Influence of biodiesel blending on physicochemical properties and importance of of biodiesel blend.pdf

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Influence of biodiesel blending on physicochemical properties and

importance of mathematical model for predicting the properties

of biodiesel blend

M.A. Wakil a,⇑, M.A. Kalam a, H.H. Masjuki a, A.E. Atabani b, I.M. Rizwanul Fattah a

a Center for Energy Sciences, Department of Mechanical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysiab Department of Mechanical Engineering, Faculty of Engineering, Erciyes University, 38039 Kayseri, Turkey

a r t i c l e i n f o

Article history:

Received 24 October 2014Accepted 16 January 2015

Keywords:

BiodieselEdible oilNon-edible oilBlendingPhysicochemical propertiesMathematical modeling

a b s t r a c t

The growing demand for green world serves as one of the most significant challenges of modernization.Requirements like largest usage of energy for modern society as well as demand for friendly milieu createa deep concern in field of research. Biofuels are placed at the peak of the research arena for their under-lying benefits as mentioned by multiple researches. Out of a number of vegetable oils, only a few are usedcommercially for biodiesel production. Due to various limitations of edible oil, non-edible oils are becom-ing a profitable choice. Till today, very little percentage of biodiesel is used successfully in engine. Theresearch is still continuing for improving the biodiesel usage level. Recently, it is found that the blendedbiodiesel from more than one feedstock provides better performance in engine. This paper reviews thephysicochemical properties of different biodiesel blends obtained from various feedstocks with a viewto properly understand the fuel quality. Moreover, a short description of each feedstock is given alongwith graphical presentation of important properties for various blend percentages from B0 to B100.Finally, mathematical model is formed for predicting various properties of biodiesel blend with the helpof different research data by using polynomial curve fitting method. The results obtained from a number

of literature based on this work shows that the heating value of biodiesel is about 11% lower than dieselexcept coconut (14.5% lower) whereas kinematic viscosity is in the range of 4–5.4 mm2/s. Flash point of all biodiesels are more than 150 C, except neem and coconut. Cold flow properties of calophyllum, palm, jatropha, moringa are inferior to others. This would help to determine important properties of biodieselblend for any percentage of biodiesel and to select the proper feedstock for better performance.

2015 Elsevier Ltd. All rights reserved.

1. Introduction

The primary catalyst of any country’s socio-economic devel-opment is energy. However, through modernization the demandof energy consumption is facing a serious threat due to the grad-ual declination of fossil fuels. Various sectors for instance, indus-try, transport, agriculture, domestic sector, etc. require energyfrom sources like wood, coal, petroleum products, nuclear power,solar, and wind [1]. Currently, more than 80% of energy demandis catered by fossil fuels [2]. The deep concern about fossil fuelsis that it’s generation of toxic pollutants links to global warming,climate change and even some impasse diseases [3]. To competewith this critical situation, a good number of research have been

conducted to find alternative to fossil fuels for eco-friendlycondition.

Biodiesel is considered to be a notable option for at least com-plementing conventional fuels [3]. Its production from renewablesources such as vegetable oils and fats has been widely reviewed[4–10]. It is advantageous over petroleum product because it issafe in handling, biodegradable, non-toxic, has higher combustionefficiency, higher cetane number, contains no sulfur, etc. [1,3,11–14].In addition, it is advantageous for numerous social benefits likerural revitalization, creation of new jobs and reduced globalwarming [15].

Among the available sources of biodiesel, edible oils are domi-nating in several countries as diesel substitute. For instance, canolaand soybean are used in USA, palm oil in Malaysia, rapeseed oil inEurope etc. [12,14]. Currently, more than 95% of the world biodie-sel comes fromedible oil. In the year 2004–2007 the edible oil usedfor biodiesel production was 6.6 million tons which would attri-bute 34% of the increase in global consumption of biodiesel and

http://dx.doi.org/10.1016/j.enconman.2015.01.043

0196-8904/ 2015 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Tel.: +60 163269524.

E-mail addresses: [email protected] (M.A. Wakil), [email protected] (M.A.Kalam).

Energy Conversion and Management 94 (2015) 51–67

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also lead to one third of the total projected growth of edible oilsbetween 2005 and 2017 [16]. This large usage of edible oils for bio-diesel has caused a serious impact on food supply. It has the abilityto lead to starvation especially in developing countries and imposeantagonistic effect on environment [13]. The prominent solution isto use second generation feedstocks (non-edible oils) which hashigher potential for biodiesel production [13] and can easily elim-

inate the food vs fuel concern. Another boosting feedstock is algae.Although full scale commercialization from algae has not begunyet, but it is expected to be rich in oil content (oil content in mic-roalgae can exceed 80% of its weight of dry biomass) [3].

The use of vegetable oils started more than a century ago. Apartfrom the remarkable advantages, biodiesel has couple of difficul-ties to be used as a replacement of fossil fuels in engine such ashigh viscosity and density and low volatility and heating value[12]. These difficulties lead to problems in pumping, atomization,gumming, injection fouling, piston ring sticking, etc. [1]. Anotherserious threat for biodiesel industry is the cost of feedstock whichcurrently accounts for over 70–85% of biodiesel production cost[13,17,18]. One solution to alleviate this problem is to use multiplefeedstocks of varying percentage. It will not only subside the cost

of production but also enhance product quality. Problems of usingedible oils can also be moderated by switching these with non-edi-ble oils. It has been proven that biodiesel containing up to B5 willhave no notable difference in terms of power and fuel economywhen it is compared to diesel [19]. ASTM D7467 suggests blendingof 20% biodiesel with diesel. In 2014, the Chevy Cruze Clean TurboDiesel is directing the engine with rated B20 biodiesel compatibil-ity [20]. Now-a-days research is going on to increase the use of bio-diesel blending with diesel. Consequently, biodiesel blending(biodiesel and diesel) bring a new topic in research arena. A num-ber of researches have been undertaken already on biodieselblending [17,21–27]. Accordingly, it has become easier to have aclear concept of the physicochemical properties of edible andnon-edible vegetable oils with varying blending percentages for a

better understanding on blend qualities. Survey of existingliterature shows that most of the studies focus on pure biodiesel

properties rather than properties of blending. Therefore, thisreview aims firstly at focusing on the physicochemical propertiesof edible and non-edible biodiesel and their blends with diesel(B0–B100). Secondly, mathematical equation for various biodieselblends would be produced in order to predict the important prop-erties of blended biodiesel for any percentage of biodiesel. Here, apolynomial curve fitting method is used to generate the equation.

