled to a DCgenerator type STC-6 with a maximum power of 6 kW to load theengine. Table 4 summarises the engine specifications. The fuelswere stored at two separate tanks where the first tank suppliedpetro-diesel fuel while the other tank supplied a specific blend ofbiodiesel/petro-diesel fuel. The output signals of the engineincluding; rpm, temperature and cylinder pressurewere coupled toa suitable data acquisition (DAQ) system. Fig. 1 shows a schematicdiagram of the engine setup. The exhaust emissions includingcarbon monoxide (CO), carbon dioxide (CO2), hydrocarbons (HC),nitrogen oxides (NOx), Oxygen (O2) and particulate matter (PM).Were analysed using Infralyt Smart Exhaust Gas Analyser (SAXONJunkalor Ltd., Germany). Different biodiesel percentages in blendswere considered including; 0, 5, 10, 15 and 20 vol% which werecoded as B0, B5, B10, B15 and B20, respectively. The average of thetriplicate experiments was considered as the result for each blend.The experiments were operated at constant speed of 1400 rpm.

3. Results and discussion

3.1. Model fitting and adequacy checking

Design Expert software has generated a regression equationrepresenting an empirical relationship between the response var-iable and the reaction parameters. Polynomial quadratic model hasbeen developed to fit the experimental results as shown in Equa-tion (7).

Y ¼ 86:28þ 14:52Aþ 8:79Bþ 10:71C þ 10:41Dþ 6:10E� 0:85ABþ 2:7E � 3AC þ 0:5ADþ 1:27AE þ 3:07BCþ 0:87BD� 1:07BE þ 4:42CDþ 2:48CE þ 0:28DE � 8:06A2

� 5:18B2 � 5:97C2 � 5:8D2 � 3:65E2(7)

Where, Y is the dependant variable of biodiesel yield while, A, B, C,D and E are the independent variables; M:O molar ratio, KOHconcentration, temperature, time and stirring rate, respectively.

The proposed model has been examined for adequacy to iden-tify any errors associated with the normality assumptions. Theregression equation illustrates the effect of different reaction vari-ables on the response. The positive sign of each term indicatessynergetic effect while the negative sign indicates antagonistic ef-fect [21].

ANOVA has been used to validate the RSM model coefficientusing F-test and p-value as shown in Table 5, these values are 36.33and 0.0004, respectively, which proved that the developedquadratic model is highly statistically significant. Lack-of-fit p-value of the model has been observed to be 0.57 (not significant),which illustrated that the model is representing most of theexperimental data successfully.

The determination coefficient values, R2 and R2adj, which mea-sure the reliability of the model fitting, have been calculated to be0.9932 and 0.9658, respectively. These values indicated thatapproximately 99.32% of the variance has been attributed to the

Table 3Crude castor oil properties.

Test Standard Method Unit Results

Density ASTM D4052 kg/m3 963.4Kinematic viscosity ASTM D445 cSt 248TAN ASTM D974 mg KOH/g oil 0.7

variables which insured the high significance of the predictedmodel. These results concluded that only 0.0068 of the total vari-ation is not well clarified by the developed model, which ensuredthe model fitting to the experimental data.

3.2. Interactive effect of process variables

3.2.1. Interactive effect of M:O molar ratio and KOH concentrationFrom the ANOVA results, it has been concluded that M:O molar

ratio has highly statistical significant effect on biodiesel yield withp-value of 0.0001 and F-value of 116.32. Fig. 2 illustrates theinteractive effect of M:O molar ratio and KOH concentration onbiodiesel yield where the other variables were at constant value i.e.temperature, time and stirring rate of 60 �C, 2 h and 300 rpm,respectively. M:O molar ratio has a directly proportional relation-ship with biodiesel yield from range of 3:1 to 7:1. However, furtherincreasing of M:O molar ratio has no valuable effect on biodieselyield. Silitonga et al [22] have observed similar observation wherethey concluded that the effect of M:O molar ratio has highly sta-tistical significant effect on biodiesel yield from range of M:O molarratio from 3:1 to 6:1. They reported that at higher M:O molar ratiothan 9:1, the increase of M:O molar ratio has negative effect onbiodiesel yield resulting in decreasing it. Kilic et al [23] have alsoreported that M:O molar ratio has directly positive linear rela-tionship with biodiesel yield but within range 5:1 to 7:1.

