Influence of biodiesel blending on physicochemical

Energy Conversion and Management 94 (2015) 51–67
Contents lists available at ScienceDirect
Energy Conversion and Management
journal homepage: www.elsevier.com/locate/enconman
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
b
Center for Energy Sciences, Department of Mechanical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
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 2014
Accepted 16 January 2015
Available online 6 February 2015
Keywords:
Biodiesel
Edible oil
Non-edible oil
Blending
Physicochemical properties
Mathematical 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 create
a deep concern in field of research. Biofuels are placed at the peak of the research arena for their underlying benefits as mentioned by multiple researches. Out of a number of vegetable oils, only a few are used
commercially for biodiesel production. Due to various limitations of edible oil, non-edible oils are becoming a profitable choice. Till today, very little percentage of biodiesel is used successfully in engine. The
research is still continuing for improving the biodiesel usage level. Recently, it is found that the blended
biodiesel from more than one feedstock provides better performance in engine. This paper reviews the
physicochemical properties of different biodiesel blends obtained from various feedstocks with a view
to properly understand the fuel quality. Moreover, a short description of each feedstock is given along
with 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 help
of 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 diesel
except 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 biodiesel
blend for any percentage of biodiesel and to select the proper feedstock for better performance.
Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction
The primary catalyst of any country’s socio-economic development is energy. However, through modernization the demand
of energy consumption is facing a serious threat due to the gradual declination of fossil fuels. Various sectors for instance, industry, transport, agriculture, domestic sector, etc. require energy
from sources like wood, coal, petroleum products, nuclear power,
solar, and wind [1]. Currently, more than 80% of energy demand
is catered by fossil fuels [2]. The deep concern about fossil fuels
is that it’s generation of toxic pollutants links to global warming,
climate change and even some impasse diseases [3]. To compete
with this critical situation, a good number of research have been
⇑ Corresponding author. Tel.: +60 163269524.
E-mail addresses: [email protected] (M.A. Wakil), [email protected] (M.A.
Kalam).
http://dx.doi.org/10.1016/j.enconman.2015.01.043
0196-8904/Ó 2015 Elsevier Ltd. All rights reserved.
conducted to find alternative to fossil fuels for eco-friendly
condition.
Biodiesel is considered to be a notable option for at least complementing conventional fuels [3]. Its production from renewable
sources such as vegetable oils and fats has been widely reviewed
[4–10]. It is advantageous over petroleum product because it is
safe in handling, biodegradable, non-toxic, has higher combustion
efficiency, higher cetane number, contains no sulfur, etc. [1,3,11–14].
In addition, it is advantageous for numerous social benefits like
rural revitalization, creation of new jobs and reduced global
warming [15].
Among the available sources of biodiesel, edible oils are dominating in several countries as diesel substitute. For instance, canola
and soybean are used in USA, palm oil in Malaysia, rapeseed oil in
Europe etc. [12,14]. Currently, more than 95% of the world biodiesel comes from edible oil. In the year 2004–2007 the edible oil used
for biodiesel production was 6.6 million tons which would attribute 34% of the increase in global consumption of biodiesel and
52
M.A. Wakil et al. / Energy Conversion and Management 94 (2015) 51–67
Nomenclature
APME
CIME
COME
CME
CMME
JCME
MOME
NME
POME
RBME
Aphanamixis polystachya methyl ester
Calophyllum inophyllum methyl ester
Coconut methyl ester
Canola methyl ester
Croton megalocarpus methyl ester
Jatropha curcas methyl ester
Moringa oleifera methyl ester
Neem methyl ester
Palm methyl ester
Rice bran methyl ester
also lead to one third of the total projected growth of edible oils
between 2005 and 2017 [16]. This large usage of edible oils for biodiesel has caused a serious impact on food supply. It has the ability
to lead to starvation especially in developing countries and impose
antagonistic effect on environment [13]. The prominent solution is
to use second generation feedstocks (non-edible oils) which has
higher potential for biodiesel production [13] and can easily eliminate the food vs fuel concern. Another boosting feedstock is algae.
Although full scale commercialization from algae has not begun
yet, but it is expected to be rich in oil content (oil content in microalgae can exceed 80% of its weight of dry biomass) [3].