It is believed that such kind of studies will assist researchers forfurther study about optimal usage of biodiesel.

2. Biodiesel feedstocks

Feedstock-related cost has been regarded as a primary obstacleas it constitutes roughly around 60–90% of the total biodiesel pro-duction cost [28]. Biodiesel can be produced from a wide variety of oils. These include vegetable oils (edible and non-edible oils)[13,29–34], food processing waste (waste cooking oils, animal fat(tallow, lard, yellow grease, chicken fat) [28,35–37]), industrial res-idues) [38], algae, halophytes (Salicomia bigelovii [39]), sewagesludge [40], etc.

Globally, more than 350 oil-bearing crops have been identifiedas potential biodiesel sources [12,13,29,41]. The regional climatemainly affects the feedstock selection for biodiesel production[13]. Table 1 presents some important oil bearing species[1,2,8,13,14,16,29,42].

A concise description of some edible and non-edible oil plantsincluding their country of origin, oil content and their necessaryuses are portrayed in Table 2 with their fatty acid composition inTable 3. The identification of plants and seeds of the selected oilsources are shown in Fig. 1.

3. Characteristics of crude oils and biodiesels

Characterization of oil properties is necessary to research aboutthe processing of crude oil to biodiesel and afterwards to dieselengine successfully. The physical and chemical properties of anyfuel are significant factors which help to decide whether the oil

Nomenclature

APME Aphanamixis polystachya methyl esterCIME Calophyllum inophyllum methyl esterCOME Coconut methyl esterCME Canola methyl esterCMME Croton megalocarpus methyl ester

JCME Jatropha curcas methyl esterMOME Moringa oleifera methyl esterNME Neem methyl esterPOME Palm methyl esterRBME Rice bran methyl ester

SME Sesame methyl esterSFME Sterculia foetida methyl esterCB10 Calophyllum biodiesel (10% + Diesel 90%) blendCoB Coconut biodiesel, diesel blendCrB Croton biodiesel, diesel blend

JB Jatropha biodiesel, diesel blendCP Cloud pointPP Pour pointCFPP Cold filter plugging point

Table 1

Oil species for biodiesel production.

Category Source of oil

Edible oil Sunflower, Rapeseed, Rice bran, Soybean, Coconut, Corn, Palm, Olive, Pistachia Palestine, Sesame seed, Peanut, Opium Poppy, Safflower oil, Amaranth,apricot, argan, artichoke, avocado, babassu, bay laurel, beech nut, ben, Borneo tallow nut, carob pod (algaroba), cohune, coriander seed, false flax, grapeseed, hemp, kapok seed, lallemantia, lemon seed, macauba fruit (Acrocomia sclerocarpa), meadowfoam seed, mustard, okra seed (hibiscus seed), perillaseed, pequi,(Caryocar brasiliensis seed), pine nut, poppy seed, prune kernel, quinoa, ramtil (Guizotia abyssinica seed or Nigerpea), rice bran, tallow, tea(camellia), thistle (Silybum marianum seed), and wheat germ

Non-edibleoil

Jatropha, Karanjaor Pongamia, Neem, Jojoba, Cottonseed, Linseed, Mahua, Deccan hemp, Kusum, Orange, Rubbe rseed, Sea Mango, Karanja or Honge,milk bush, Nagchampa, Rubber seed tree, Tobacco seed oil, Algae, Halophytes and Xylocarpus moluccensis

Waste or recycled oil

Animal fats Tallow, Yellow grease, chicken fat and by-products from fish oil, etc.

52 M.A. Wakil et al. / Energy Conversion and Management 94 (2015) 51–67

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Table 3

Fatty acid composition of crude edible and non-edible oils.

Oil C8:0 C10:0 C12:0 C14:0 C16:0 C16:1 C17:0 C18:0 C18:1 C18:2 C18:3 C18:4 C20:0

Aphanamixis polystachya(meliaceae) [13,70]

N/D N/D N/D N/D 23.1 N/D N/D 12.8 21.5 29 13.6 N/D N/D

Calophyllum inophyllum L. [13] N/D N/D N/D 0.09 14.6, 17.9 2.5 N/D 19.96,18.5

37.57, 42.7 26.33, 13.7 0.2, 2.1 N/D 0.94

Croton megalocarpus [52] N/D N/D N/D 0.1 6.5 0.1 0.1 3.8 11.6 72.7 3.5, o.4 N/D N/D Coconut oil [1,11] N/D 14 51,48.8 18.5,

19.97.5, 7.8 0.1 3 5, 4.4 1, 0.8 0 65.7 N/D

Jatropha curcas[1,13,16] N/D 0 .1 N/D 1.4, 0.1 12.6,15.6,15.1,14.2

0.7,0.9

0.1 5.5, 9.7,7.1

39.1, 40.8,44.7

41.6, 32.1,31.4, 32.8

0.2, 0.2 N/D 0.2, 0.