ANOVA results showed that catalyst concentration has signifi-cant effect on biodiesel yield where p-value was concluded to be0.0013 and F-value was 42.56. It is clearly shown in Fig. 2 thatincreasing KOH concentration affects biodiesel yield positively fromthe range between 0.3 and 0.65% where other variables were keptconstant i.e. temperature, time and stirring rate of 60 �C, 2 h and300 rpm, respectively. While at higher concentration, it has nosubstantial effect on biodiesel yield. Sanchez et al [7] have studiedthe effect of KOH catalyst on biodiesel yield. They have reportedthat catalyst concentration has positive effect on biodiesel yieldwhere increasing of catalyst concentration increases biodieselyield. Kilic et al [23] have reported similar results where theyconcluded direct proportional relationship between catalyst weightpercentage and biodiesel yield. They have studied catalyst weightpercentage within range between 0.5 and 1.5wt percentage.

At low molar ratio values (

Table 4Engine specifications.

Model Specifications

Engine Configuration Single cylinder, vertical, compressionignition and four strokes.

Bore 85mmStroke 110mmCompression ratio 17:1Cooling system Water cooledRated output 3.8 kW at 1500 rpmNumber of valves 2

Fig. 1. Schematic diagram of the engine setup.

O. Aboelazayem et al. / Energy 157 (2018) 843e852 847

at higher reaction temperature than 65 �C, methanol evaporationaffects biodiesel yield where the process of evaporation andcondensation again through the condenser fitted in the reactionhas negative effect on biodiesel yield. Sanchez et al [7] reported that

Table 5ANOVA of biodiesel predicted quadratic model for biodiesel yield.

Source Sum of Squares df Mean Square F-Value p-value

Model 8740.54 20 437.03 36.33 0.0004A-M:O molar ratio 1399.20 1 1399.20 116.32 0.0001B-KOH wt% 512.000 1 512.000 42.56 0.0013C-Temperature 760.500 1 760.500 63.22 0.0005D-Time 718.200 1 718.200 59.71 0.0006E-Stirring rate 246.420 1 246.420 20.49 0.0062AB 2.210 1 2.210 0.18 0.6860AC 0.0002 1 0.0002 0.0001 0.9989AD 0.78 1 0.78 0.063 0.8093AE 4.89 1 4.89 0.41 0.5517BC 28.73 1 28.73 2.39 0.1829BD 2.30 1 2.30 0.19 0.6800BE 3.51 1 3.51 0.29 0.6123CD 5.79 1 5.79 4.97 0.0763CE 18.82 1 18.82 1.56 0.2664DE 0.25 1 0.25 0.02 0.8920A2 1347.24 1 1347.24 112.0 0.0001B2 566.78 1 566.78 46.29 0.0010C2 730.50 1 730.50 60.73 0.0006D2 697.49 1 697.49 57.98 0.0006E2 275.42 1 275.42 22.90 0.0049Residual 60.15 5 12.03Lack of Fit 60.15 1 60.15 0.4 0.57Pure Error 0 4 0Cor Total 8800.68 25

reaction temperature affects transesterification reaction of castoroil positively, where biodiesel yield increases with the increase ofreaction temperature.

According to the ANOVA results, reaction time showed highstatistical significant effect on biodiesel yield with p-value of0.0006. This significant effect is clearly shown in Fig. 3 whereincreasing time has positive effect on biodiesel yield from timerange between 60 and 150min where other variables were keptconstant i.e. M:O molar ratio, temperature, KOH concentration andstirring rate of 6:1, 60 �C, 0.6 wt% and 300 rpm, respectively. Then,at reaction residence timemore than 150min there is no significanteffect on biodiesel yield. Barbosa et al [24] have investigated theeffect of reaction time on performance of transesterification reac-tion on castor oil/soybean oil blends. They have concluded similarresults to that reported by the present study where they stated thatbiodiesel yield increases with the residence reaction time incre-ment between 1 h and 3 h while beyond 3 h biodiesel yield eitherdecrease or does not have significant change.

The positive interactive effect of reaction temperature and timewas obvious in the surface plot showed in Fig. 3, at constant M:O,KOH weight percentage and stirring rate of 6:1, 0.6 wt% and300 rpm, respectively. Low yield of biodiesel has been observed atlow reaction temperature and time. However, there was an overallincrease in the biodiesel yield with the increment of reactiontemperature and time. The biodiesel was produced more rapidlywith increasing temperature, which might be attributed to the in-crease in the reaction rates, the intensification of the mass transfercoefficients and the increment of the solubility of the components,where the reactants would be more evenly distributed betweenphases with the increase of the temperature [21].