The use of vegetable oils started more than a century ago. Apart
from the remarkable advantages, biodiesel has couple of difficulties to be used as a replacement of fossil fuels in engine such as
high 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]. Another
serious threat for biodiesel industry is the cost of feedstock which
currently accounts for over 70–85% of biodiesel production cost
[13,17,18]. One solution to alleviate this problem is to use multiple
feedstocks of varying percentage. It will not only subside the cost
of production but also enhance product quality. Problems of using
edible oils can also be moderated by switching these with non-edible oils. It has been proven that biodiesel containing up to B5 will
have no notable difference in terms of power and fuel economy
when it is compared to diesel [19]. ASTM D7467 suggests blending
of 20% biodiesel with diesel. In 2014, the Chevy Cruze Clean Turbo
Diesel is directing the engine with rated B20 biodiesel compatibility [20]. Now-a-days research is going on to increase the use of biodiesel blending with diesel. Consequently, biodiesel blending
(biodiesel and diesel) bring a new topic in research arena. A number of researches have been undertaken already on biodiesel
blending [17,21–27]. Accordingly, it has become easier to have a
clear concept of the physicochemical properties of edible and
non-edible vegetable oils with varying blending percentages for a
better understanding on blend qualities. Survey of existing
literature shows that most of the studies focus on pure biodiesel
SME
SFME
CB10
CoB
CrB
JB
CP
PP
CFPP
Sesame methyl ester
Sterculia foetida methyl ester
Calophyllum biodiesel (10% + Diesel 90%) blend
Coconut biodiesel, diesel blend
Croton biodiesel, diesel blend
Jatropha biodiesel, diesel blend
Cloud point
Pour point
Cold filter plugging point
properties rather than properties of blending. Therefore, this
review aims firstly at focusing on the physicochemical properties
of edible and non-edible biodiesel and their blends with diesel
(B0–B100). Secondly, mathematical equation for various biodiesel
blends would be produced in order to predict the important properties of blended biodiesel for any percentage of biodiesel. Here, a
polynomial curve fitting method is used to generate the equation.
It is believed that such kind of studies will assist researchers for
further study about optimal usage of biodiesel.
2. Biodiesel feedstocks
Feedstock-related cost has been regarded as a primary obstacle
as it constitutes roughly around 60–90% of the total biodiesel production 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 residues) [38], algae, halophytes (Salicomia bigelovii [39]), sewage
sludge [40], etc.
Globally, more than 350 oil-bearing crops have been identified
as potential biodiesel sources [12,13,29,41]. The regional climate
mainly 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 plants
including their country of origin, oil content and their necessary
uses are portrayed in Table 2 with their fatty acid composition in
Table 3. The identification of plants and seeds of the selected oil
sources are shown in Fig. 1.
3. Characteristics of crude oils and biodiesels
Characterization of oil properties is necessary to research about
the processing of crude oil to biodiesel and afterwards to diesel
engine successfully. The physical and chemical properties of any
fuel are significant factors which help to decide whether the oil
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, grape
seed, hemp, kapok seed, lallemantia, lemon seed, macauba fruit (Acrocomia sclerocarpa), meadowfoam seed, mustard, okra seed (hibiscus seed), perilla
seed, 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
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
Non-edible
oil
Waste or recycled oil
Animal fats
Tallow, Yellow grease, chicken fat and by-products from fish oil, etc.
Table 2
Acknowledgement of some edible and non-edible oils.
Name of oil
Characteristics
Country of available
Oil
Yield of
content plant seed
Uses
Refs.
kg/ Kg/ha
tree
A widespread species found in Indo-China and 30–
Species in the family Meliaceae also known as pitraj tree. It is
indigenous as evergreen tree mainly growing in the tropical area of western Malaysia. Indonesia, India, Bangladesh, 40%
Asia. This deciduous, perennial tree grows to 20–30 m long. Flower etc.
clusters occur in leaf axils, less than a foot long. Seeds are greyish
brown. Flowering: May–September
25–
40
[43–47]
Herbal medicine, potential for
biodiesel, The wood is used for
construction of ships, vehicles, posts
and agricultural tools
Calophyllum inophyllum L.
Known as penaga laut, non-edible oil seed tree belongs to Clusiaceae Native Australian tree, Found in tropical region 45–
family. It enables to tolerate harsh environmental conditions (acidity, of India, Malaysia, Indonesia, Philippines, etc. 70%
salinity, and drought), require little maintenance, 1000–4000 mm
rainfall is sufficient per year. It is non-invasive, fruits profusely
(3000–10,000 seeds tree1 season1). Duration of harvest is two
months, Flowering in March–May and fruiting in October–November
25– 4680
50
For burning, timber, medicinal uses, [13,47–50]
etc.