Moringa oleifera [70–72] N/D N/D N/D N/D 6.5, 7.8, 9.1 1.4,2.1

N/D 6.0, 5.5,2.7 72.2, 66.6,79.4

1.0, 8.1, 0.7 0.2 N/D 4, 0.905.8

Neem [3,11] N/D N/D N /D 0.2-0.26 13.6-16.2,18.1

N/D N/D 14.4-24.118.1

49.1-61.9,44.5

2.3-15.8,18.3

0.2 N/D 0.8-3.4

Palm [1,11,16] N/D N/D 0.1 1 42.8, 42.6 0.3 N/D 4.5, 4.4 40.5 10.2, 10.1 0.2 1.1 N/D Rice bran [3,11,13,73] N/D N/D N/D 0.3, 0.8

0.4–0.6,

12.5, 17.7

11.7–16.5

0.23 N/D 2.1, 2.2

1.7–2.5

47.5, 40.6

39.2–43.7

35.4,35.6

26.4–35.1

1.1, 1.8 N/D 0.2, 0.

0.6,

Sesame [1] N/D N/D N/D N/D 13.1 N/D N/D 3.9 52.8 30.2 N/D N/D N/D Stauntonia chinensis [28] N/D N/D N/D N/D 6.87 0.21 N/D 1.19 79.95 8.32 0.13 N/D 1.72 Raphanus sativus [74] N/D N/D N/D N/D 6.13 0.05 N/D 1.68 23.87 13.46 10.34 N/D 0.68 Annona diversifolia [75] N/D N/D N/D N/D 16.40 N/D N/D 5.22 70.42 7.97 N/D N/D N/D Syagrus coronate [76] 9.0 6.0 42.0 16.0 8.0 N/D N/D 4.0 12.0 3.0 N/D N/D N/D Syagrus coronate [77] 6 6 37 11 8 N/D N/D 3 24 5 N/D N/D N/D chufa sedge [78] N/D N/D N/D 0.1 13.1 2.1 N/D 2.8 61.6 17.2 1.4 N/D 0.7 Citrus reticulate [79] N/D N/D N/D N/D 26.90 N/D N/D 4.62 26.75 37.65 3.80 N/D 0.26 Phoenix dactylifera [80] N/D N/D 24 13 17.44 N/D N/D


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4.2. Density

The air–fuel ratio and energy content of the air fuel mixturelargely depend on fuel density within the combustion chamberof diesel engine [14]. In general, density of biodiesel is slightlyhigher than petro diesel and it is augmented by increasing biodie-

sel percentage in blends [12,14]. Fig. 3 shows density variationswith blend percentage variations. It is found that except Neem bio-diesel (having higher density 0.891 g/cc at 40 C) the density of other feedstocks biodiesel are lower than 0.87 g/cc. Moreover, Ricebran and Sesame biodiesels have the same trend of increasing den-sity (0.849, 0.853, 0.857, and 0.86 at 50%, 60%, 70% and 80% blendpercentage). Except Aphanamixis, Calophyllum and Neem, densityof other biodiesel varies slightly with the rise of biodiesel percent-ages in blend.

4.3. Calorific value

In general, biodiesel has lower calorific value than diesel

because of its higher oxygen content [12–14]. Among the datapresented in Fig. 4, it is found that only Aphanamixis (Pitraj) and

Coconut biodiesel contain significantly lower calorific value(38,080 and 37,722 kJ/kg on an average) where the calorific valueof other biodiesels are nearly 40,000 kJ/kg. The heating value of blended biodiesel is higher than biodiesel and slightly lower thandiesel. The heating value decreases marginally with the increasingpercentages of biodiesel in blend. With the rise of blend percentage

(for example, B20–B30–B40, etc.), calorific value decrease to about250–400 kJ/kg except coconut biodiesel blend which decreasequite higher (about 700 kJ/kg). Up to B60, Palm, Rice bran and Ses-ame biodiesels have shown considerable heating value above42,000 kJ/kg. This value is 7% lower than petro diesel where purebiodiesel has normally 12% lower calorific value than diesel.

4.4. Flash point

Flash point is a measure of flammability of fuels which is inver-sely proportional to volatility [12–14]. The biofuels specificationfor flash point is meant to protect against contamination for highlyvolatile matters. In general, biodiesel has higher flash point than

petro-diesel. The average flash point of pure biodiesel is almostdouble than that of diesel. There is an increasing trend of flash

Fig. 1. Some pictures of edible and non-edible plants and seed.



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Xylocarpus moluccensis Manchurian apricot (Prunus mandshurica Skv.)

Siberian apricot (Prunus sibirica L.)

Baobab (Adansonia digitata L.)

Fig. 1 (continued)

Table 4

U.S. and European specification for biodiesel.

Property U.S. (ASTM D6751-08) Europe (EN 14214)

Test methods Limit Test methods Limit

Kinematic viscosity at 40 C (mm2/s) D 445 1.9-6.0 EN ISO 3104 3.5-5.0Density at 15 C (kg/m3) D 1298 880 EN ISO 3675/12185 860-900Calorific value (MJ/kg) – – EN14214 35Flash point C D 93 93 EN ISO 3679 101 min.Pour point (C) D 97 15 to 16 – –Cloud point (C) D 2500 3 to 12 – –Cold filter plugging point (CFPP) (C) ASTM Max + 5 EN 14214 –Cetane number D 613 47 min EN ISO 5165 51 minOxidation stability at 110 C (h) D 675 3 min EN 14112 6 minAcid value (mg KOH/g) D 664 0.5 max EN 14104 0.5 maxFree glycerin (wt% max) D 6584 0.02 EN 14105 0.02Total glycerin (wt% max) D 6584 0.24 EN 14105 0.25Carbon residue (wt% max) D 4530 0.05 EN 10370 0.30e