3.3. Optimisation of reaction variables

Design Expert software has been used to develop the numericaloptimisation step by combining the desirability of each indepen-dent variable into single value and then search for optimum valuesfor the response goals. Biodiesel yield has been targeted to bemaximised while minimising reaction variables. The optimumconditions have been concluded using numerical optimisation atM:O molar ratio of 5.4:1, KOH concentration of 0.73%, reactiontemperature of 64 �C, reaction time of 2.5 h and stirring rate of320 rpm resulting in 97.82% and 2.05% for biodiesel and glycerolyields, respectively.

The predicted optimum conditions have been examined usingexperimental validation. The experiments have been performedthree times and the result has been considered as an average result.The experiments have resulted an average yield of biodiesel of98.39%. Accordingly, the predicted optimum conditions have beenvalidated with 0.59% relative error from the experimental results.The similarity between the experimental response results and thepredicted optimal response confirmed and verified the accuracyand adequacy of the predicted quadratic model of biodiesel yield.

3.4. Kinetics of the reaction

In this study, kinetic and thermodynamic data have beenexamined at the concluded optimum conditions for the overalltransesterification reaction of castor oil. The validated quadraticmodel has been used to predict the kinetic data required for kineticparameters calculations. Temperature range between 50 �C and62 �C and reaction time from 60 to 180min have been used toconclude the required kinetic data.

Pseudo second order reaction has been examined for kineticcalculations. A graphical representation has been plotted toexamine the fitting of the experimental data with the assumed

Fig. 2. Interactive effect of M:O molar ration and KOH weight percentage on biodiesel yield.

O. Aboelazayem et al. / Energy 157 (2018) 843e852848

second order reaction. A plot between j1/(1-X)j versus (t) withintime range from 60 to 180min was investigated to fit the

experimental data at constant other variables at their optimumconditions. The plot has concluded an accurate representation for

Fig. 3. Interactive effect of reaction temperature and residence time on biodiesel yield.

O. Aboelazayem et al. / Energy 157 (2018) 843e852 849

the experimental data with R2¼ 0.988 and slope of 0.16. From theplot, the rate constant of the reaction has been concluded to be0.16M�1min�1 at the concluded optimum conditions of thereaction.

In addition, the thermodynamic parameters have been

calculated throughout this study. Reaction rate constants have beenconcluded for temperature range between 50 �C to 64 �C where theother variables were kept constant at their optimum values. Thecalculated reaction rate constants were used to plot Arrheniusequation. Accordingly, activation energy and frequency factor have

O. Aboelazayem et al. / Energy 157 (2018) 843e852850

been concluded as 21.95 kJ/mol and 6.02M�1min�1, respectively.Cavalcante et al [10] have studied the effect of different variables

on ethanolysis of castor oil. They have observed that ethanol to oilmolar ratio, KOH catalyst loading, reaction time and the interactionbetween reaction time and catalyst loading have significant effecton biodiesel yield. Kilic et al [23] have optimised biodiesel pro-duction from castor oil using factorial design. They have concludedthat CH3OK is the optimum catalyst for biodiesel production. Theyhave reported a maximum value of 99.8% yield of biodiesel at7:1M:O molar ratio, 1.5% of catalyst concentration at 65 �C. Theyhave reported that temperature and M:O molar ratio are the mostsignificant parameters affecting biodiesel yield. Ramenzani et al[11] have studied optimisation of biodiesel production from castoroil. They have used KOH as a homogenous catalyst for the reaction.They have concluded the optimum conditions of M:O molar ratio,temperature, stirring rate and time of 8:1, 65 �C, 400 rpm and 2 h,respectively.

Apita and Temu [12] have studied the transesterification reac-tion of castor oil using KOH as basic homogenous catalyst at6:1M:Omolar ratio. Significantly, they found that the second-orderkinetics provided a satisfactory agreement with the experimentalresults. Chaudhary et al [13] studied the kinetics of the overalltransesterification reaction of castor oil using sulphuric acid as anacidic homogenous catalyst at 6:1M:O molar ratio. They haveconcluded that the transesterification reaction experimental datafits accurately with the second order irreversible reaction wherethey concluded the activation energy by 38.611 kJ/mol.