Croton megalocarpus
40–
C. megalocarpus belongs to Euphorbiaceae family. It is a multipurpose The plant id indigenous to East Africa and
tree of Mexican and Central American origin with height 15–40 m. It widely found in mountain of Tanzania, Kenya, 45%
Uganda, etc.
is capable to engender at the altitude of 1200 m and 2450 m
respectively. It requires less water footprint and fertilization during
cultivation stage
50
Coconut
Coconut is member of the family Arecaceae. Found throughout the Indigenous to Indo-pacific origin. Philippines,
tropic and sub-tropic area. It is familiar as large palm growing up to Indonesia, India, Thailand, Sri Lanka, Mexico,
Brazil etc.
30 m tall with pinnate leaves 4–6 m long. On very fertile land a
coconut tree can yield 75 fruits per year but often yield less than 30
mainly due to tough cultural practices. Coconut palms are growing in
more than 80 countries of the world with a total production of 61
million tons per year. Coconut trees are very hard to establish in dry
climates and cannot grow without frequent irrigation, in drought
conditions
Jatropha curcas
This plant is native to Mexico, Central America, 43–
J. curcas belongs to the family Euphorbiaceae. It grows throughout
Africa, India, Brazil, Bolivia, Peru, Argentina, and 59%
most of the tropics and can survive on poor soil and drought
condition. It is reported as wild, semi-arid-climates plant. It flowers in Paraguay
March–September and fruiting in April–May and October–November
of ellipsoidal green fruits. It produces seeds after 12 months and
reaches its maximum yield after 4–5 years.
0.5– 100–
2.0 8000
Moringa oleifera
Native to sub-Himalayan tracts of north-west
Moringa oleifera is the most widely cultivated tree species in the
India, Africa, Latin America, Pakistan,
family of Moringaceae grows throughout most of the tropics. It is
drought tolerant and can survive in arid, harsh and infertile land. The Bangladesh, Afghanistan, etc.
tree can range from 5 to 10 m in height; sometimes can be even 15 m.
The plant starts bearing pods 6–8 months after planting
35–
45%
3000
Neem
Neem (Azadirachta indica), a tree of mahogany family Meliaceae. It is Native to Asian countries like India, Pakistan,
fast growing evergreen tree and can reach a height of 15–20 m, rarely Bangladesh, etc.
to 35–40 m. It is drought tolerant, thrives in area with sub-arid to
sub-humid condition with annual rainfall 400–1200 mm and hardly
below 400 mm. This is a typical tropical to subtropical tree can
tolerate high to high temperature but cannot tolerate temperature
below 4 °C. Duration of harvest is 2–3 months. Flowering starts in
March–April and fruiting in June–July. The white, hard inner shell of
the fruits enclose with one and rarely two or three seeds
30–
39%
Palm
Palm oil tree belonging to the species namely, Elaeis guineensis. Oil Mostly available in South East Asia (Indonesia,
palms are originally from Western Africa, but can flourish wherever Malaysia), Thailand, Brazil, Nigeria, Colombia,
heat and rainfall are abundant. It is found to be a tropical perennial Ecuador, Costa Rica Venezuela, etc.
5000– Rejuvenating chemical peels, pain
10,000 relieving and anti-inflammatory
drag, biodiesel production, etc.
60%
[13,51–53]
Coconut oil is used for Skin
[1,54]
moisturizer, ingredient for soap, etc.
20–
35
Bio-fuels, carbon dioxide
sequestration, etc.
[13,47,53,55–
57]
Moringa leaves uses as sources of
food, as forage for livestock, etc.
[53,58–60]
Toiletries, pest control, cosmetics,
Pharmaceuticals, etc.
[47,61,62]
4000– Cooking ingredient, confectionery,
5000 cosmetics, body products and
cleaning agents
(oil)
M.A. Wakil et al. / Energy Conversion and Management 94 (2015) 51–67
Aphanamixis polystachya
[12,63–66]
53
(continued on next page)
Originated from Africa and Turkey, Also in India, 57–
China, Sudan, Burma, Tunisia, Egypt, Thailand, 63%
Mexico, Guatemala, Afghanistan, Pakistan,
Bangladesh, etc.