Copper strip corrosion (3 h at 50 C) D130 No. 3 (max.) EN 2160 No. 1Iodine value (g/l2/100 g) max. – – EN 14111 120Water and sediments (vol%, max) D 2709 0.05 EN 12937g 0.05Total sulfur (ppm), max D 5453 15b EN 20846 10Phosphorous (ppm), max D 4951 10 EN 14107 4

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point for biodiesel blends as portrayed in Fig. 5. Calophyllum andcoconut biodiesel have shown considerably lower flash point(122 and 139 C on average) than other biodiesels (APME = 170,CMME = 178, JCME = 166, MOME = 163, NME > 150 [118],POME = 160, RBME = 185, SME = 186 C on average). It is seen from

data that the variation of flash point basically occurs within therange of 3–8 C with the increase of blend B20. This trend is foundup to B60, but the variation is increased about 15–30 C when bio-diesel percentage increase above 60% in blend.

4.5. Cloud point (CP), pour point (PP), and cold filter plugging point

(CFPP)

These properties are considered to be cold flow properties asthey establish the limit for the use of fuels under cold weather con-ditions [2,13,14,119,120]. The cloud point is the lowest tempera-ture at which smallest observable cluster of wax crystal firstappears [120]. Pour point is the lowest temperature at which the

wax becomes semisolid and loses its flow characteristics. Cold fil-ter plugging point is an estimation of lowest temperature at whichfuel will provide a trouble free flow in certain fuel systems[13,120]. In general, biodiesel has higher CP and PP than diesel.The CP and PP of biodiesel feedstock largely depends on fatty acidcomposition [12,13]. The freezing point of biodiesel increases withthe increase of carbon atoms in carbon chain and decrease withdouble bonds [29,121]. It is found from Fig. 6 that Moringa andPalm have rising trend of cloud point while Croton gives thereverse trend. Maximum cloud point is noted on Moringa (19 C)and it varies from 8 to 19 C for the blends. The minimum cloudpoint is observed on croton (4 C).

The minimum pour point was observed for Coconut at 20% bio-diesel blend (15 C) and it increases with blend percentages as

shown in Fig. 7. While the highest pour point was found for Morin-ga 19 C, sesame biodiesel has a little variation in pour point (0–

1 C). Jatropha and croton show a moderate variation of 0–3 Cand 3 to 0 C, respectively.

Jatropha and Palm biodiesel have the same trend of CFPP(Fig. 8). Coconut and Croton were found to have decreasing trendsof CFPP (5 to 4 C), (5 to 6 C) while Aphanamixis, Calophyllum,

Jatropha and Palm biodiesel have increasing trend with theincrease of biodiesel blends. The minimum CFPP was found at90% biodiesel blend for Croton which is 6 C and for Coconut at90% and 100% blend (4 C). Moreover, pure Moringa and Sesamebiodiesel show 2 C and 3 C respectively.

4.6. Oxidation stability

Oxidation stability is a prominent parameter that assesses thefuels quality. Oxidation stability of biodiesel is generally influencedby some factors such as presence of air, heat, traces of metal, per-oxides, light and fatty acid composition [12]. The presence of dou-ble bonds in biodiesel results in a high level of reactivity withoxygen, especially when placed in direct contact with air, sunlight

or water [122–124] which afterwards affects engine adversely.From Fig. 9, it is clear that with the rise of blend percentages theoxidation stability is waning. Moringa biodiesel has the best stabil-ity (26.2 h at 110 C) than other feedstocks at B100 and 88.84 h,71.27 h and 64.25 h for B40, B60 and B80 respectively, the reverseresults were found for Calophyllum biodiesel (0.09 h at 110 C). Onthe other hand, Coconut biodiesel also has a good oxidation stabil-ity (113.06, 85.88, 64.54, 56.55, 41.05, 32.08, 23.23, 5.12) for B20,B30, B50, B60, B70, B80, B90, B100 respectively. On the other hand,Croton, Sesame and Rice bran biodiesel give moderate stability.

5. Mathematical modeling for predicting the important

properties of biodiesel and its blend

The prediction of important physical and chemical properties of biodiesel and its blends (weather with diesel or biodiesel) is a very

Table 5

Properties of crude edible and non-edible oils.

Properties Aphanamixispolystachya [89]

Calophyllum[2]

Coconut [2] Croton[2]

Jatropha [2] Moringa[2]

Palm [2] Ricebran[90]

Sesame[90]

Neem [91]

1 Heating value (kJ/kg) 38729 38,511 37,806 39,331 38,961 39,762 39,867 39,548 39,386 32,000–40,000[92]

2 Kinematic viscosity

(mm2/s) at 40 C

35.093 55.478 27.64 29.844 48.095 43.468 41.932 52.225 34.087 35.83

3 Kinematic viscosity(mm2/s) at 100 C

7.2547 9.5608 5.9404 7.2891 9.1039 9.0256 8.496 10.393 7.6364 –

4 Viscosity Index (VI) 177.9 165.4 168.5 224.2 174.1 195.2 185.0 192.8 202.9 –5 Density (kg/m3) at

40 C0.9164 0.9249 0.9089 0.9100 0.9054 0.8971 0.8998 0.9069 0.9066 0.9200

6 Flash point (C) – 236.5 264.5 235.0 258.5 263.5 254.5 300.50 280.0 1007 CFPP (C) – 26 22 10 21 18 23 44 11[92]8 Cloud point (C) 5 8 17 – 9 ± 1[1] 10 23[93] 0 3 199 Pour point (C) 4 8 19 – 4 ± 1[1] 11 12[93] 0 4 10