3.5. Physiochemical properties of the produced biodiesel

In this study, physicochemical properties of the final biodieselproduct have been analysed and compared to the European Bio-diesel Standard, EN14214 and Biodiesel International StandardsASTM D6751. Table 6 shows the concluded properties in

Table 6Physicochemical properties of the produced biodiesel.

Test Standard Method Pro

Kinematic viscosity at 40 �C (cSt) ASTM D-445 18.Density at 15 �C (g/cm3) ASTM D-4052 0.9Pour point (�C) ASTM D-97 �20Cloud point (�C) ASTM D-97 �13Flash point (�C) ASTM D-93 180Carbon residue (wt.%) ASTM D-189 0.2Sulphated ash (wt.%) ASTM D-874 0.0Ash (wt.%) ASTM D-3174 0.0Copper strip corrosion ASTM D-130 B1TAN (mg KOH/g oil) ASTM D-974 0.1Calorific value (MJ/kg) ASTM D-5865 35.Sulphur content (wt.%) ASTM D-4294 Nil

Table 7Properties of biodiesel/petro-diesel blends.

Test Unit Standard method

Kinematic viscosity at 40 �C cSt ASTM D-445Density at 15 �C g/cm3 ASTM D-4052Pour point �C ASTM D-97Flash point �C ASTM D-93Carbon residue wt.% ASTM D-189Sulphated ash wt.% ASTM D-874Ash wt.% ASTM D-3174TAN mg KOH/g oil ASTM D-974Calorific value MJ/kg ASTM D-5865Sulphur content wt.% ASTM D-4294

comparison with the standard limits.As shown in Table 6 most of the physicochemical properties

agree with both EN14214 and ASTM D 6751. However, both densityand viscosity of the produced biodiesel exceeds the limits of bio-diesel standard due to the existence of the hydroxyl group in rici-noleic acid. Accordingly, blends between biodiesel and petro-dieselhave been prepared to investigate the effect of blending on bothviscosity and density reduction. Prepared blends betweenbiodiesel/petro-diesel have been analysed as shown in Table 7. Ithas been concluded that up to B10 blend, the properties agree withthe biodiesel blends standard properties (ASTM D7467). Accord-ingly, B6 and B10 have been considered as the optimum biodiesel/petro-diesel blends.

3.6. Engine performance

In the present study, an increasingly effect for engine's BSFC hasbeen observed by increasing biodiesel in biodiesel/petro-dieselblend as shown in Fig. 4. These results are qualitatively in agree-ment with previous studies where BSFC reported significant in-crease while increasing biodiesel percentage in blends [15,16,25].However, the increasing effect of BSFC in the present study hasrecorded a percentage between 3 and 6.5% which is slightly lowercompared to previously published results for the same blendinglevels for castor oil [26]. From the other hand, BTE has slightlydecreased during this study by increasing biodiesel volume per-centage in biodiesel/petro-diesel as shown in Fig. 4. It has beenreported that some properties in biodiesel are responsible forlowering BTE for its blends including; lower cetane index, smallerignition delay and higher BSFC [15].

3.7. Emission characteristics

The HC emissions are realeased from the unburned fuel through

duced biodiesel ASTM D6751 Biodiesel (EN14214)

6 1.9e6.0 3.5e5.03512 0.86e0.9

< �4>130 >101

91

16

17

18

19

20

21

22

23

24

25

330

350

370

390

410

430

450

470

0 5 10 15 20 25

BTE(%)

BSFC

(kh/kW

.h)

Biodiesel in biodiesel/petro-diesel blend (vol%)

BSFC BTE

Fig. 4. BSFC and BTE versus biodiesel volume percentage.