Sesame (Sesamum indicum L) is an oil seed herbaceous crop of the
Pedaliaceae family primarily found in tropical and subtropical areas. It
is very drought-tolerant, in part due to its extensive root system and
requires adequate moisture for germination and early growth. It is an
annual plant growing 50–100 cm tall with opposite leaves 4–14 cm
long. The flowers are yellow, tubular with four-lobed mouth. The
flower may vary in color with some being white, blue or purple
Sesame
Rice bran
plant grows well in lowland with humid places. Identifies as
unbranched, single stemmed which can grow up to 20–30 m in
height. Can be cultivated for 40–50 years. It carries fruits from the
fourth year onward. Due to the reduction in fruit production it is right
choice to replanting for every 25 years rotation
However, rice can be grown practically
Rice is the seed of the monocot plants Oryza sativa (Asian rice) or
anywhere, even on a steep hill
Oryza glaberrima (African rice). Rice is the most important cereal
cultivated in the world which fed more than half of the people of the
world. Rice bran is a by-product of rice milling process. Due to the
presence of active lipase and high free fatty acid, about 60–70% of rice
bran oil production is non-edible. Rice cultivation requires ample
water
16–
32%
440
kg/ Kg/ha
tree
Food, nutraceutical, Pharmaceutical
industry
Refs.
Uses
Oil
Yield of
content plant seed
Country of available
Characteristics
Name of oil
Table 2 (continued)
[68,69]
M.A. Wakil et al. / Energy Conversion and Management 94 (2015) 51–67
Popular ingredient in Japan, Also uses [1,18,67]
as medicine, animal food, cosmetics,
shoes cream, etc.
54
is suitable for engine or unsuitable. Researchers have shown that
the properties of biodiesel vary significantly due their diverse fatty
acid composition which provides an obvious effect on engine performance. Therefore, it is important to characterize biodiesels
according to preset standard testing methods [13]. American
Standard Test Method (ASTM) and European (EN) standard have
formulated the specification for biodiesel as shown in Table 4
[1,2,12,13,87,88]. Table 5 shows some selected properties of crude
edible and non-edible oils. It is seen that the heating value of crude
oils vary in the range of 38,500–40,000 kJ/kg. The maximum kinematic viscosity was reported for Calophyllum inophyllum
(55.478 mm2/s) and rice bran (52.225 mm2/s). Among these feedstocks Moringa oleifera contains highest oxidation stability
(41.75 h at 110 °C) while the maximum acid value was in C. inophyllum (41.74 mg KOH/g oil).
On the other hand, except coconut (38,300 kJ/kg) the heating
value of other edible and non-edible methyl esters are almost
above 39,500 kJ/kg as shown in Table 6. Coconut possesses the
lowest kinematic viscosity (3.1435 mm2/s). However, according
to data found from various researches, all feedstocks indicated in
this study have satisfied ASTM limit for viscosity. It is found that
Neem and coconut have lower flash point rather than other feedstock. Cetane number varies from 45 to 75.
4. Impact of blending on physicochemical properties of
biodiesel
Though biodiesel satisfy the ASTM and EN limits, it cannot be
used alone in diesel engine due to its high kinematic viscosity
and density and also lower oxidation stability and heating value.
To improve those properties, it is blended with diesel. In this paper
we carried out some work to review the variation of physicochemical properties such as kinematic viscosity, calorific value, density,
flash point, cloud point, pour point, CFPP, and oxidation stability
with the varying blended percentage, B0 (Pure diesel)–B100 (Pure
biodiesel). The data were gathered from different resources as in
Refs. [18,51,100–115] and depicted in Figs. 2–9, respectively.
As biodiesel is completely miscible to diesel, biodiesel and diesel blend was prepared using a beaker glass on a volume basis and
the mixture was agitated with a shaker for about 15–30 min at
ambient temperature. The effects of blend on the important properties are discussed in the following sections.
4.1. Kinematic viscosity
Kinematic viscosity is the measure of resistance to fluid flow
over another due to internal friction. It is the most critical property
as it affects injection behavior [14]. Viscosity of vegetable oil is
typically ten times higher than petroleum based diesel [116]. High
viscosity leads to a poorer atomization and vaporization, formation
of shoots, etc. [13,14,117]. The data collected from various research
articles based on viscosity of many feedstocks at various blend
percentages are depicted in Fig. 2. It is seen from figure that at
any blend percentage, Calophyllum, Moringa, Neem and Rice bran
biodiesel blends show higher viscosity than other feedstocks
except that at lower blend percentages (below B30), the viscosity
variation are small. For instance, results for B20 show that the
average viscosity of each feedstock, APME = 3.657, CIME = 3.482,
COME = 3.54,
CMME = 3.50,
JCME = 3.74,
MOME = 3.67,
NME = 3.81, POME = 3.54, RBME = 3.50, SME = 3.37 mm2/s respectively. Average value of B100 for each feedstock biodiesel are
APME = 4.46,
CIME = 5.23,
COME = 4.06,
CMME = 4.376,
JCME = 4.57, MOME = 4.87, NME = 5.4, POME = 4.39, RBME = 4.82,
SME = 4.339 mm2/s respectively.