10 Refractory Index 1.4789 1.4784 1.4545 1.4741 1.4652 1.4661 1.4642 1.4718 1.4709 –11 Oxidation stability (h

at 110 C)0.09 0.23 6.93 0.14 0.32 41.75 0.08 4.40 9.795 12.4 [92]

12 Acid value (mg KOH/g oil)

26.7 41.74 11.6[3] 12.073.343[94]

14.47[86] 8.622.90[3]

18.5[95]7.40[93]

1.314 13.56 32.64 [3]

13 Transmission (%T) 61.6 34.7 91.2 87.5 61.8 69.2 63.2 87.10 78.4 –14 Copper strip

corrosion 3 h at 50

C

– 1a 1a 1a 1a 1a 1a 1a – –

15 Absorbance (Abs) 0.209 0.46 0.04 0.058 0.209 0.16 0.199 0.06 0.10616 MIU (wt%) [95] – – 2.74 – 0.16 0.30 0.03 2.74 – 2.1617 FFA (wt%) [95] – – 0.07(Lauric

acid)1.68[93] 1.17(Palmitic

acid)0.21 0.54 0.05 – 2.14

18 Sulfur (ppm) [95] – – 2.7 – 3.5 31.4 1.0 4.0 0[b][68] 199019 Phosphorous (ppm)

[95]– – 2.0 – 322.9 7.3 7.3 0.9 – 47.6



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important factor in the design of fuel spray, atomization andcombustion and emission system for diesel engines. It is also ahighly demanding parameter because research is going on withvarious feedstocks for biodiesel production. These equations wouldhelp to predict the property at any percentages of biodiesel inbiodiesel–diesel blend. Recently, several studies have beenconducted to examine the physical and chemical properties of biodiesel–diesel blends. The following paragraph will summarizethe most important works done in this aspect.

Saxena et al. [14] reviewed various methods for the predictionof important thermophysical properties such as cetane number,kinematic viscosity, density, higher heating value, flash point,cloud point pour point, cold filter plugging point and vapor pres-sure for various biodiesel feedstocks.

Sivaramakrishnan and Ravikumar [125] developed an equationto calculate cetane number of various vegetable oils and their bio-diesel from their viscosity, density, flash point and higher calorificvalue. They concluded that this equation gives an accuracy of 90%.

Atabani et al. [2] discussed the concept of biodiesel–biodieselblending to improve the properties of some feedstocks. Forinstance, blending of Sterculia feotida methyl ester (SFME) andcoconut methyl ester (CoME) improves the viscosity of (SFME)from 6.3717 mm2/s to 5.3349 mm2/s (3:1), 4.4912 mm2/s (1:1)and 3.879 mm2/s (1:3) respectively. Similar work was conductedon the effect of biodiesel–biodiesel blending on cloud point, pourpoint and cold filter plugging point. The properties at different bio-diesel–biodiesel blends percentages were estimated using thepolynomial curve fitting method. This paper concludes that blend-ing of edible and non-edible biodiesel feedstocks could be consid-ered as an approach to improve the properties of the final product.

Moser [17] indicated that the fuel properties of Soybean methylester were improved by blending with Canola, Palm and Sunflowermethyl esters to satisfy the IV (6 h) specificationscontained within EN 14214. The CFPP of Palm methyl ester wasimproved by up to 15 C through blending with Canola methylester. Statistically significant relationships were elucidated

Table 6

Properties of edible and non-edible methyl esters.

Properties Aphanamixispolystachya [89]

Calophyllum[2]

Coconut[2]

Croton [2] Jatropha[2]

Moringa[2]

Palm[2]

Ricebran[90]

Sesame[90]

Neem [91]

1 Heating value (kJ/kg) 39,960 39,513 38,300 39,786 39,738 40,115 40,009 39,957 39,996 39,8102 Kinematic viscosity

(mm2/s) at 40 C4.7177 5.5377 3.1435 4.0707 4.9476 5.0735 4.6889 5.3657 4.3989 3.70

3 Kinematic viscosity(mm2/s) at 100 C

1.8239 1.998 1.3116 1.6781 1.8557 1.9108 1.7921 1.9609 1.7236 –

4 Viscosity Index (VI) 220.7 183.2 230.8 276.3 194.6 206.7 203.6 187 229.0 –5 Density (kg/m3) at

40 C0.8735 0.8776 0.8605 0.8704 0.8742 0.8597 0.8591 0.8681 0.8848 0.8680

6 Flash point (C) 188.5 162.5 118.5 164.0 186.5 176.0 214.5 174.50 208.5 76, 120[92]7 CFPP (C) 5 11 1 4 10 18 12 1 11[96]8 Cloud point (C) 8 12 1 3 10 21 13 0 1, 6[68] 9[92],

14.4[96]9 Pour point (C) 8 13 4 2 10 19 15 3 1, 14[68] 2[92]

10 Cetane no. – 57.3[13] 59[1] 46.6[52] 55.4[97],57.1[13] 67.07[71] 52[1] 73.6[13] 50.48[68] 48–53[92]

11 Refractory Index at 25(C)

– 1.4574 1.4357 1.4569 1.4513 1.4494 1.4468 1.4541 – –

12 Oxidation stability (hat 110 C)

0.16 6.12 8.01 0.71 4.84 12.64 23.56 1.61 1.14 7.1

13 Acid value (mg KOH/g)[96]

0.448 0.30 0.106 0.16[94],0.2[98]

0.156 0.185 0.046 0.586 0.3[67] 0.649[96]

14 Free glycerin (%mass)[96]

– – 0.025 0.019[51] 0.006 0.001 0.003 0.001 – 0.02[92]

15 Total glycerin (%mass)[96]

– – 0.065 0.22[51] 0.10 0.067 0.068 0.083 – 0.158[96],0.26[92]