O. Aboelazayem et al. / Energy 157 (2018) 843e852 851

the combustion process. In the present study, HC emissions havebeen reduced significantly while increasing biodiesel percentage inthe blend as shown in Fig. 5. The reduction percentage recorded41.6% for B20 in comparisonwith B0. These results are in agreementwith previous studies where they attributed the reduction of HC tothe higher oxygen content of biodiesel which leads to completecombustion [15,27]. Moreover, a significant reduction of CO emis-sions has been observed in the present study with 33.3% overallreduction as shown in Fig. 5. Roy et al. [28] have concluded similarresults where CO emission reduced with the increase of biodieselpercentage in the diesel fuel. It is reasonable to expect an increas-ingly effect of CO2 emissions while increasing biodiesel percentageas complete combustion has been enhanced. However, CO2 emis-sions have been slightly reduced in the present study whileincreasing biodiesel in the blend as shown in Fig. 5. Xue et al [25]have reported the same trend where they have attributed theseresults to the lower elemental carbon to hydrogen ratio in biodieselthan diesel fuel.

Particulates emission is considered the most complex anddangerous emission from diesel combustion exhaust. It is definedby the residuals matters including liquids and solids that condenseduring the dilution process [25]. The factors affecting PM emissionwere reported by Xue et al [25] including biodiesel composition,

Fig. 5. Exhaust emission analysis, where HC, CO, CO2, PM, NOx and O2 were measuredin; ppm, %vol.(*103), %vol.(*10), dg/m3, %vol.(*10�1), %vol., respectively.

properties of biodiesel and its feedstock, engine type, operatingconditions and finally the additives. In this study, PM emissionshave been significantly decreased while increasing the biodieselpercentage in the blend with 34% for B20 compared to B0 as shownin Fig. 5. Di et al [29] have explained that the reduction of PM isattributed to the reduction of sulphur in the blended fuel whichaccordingly reduce the sulphate in the particulates. They have alsoadded that the absence of polycyclic aromatic hydrocarbons (PAH),which are soot precursors and sources of particulates, might be areason for PM reduction in biodiesel blends.

In the present study, NOx emissions have increased whileincreasing biodiesel percentage in the blend as shown in Fig. 5. Themain factors affecting NOx emissions are the oxygen level andcombusting temperature [29]. Biodiesel is an oxygen rich fuelwhich accordingly increases the oxygen level in the combustionenvironment where oxygen emissions have reported increasinglyeffect as shown in Fig. 5. Moreover, the exhaust temperature hasincreased in the present study by 22% from B0 to B20 blends.Accordingly, these reasons have introduced the suitable environ-ment for NOx emissions to increase. Silva et al. [30] have reportedsimilar results where NOx emissions have increased whileincreasing biodiesel percentage in biodiesel/petro-diesel blend.Kumar et al [27] have suggested to add some additives to reduce theNOx emissions including cetane improver additives, oxygenatedadditive and metal-based additives.

4. Conclusions

Castor oil is a promising second-generation feedstock for bio-diesel production. The present study has concluded optimal reac-tion conditions at M:O molar ratio of 5.4:1, KOH concentration of0.73%, reaction temperature of 64 �C, reaction time of 2.5 h andstirring rate of 320 rpm resulting in 97.82% biodiesel yield. Thepredicted optimum conditions have been validated experimentallywith 0.59% relative error from the experimental results (98.39%).Reaction kinetics have been studied where reaction has beenobserved as pseudo second order with reaction rate constant andactivation energy of 0.16M�1min�1 and 21.95 kJ/mol, respectively.Physicochemical properties of the produced biodiesel and theprepared blends with petro-diesel have been analysed. Diesel en-gine performance and exhaust gasses emissions have been ana-lysed for the different biodiesel/petro-diesel blends to analyse theeffect of blending in reduction of greenhouse and pollutant gasses.Significant reduction for CO, CO2, HC and PM emission observedwhile increasing the percentage of biodiesel in biodiesel/petro-diesel blend.

Acknowledgment

The authors acknowledge The British University in Egypt (BUE)for funding this research.

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Biodiesel production from castor oil in Egypt: Process optimisation, kinetic study, diesel engine performance and exhaust e ...1. Introduction2. Material and methods2.1. Experimental setup2.2. Experimental design2.3. Statistical analysis2.4. Reaction kinetics2.5. Physiochemical properties of the produced biodiesel2.6. Engine setup

3. Results and discussion3.1. Model fitting and adequacy checking3.2. Interactive effect of process variables3.2.1. Interactive effect of M:O molar ratio and KOH concentration3.2.2. Interactive effect of reaction temperature and time

3.3. Optimisation of reaction variables3.4. Kinetics of the reaction3.5. Physiochemical properties of the produced biodiesel3.6. Engine performance3.7. Emission characteristics

4. ConclusionsAcknowledgmentReferences