Table 3
Fatty acid composition of crude edible and non-edible oils.
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
C20:1
C22:0
C22:1
C24:0
C24:1
Aphanamixis polystachya
(meliaceae) [13,70]
Calophyllum inophyllum L. [13]
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
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
0.09
14.6, 17.9
2.5
N/D
37.57, 42.7
26.33, 13.7
0.2, 2.1
N/D
0.94
0.72
N/D
N/D
2.6
N/D
Croton megalocarpus [52]
Coconut oil [1,11]
N/D
N/D
N/D
14
N/D
51,48.8
6.5
7.5, 7.8
0.1
0.1
0.1
11.6
5, 4.4
72.7
1, 0.8
3.5, o.4
0
N/D
65.7
N/D
N/D
0.9
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
Jatropha curcas[1,13,16]
N/D
0.1
N/D
0.1
18.5,
19.9
1.4, 0.1
19.96,
18.5
3.8
3
0.2, 0.4
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
41.6, 32.1,
31.4, 32.8
1.0, 8.1, 0.7
N/D
N/D
0.2
N/D
N/D
N/D
0.2-0.26
N/D
N/D
N/D
N/D
N/D
N/D
0.1
N/D
1
0.3, 0.8
0.4–0.6,
0.3
0.23
N/D
N/D
2.3-15.8,
18.3
10.2, 10.1
35.4,35.6
26.4–35.1
N/D
N/D
N/D
14.4-24.1
18.1
4.5, 4.4
2.1, 2.2
1.7–2.5
0.2
Palm [1,11,16]
Rice bran [3,11,13,73]
13.6-16.2,
18.1
42.8, 42.6
12.5, 17.7
11.7–16.5
2.0,
5.8
N/D
N/D
N/D
4, 0.907,
5.8
0.8-3.4
7.1
Neem [3,11]
39.1, 40.8,
44.7
72.2, 66.6,
79.4
49.1-61.9,
44.5
40.5
47.5, 40.6
39.2–43.7
0.2, 0.2
N/D
0.7,
0.9
1.4,
2.1
N/D
0.1
Moringa oleifera [70–72]
12.6,15.6,
15.1,14.2
6.5, 7.8, 9.1
0.2
1.1, 1.8
1.1
N/D
N/D
0.2, 0.4–
0.6,
N/D
N/D
N/D
0.3
N/D
N/D
N/D
N/D
Sesame [1]
Stauntonia chinensis [28]
Raphanus sativus [74]
Annona diversifolia [75]
Syagrus coronate [76]
Syagrus coronate [77]
chufa sedge [78]
Citrus reticulate [79]
Phoenix dactylifera [80]
Idesia polycarpa [81]
Calotropis gigantean [82]
Baobab (Adansonia digitata L.)