16 Sulfur (ppm) [96] – 4.11 0.94 – 3.84 9.9 1.81 6.0 0.0[68] 473.8[96]17 Carbon residue [96] – – 0.01 – 0.026 0.033 0.01 0.047 0.6214[68] 0.105[96]

Properties Stauntonia chinensis[28]

Raphanus sativus

[74] Annona diversifolia

[75]Manchurian apricot

[99]Siberian apricot

[99]

1 Heating value (kJ/kg) N/D N/D 36.3 N/D N/D2 Kinematic viscosity (mm2/s) at 40 C 4.48 N/D 4.451 4.32 4.343 Kinematic viscosity (mm2/s) at

100 CN/D N/D N/D N/D N/D

4 Viscosity Index (VI) N/D N/D N/D N/D N/D5 Density (kg/m3) at 40 C N/D N/D N/D N/D N/D6 Flash point (C) 167 N/D N/D 180 1757 CFPP (C) -9 6 N/D -15 -148 Cloud point (C) N/D N/D 0 N/D N/D9 Pour point (C) N/D N/D -9 N/D N/D

10 Cetane no. 52.1 N/D 44.7 49.7 49.211 Refractory Index at 25 (C) N/D N/D N/D N/D N/D12 Oxidation stability (h at 110 C) 2 N/D N/D 2.9 2.713 Acid value (mg KOH/g) 0.12 0.082 0.5 N/D N/D14 Free glycerin (%mass) 0.003 0.000 N/D 0.015 0.01715 Total glycerin (%mass) 0.14 0.108 N/D 0.16 0.1416 Sulfur (ppm) 5 (mg/kg) 0.79 (mg/kg) N/D 4.5 (mg/kg) 4.7 (mg/kg)

17 Carbon residue 0.05 N/D N/D N/D N/D


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between oxidation stability and iodine value, oxidation stabilityand saturated fatty acid methyl ester (Sunflower methyl ester)content, oxidation stability and CFPP, CFPP and iodine value, andCFPP and Sunflower methyl ester content. However, the only prac-tically significant relationship was that of CFPP vs. Sunflowermethyl ester content when Sunflower methyl ester content wasgreater than 12 wt%.

Oghenejoboh and Umukoro [126] indicated that blending of biodiesel from some feedstocks such as palm, palm kernel, Jatro-

pha and rubber oils with diesel has resulted in an increase in the

calorific value, decrease in density, cloud point, pour point, kine-matic viscosity and flash point of biodiesel. The same work wasdone by Krishna [127] to improve the cold flow properties of biodiesel.

Sivaramakrishnan and Ravikumar [128] developed an equationto predict the higher heating value of biodiesel based on its kine-matic viscosity, flash point and density with 0.949 accuracy.

A review on the physical and chemical properties and the fattyacid composition of 26 biodiesel feedstocks (including of 22 edible

and non-edible oils and four animal fats) was conducted by

Fig. 2. Kinematic viscosity at 40 C (mm2/s).

Fig. 3. Density at 40 C.

Fig. 4. Calorific value.



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Giakoumis [129]. The author derived an excellent correlationbetween iodine number and the degree of unsaturation. Besides, asmall statistical correlation (R2 > 0.60) was also established forcetane number, density, pour point, carbon content, number of car-bon atoms, stoichiometric air–fuel ratio and T90 distillatetemperature.

Kalayasiri et al. [130] developed 2 empirical equations to pre-dict the saponification number and iodine value of biodiesel basedon its fatty acid composition.

SN ¼

X 560 AiMW i

ð

1Þ

IV ¼X 254 D Ai

MW i

ð2Þ

where SN the saponification number, Ai the percentage of eachcomponent, D the number of double bond, MW i the molecularmass of each component and IV the iodine value.

Krisnangkura [131] illustrated a simple method to estimate thecetane number of biodiesel which is based on their saponificationand iodine numbers. The range of the calculated values covers all

the cetane numbers of vegetable oil methyl esters determinedexperimentally. When it was appliedto individual fattyacidmethyl

Fig. 5. Flash point.

Fig. 6. Cloud point.

Fig. 7. Pour point.


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Fig. 8. Cold filter plugging point (CFPP).

Fig. 9. Oxidation stability (h at 110 C).

Table 7

Mathematical equation for predicting properties for various biodiesel blends.

Biodiesel blends Property Mathematical equation R2 Variable Ref.

Biodiesel-diesel blending

APME+ Diesel Kinematic viscosity at 40 C y = 6E05 x2 + 0.0169 x + 3 .3722 0.9947 x is the dependent variable; x biodiesel% [113]Density at 40 C y = 2E07 x2 + 0.0005 x + 0.8298 1Flash point y = 0.0137 x2 0.6219 x + 89.225 0.9683Calorific value y = 0.2778 x2 41.011 x + 4 5,223 0.9898CFPP y = 0.0004 x2 + 0.0566 x 5.3142 0.9161Cloud point y = 9E05 x2 + 0.1131 x 4.3545 0.978Pour point y = 0.0008 x2 + 0.1681 x 4.4431 0.9893

CIME+ Diesel Kinematic viscosity at 40 C y = 7E05 x2 + 0.0141 x + 3.191 0.9989Density at 40 C y = 2E07 x2 + 0.0004 x + 0.8348 0.9998Flash point y = 0.0048 x2 + 0.0445 x + 69.912 0.9948Calorific value y = 0.0869 x2 69.155 x + 45,336 0.9989CFPP y = 0.0017 x2 0.167 x + 7.3147 0.5621Cloud point y = 0.0007 x2 0.0629 x + 8.3846 0.8207Pour point y = 0.0003 x2 + 0.1194 x 0.1888 0.9606