[83]
Fodder radish (Raphanus
sativus L. var oleiferus)
[84,85]
Citrullus colocynthis [86]
N/D
N/D
N/D
N/D
9.0
6
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
6.0
6
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
42.0
37
N/D
N/D
24
N/D
N/D
N/D
N/D
N/D
N/D
N/D
16.0
11
0.1
N/D
13
N/D
N/D
N/D
13.1
6.87
6.13
16.40
8.0
8
13.1
26.90
17.44
15.06
15.5
20.96 ± 1.2
N/D
0.21
0.05
N/D
N/D
N/D
2.1
N/D
N/D
6.5
0.3
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
3.9
1.19
1.68
5.22
4.0
3
2.8
4.62
<0.3
1.18
10.5
20.29 ± 0.2
52.8
79.95
23.87
70.42
12.0
24
61.6
26.75
36.8
5.5
31.1
22.14 ± 0.7
30.2
8.32
13.46
7.97
3.0
5
17.2
37.65
7
70.6
36.3
27.47 ± 1.4
N/D
0.13
10.34
N/D
N/D
N/D
1.4
3.80
N/D
1.1
0.8
8.84 ± 2.2
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
1.72
0.68
N/D
N/D
N/D
0.7
0.26
N/D
N/D
0.6
0.29 ± 0.9
N/D
0.51
8.58
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
1.64
N/D
N/D
N/D
0.2
N/D
N/D
N/D
0.1
N/D
N/D
N/D
31.76
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
0.2,
0.4–
0.9
N/D
N/D
0.61
N/D
N/D
N/D
0.8
N/D
N/D
N/D
0.4
N/D
N/D
N/D
N/D
N/D
7
N/D
N/D
3.6
27.9
7.6
4.6
N/D
2.2
11.2
N/D
33.3
0.6
2.0
N/D
N/D
N/D
0.7
10.53
0.05
0.14
9.57
14.07
64.65
0.1
N/D
0.12
0.06
0.01
N/D
N/D
N/D
N/D
5.5, 9.7,
7.1
6.0, 5.5,2.7
N/D
N/D
N/D
1.26
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
N/D
M.A. Wakil et al. / Energy Conversion and Management 94 (2015) 51–67
Oil
N/D Not detected.
55
56
M.A. Wakil et al. / Energy Conversion and Management 94 (2015) 51–67
Fig. 1. Some pictures of edible and non-edible plants and seed.
4.2. Density
The air–fuel ratio and energy content of the air fuel mixture
largely depend on fuel density within the combustion chamber
of diesel engine [14]. In general, density of biodiesel is slightly
higher than petro diesel and it is augmented by increasing biodiesel percentage in blends [12,14]. Fig. 3 shows density variations
with blend percentage variations. It is found that except Neem biodiesel (having higher density 0.891 g/cc at 40 °C) the density of
other feedstocks biodiesel are lower than 0.87 g/cc. Moreover, Rice
bran and Sesame biodiesels have the same trend of increasing density (0.849, 0.853, 0.857, and 0.86 at 50%, 60%, 70% and 80% blend
percentage). Except Aphanamixis, Calophyllum and Neem, density
of other biodiesel varies slightly with the rise of biodiesel percentages 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 data
presented 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 value
of other biodiesels are nearly 40,000 kJ/kg. The heating value of
blended biodiesel is higher than biodiesel and slightly lower than
diesel. The heating value decreases marginally with the increasing
percentages of biodiesel in blend. With the rise of blend percentage
(for example, B20–B30–B40, etc.), calorific value decrease to about
250–400 kJ/kg except coconut biodiesel blend which decrease
quite higher (about 700 kJ/kg). Up to B60, Palm, Rice bran and Sesame biodiesels have shown considerable heating value above
42,000 kJ/kg. This value is 7% lower than petro diesel where pure
biodiesel has normally 12% lower calorific value than diesel.
4.4. Flash point
Flash point is a measure of flammability of fuels which is inversely proportional to volatility [12–14]. The biofuels specification
for flash point is meant to protect against contamination for highly
volatile matters. In general, biodiesel has higher flash point than
petro-diesel. The average flash point of pure biodiesel is almost
double than that of diesel. There is an increasing trend of flash
57
M.A. Wakil et al. / Energy Conversion and Management 94 (2015) 51–67
Manchurian apricot (Prunus mandshurica Skv.)
Xylocarpus moluccensis
Siberian apricot (Prunus sibirica L.)
Baobab (Adansonia digitata L.)
Fig. 1 (continued)
Table 4
U.S. and European specification for biodiesel.
Property
Kinematic viscosity at 40 °C (mm2/s)
Density at 15 °C (kg/m3)
Calorific value (MJ/kg)
Flash point °C
Pour point (°C)
Cloud point (°C)
Cold filter plugging point (CFPP) (°C)
Cetane number
Oxidation stability at 110 °C (h)
Acid value (mg KOH/g)
Free glycerin (wt% max)
Total glycerin (wt% max)
Carbon residue (wt% max)
Copper strip corrosion (3 h at 50 °C)
Iodine value (g/l2/100 g) max.
Water and sediments (vol%, max)
Total sulfur (ppm), max
Phosphorous (ppm), max
U.S. (ASTM D6751-08)
Europe (EN 14214)
Test methods
Limit
Test methods
Limit
D 445
D 1298
–
D 93
D 97
D 2500
ASTM
D 613
D 675
D 664
D 6584
D 6584
D 4530
D130
–
D 2709
D 5453
D 4951
1.9-6.0
880
–
93
15 to 16
3 to 12
Max + 5
47 min
3 min
0.5 max
0.02
0.24
0.05
No. 3 (max.)