COME+ Diesel Kinematic viscosity at 40 C y = 2E05 x2 + 0.0045 x + 3.3625 0.9075Density at 40 C y = 9E08 x2 + 0.0003 x + 0.8351 0.9994Flash point y = 0.008 x2 0.1823 x + 73.239 0.9655Calorific value y = 0.008 x2 74.066 x + 4 5,292 0.9994CFPP y = 0.0017 x2 + 0.0494 x + 6 .1818 0.9536Cloud point y = 0.001 x2 + 0.0153 x + 7.5524 0.9083Pour point y = 0.0031 x2 0.3092 x 2.007 0.4009

CMME+ Diesel Kinematic viscosity at 40 C y = 4E05 x2 + 0.0044 x + 3.3503 0.919Density at 40 C y = 1E08 x2 + 0.0004 x + 0.8271 0.9997Flash point y = 0.0118 x2 0.2759 x + 79.312 0.9293Calorific value y = 0.0362 x2 61.61 x + 45,377 0.9968CFPP y = 0.0018 x2 + 0.0532 x + 6 .1469 0.8972Cloud point y = 0.0009 x2 0.0374 x + 7 .0699 0.9609Pour point y = 0.002 x2 + 0.1696 x 1.3706 0.7637

JME + Diesel Kinematic viscosity at 40 C y = 5E05 x2 + 0.0059 x + 3.4774 0.8463Density at 40 C y = 2E07 x2 + 0.0004 x + 0.8274 0.9997



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esters from C8 to C24, a straight line parallel to that of Klopfensteinwas obtained. The developed equation was as follows:

CN ¼ 46:3 þ 5458

SN

ð0:225 IV Þ

ð3Þ

where CN the cetane number, SN the saponification number,and IV the iodine value.

Ramírez-Verduzco et al. [132] attempted to develop 4 empiricalcorrelations that can be used estimate the cetane number, kine-matic viscosity, density and higher heating value of biodieselsbased on their molecular weight and degree of unsaturation. The

estimated values were found to be in a good agreement with theexperimental values and an average absolute deviation (AAD) of

5.95%, 2.57%, 0.11% and 0.21% for the cetane number, kinematicviscosity, density, and higher heating value were found. Thosederived equations were as follows:

;i ¼ 7:8 þ 0:302 M i 20 N ð4Þ

lnðniÞ ¼ 12:503 þ 2:496 lnðM iÞ 0:178 N ð5Þ

P i ¼ 0:8463 þ4:9

M i 0:0118 N ð6Þ

di ¼ 46:

19 1794

M i 0

:

21 N ð7Þ

Table 7 (continued)

Biodiesel blends Property Mathematical equation R2 Variable Ref.

Flash point y = 0.0085 x2 + 0.081 x + 74.015 0.9808Calorific value y = 0.176 x2 68.831 x + 45,205 0.9869CFPP y = 0.0007 x2 + 0.0719 x + 4.6853 0.9709Cloud point y = 0.0008 x2 0.09 x + 6.7238 0.2857Pour point y = 0.0004 x2 + 0.013 x 1.0594 0.8353

MOME+ Diesel Kinematic viscosity at 40 C y = 3E05 x2 + 0.0192 x + 3 .2815 0.9919

Density at 40 C y = 1E07 x2 + 0.0003 x + 0.8272 0.9994Flash point y = 0.0075 x2 + 0.0604 x + 74.8 0.9464Calorific value y = 0.0444 x2 56.284 x + 45,223 0.9901CFPP y = 0.0006 x2 0.1293 x + 4.3843 0.9361Cloud point y = 8E05 x2 + 0.1146 x + 6.6224 0.9606Pour point y = 0.0013 x2 + 0.316 x + 0.042 0.9869

NME + Diesel Kinematic viscosity at 40 C y = 0.0002 x2 + 0.0423 x + 2 .9568 0.9559Density at 40 C y = 6E07 x2 + 0.0005 x + 0.8374 0.9993Flash point N/DCalorific value y = 0.5887 x2 118.16 x + 46,138 0.994CFPP N/DCloud point N/DPour point N/D

POME + Diesel Kinematic viscosity at 40 C y = 7E05 x2 + 0.0042 x + 3.3741 0.8893Density at 40 C y = 1E07 x2 + 0.0002 x + 0 .8351 0.998Flash point y = 0.0098 x2 0.2335 x + 77.701 0.9305Calorific value y = 0.1495 x2 62.708 x + 45,106 0.9696

CFPP y = 0.0022 x2

0.1529 x + 6.007 0.8763Cloud point y = 0.0023 x2 0.1882 x + 8.7622 0.7907Pour point y = 0.0006 x2 + 0.0578 x 1.3692 0.9076

RBME + Diesel Kinematic viscosity at 40 C y = 4E05 x2 + 0.0237 x + 3 .0904 0.9599Density at 40 C y = 7E08 x2 + 0.0004 x + 0.8319 0.9999Flash point y = 0.0165 x2 0.6966 x + 80.524 0.9521Calorific value y = 0.1462 x2 63.082 x + 45,358 0.9849CFPP y = 0.0007 x2 0.0947 x + 4.4311 0.932Cloud point N/DPour point N/D

SME + Diesel Kinematic viscosity at 40C y = 2E05 x2 + 0.0102 x + 3.1682 0.9983Density at 40 C y = 3E08 x2 + 0.0003 x + 0.8319 0.9999Flash point y = 0.0168 x2 0.7353 x + 81.618 0.9438Calorific value y = 0.0635 x2 59.489 x + 45,381 0.9989CFPP y = 0.0007 x2 0.0049 x + 4 .2296 0.9854Cloud point y = 0.0008 x2 + 0.0137 x + 5 .2554 0.7033Pour point y = 0.0018 x2 + 0.145 x 0.2907 0.6526

CIME + Diesel Kinematic viscosity at 40 C y = 0.1664 x + 2.8361 0.9978 [101]Density at 40 C y = 3.9209 x + 825.46 0.9998Flash point y = 0.6678 x2 1.0049 x + 7 1.355 0.9965Calorific value y = 0.5934 x + 45.848 0.9994

Biodiesel-biodiesel blending

SFME-POME Kinematic viscosity at 40 C y = 0.5159 x2 1.1195 +6.3599 0.9908 x POME% [2]SFME-COME y = 0.9533 x2 4.1182 x + 6 .3457 0.9981 x COME%POME-CME Cloud point y = 3.4286 x2 20.629 x + 13.429 0.9704 x CME%

JCME-CME y = 1.1429 x2 12.857 x + 1 0.457 0.979CIME-CME y = 3.4286 x2 12.171 x + 1 2.171 0.9867POME-CME Pour point y = 2.2857 x2 20.114 x + 1 4.114 0.9784

JCME-CME y = 13.714 x2 6.2857 x + 1 0.286 0.9785CIME-CME y = 13.714 x2 8.6857 x + 1 3.286 0.9972POME-CME Cold filter plugging point y = 6.8571 x2 15.543 x + 1 1.943 0.9843

JCME-CME y = 6.8571 x2 14.743 x + 1 0.543 0.9639CIME-CME y = 5.7143 x2 16.286 x + 1 1.486 0.9918

N/DNot determined.


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where ;i the cetane number of the ith FAME, M i the molecularweight of the ith FAME, N the number of double bonds in a givenFAME, ni the kinematic viscosity at 40 C of the ith FAME in mm

2/s, P i the density at 20 C of the ith FAME in g/cm

3 and di thehigher heating value of the ith FAME in MJ/kg.

Talebi et al. [133] developed a new software package (the Bio-dieselAnalyzer) that can predict 16 different properties of biodie-sel based on the fatty acid methyl ester profile of the oil feedstockused in making it.

The polynomial curve fitting method has been used in several

studies [2,101,113,134,135] to predict the properties of biodie-sel–diesel blends. Mathematically, a polynomial of order k in X isexpressed in the form of:

Y ¼ C o þ C 1 X þ C 2 X 2 þ þC k X

k

where X is the variable as a function of available data and Y is thepredicted value. Table 7 shows some examples of the generatedequations for various biodiesel blends. Table 8 shows some mathe-matical equations for predicting properties of various biodieselfeedstock.

6. Conclusion

In recent time, the research on biodiesel is reaching to the peakbecause it is found as a good complementary substitute to dieselthan other sources. A number of research have been conductedon biodiesel from different feedstock’s by various researchers andsome are still ongoing for a considerable level of usage. Accord-ingly, this study highlighted the physicochemical properties undervarious biodiesel–diesel blend. For clear understanding, a shortdescription on feedstock has been also carried out. A polynomialcurve fitting method is used to generate mathematical equationfor different biodiesel–diesel blend in order to predict the proper-ties of any percentage of biodiesel in the blend. This would help theresearchers to optimize the blend percentage which is necessary tomeet the impending scarcity of petro-diesel. The other profitableadvantage would be the proper selection of combined feedstock

to improve the performance of engine relative to diesel withoutany or little modification. This is necessary as there is the challenge

of using single feedstock as biodiesel for better performance alongwith some demerits of edible feedstock.

7. Recommendation

Based on the review work that is conducted in this paper, forfuture work it canbe recommended to investigate the optimizationof biodiesel blends (both biodiesel–diesel and biodiesel–biodiesel)as different biodiesel feedstocks possess some superior qualities aswell as some inferior qualities. Moreover, in depth instrumental

analysis for instance, effect of temperature, reaction time and cat-alyst type on biodiesel yield can help researchers to select morepotential candidate for biodiesel to be used commercially.

Acknowledgements

The authors would like to thank the Ministry of Higher Educa-tion and University of Malaya, Malaysia for the financial assistancethrough High Impact Research Grant titled: Development of alter-native and renewable energy carrier (DAREC) with Grant NumberUM.C/HIR/MOHE/ENG/60.

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Table 8

Mathematical equation for predicting properties for various biodiesel feedstocks.

Biodiesel blends Property Mathematical equation R2 Ref.

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Various biodieselFeedstocks

Higher heating value (HHV) vs. Kinematic viscosity (KV) HHV = 0.4625 (KV) + 39.450 0.9677 [134]Kinematic viscosity (KV) vs. Density (DN) KV= –16.155 (DN) + 930.78 0.9902Kinematic viscosity (KV) vs. Flash point (FP) KV= 22.981 (FP) + 346.79 0.9819Higher heating value (HHV) vs. Density (DN) HHV = –0.0259 (DN) + 63.776 0.7982Higher heating value (HHV) vs. Flash point (FP) HHV = 0.021 (FP) + 32.12 0.9530Density (DN) vs Kinematic viscosity (KV) DN = 15.77 (KV) + 929.59 0.9724 [135]Flash point (FP) vs. Kinematic viscosity (KV) FP= 12.36 (KV) + 176.3 0.964Density (DN) vs. Flash point (FP) FP = 1.46 (DN) 1099.9 0.91Density (DN) vs. Calorific value (CV) CV = 0.0207 (DN) + 23.28 0.9568

Higher heating value (HHV) vs. Kinematic viscosity (KV),

Density (DN), Flash point (FP)

HHV= 0.4527 (KV) 0.0008 (DN) 0.0003 (FP) + 40.3667 0.949 [128]

N/DNot determined.


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Influence of biodiesel blending on physicochemical properties and importance of of biodiesel blend.pdf