–
0.05
15b
10
EN ISO 3104
EN ISO 3675/12185
EN14214
EN ISO 3679
–
–
EN 14214
EN ISO 5165
EN 14112
EN 14104
EN 14105
EN 14105
EN 10370
EN 2160
EN 14111
EN 12937g
EN 20846
EN 14107
3.5-5.0
860-900
35
101 min.
–
–
–
51 min
6 min
0.5 max
0.02
0.25
0.30e
No. 1
120
0.05
10
4
58
M.A. Wakil et al. / Energy Conversion and Management 94 (2015) 51–67
Table 5
Properties of crude edible and non-edible oils.
Properties
Aphanamixis
polystachya [89]
Calophyllum
[2]
Coconut [2]
Croton
[2]
Jatropha [2]
Moringa
[2]
Palm [2]
Rice
bran
[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
2
Kinematic viscosity
(mm2/s) at 40 °C
Kinematic viscosity
(mm2/s) at 100 °C
Viscosity Index (VI)
Density (kg/m3) at
40 °C
Flash point (°C)
CFPP (°C)
Cloud point (°C)
Pour point (°C)
Refractory Index
Oxidation stability (h
at 110 °C)
Acid value (mg KOH/
g oil)
Transmission (%T)
Copper strip
corrosion 3 h at 50 °C
Absorbance (Abs)
MIU (wt%) [95]
FFA (wt%) [95]
35.093
55.478
27.64
29.844
48.095
43.468
41.932
52.225
34.087
32,000–
40,000[92]
35.83
7.2547
9.5608
5.9404
7.2891
9.1039
9.0256
8.496
10.393
7.6364
–
177.9
0.9164
165.4
0.9249
168.5
0.9089
224.2
0.9100
174.1
0.9054
195.2
0.8971
185.0
0.8998
192.8
0.9069
202.9
0.9066
–
0.9200
–
–
5
4
1.4789
0.09
236.5
26
8
8
1.4784
0.23
264.5
22
17
19
1.4545
6.93
235.0
10
–
–
1.4741
0.14
258.5
21
9 ± 1[1]
4 ± 1[1]
1.4652
0.32
263.5
18
10
11
1.4661
41.75
254.5
23
23[93]
12[93]
1.4642
0.08
300.50
0
0
1.4718
4.40
280.0
44
3
4
1.4709
9.795
100
11[92]
19
10
–
12.4 [92]
26.7
41.74
11.6[3]
14.47[86]
32.64 [3]
91.2
1a
18.5[95]
7.40[93]
63.2
1a
13.56
34.7
1a
8.62
2.90[3]
69.2
1a
1.314
61.6
–
12.07
3.343[94]
87.5
1a
87.10
1a
78.4
–
–
–
0.209
–
–
0.46
–
–
0.16
0.30
0.21
0.199
0.03
0.54
0.06
2.74
0.05
0.106
–
–
2.16
2.14
–
–
–
–
0.04
2.74
0.07(Lauric
acid)
2.7
2.0
31.4
7.3
1.0
7.3
4.0
0.9
0[b][68]
–
1990
47.6
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Sulfur (ppm) [95]
Phosphorous (ppm)
[95]
0.058
–
1.68[93]
–
–
61.8
1a
0.209
0.16
1.17(Palmitic
acid)
3.5
322.9
point for biodiesel blends as portrayed in Fig. 5. Calophyllum and
coconut 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 the
range of 3–8 °C with the increase of blend B20. This trend is found
up to B60, but the variation is increased about 15–30 °C when biodiesel percentage increase above 60% in blend.
1 °C). Jatropha and croton show a moderate variation of 0–3 °C
and 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 trends
of CFPP (5 to 4 °C), (5 to 6 °C) while Aphanamixis, Calophyllum,
Jatropha and Palm biodiesel have increasing trend with the
increase of biodiesel blends. The minimum CFPP was found at
90% biodiesel blend for Croton which is 6 °C and for Coconut at
90% and 100% blend (4 °C). Moreover, pure Moringa and Sesame
biodiesel show 2 °C and 3 °C respectively.
4.5. Cloud point (CP), pour point (PP), and cold filter plugging point
(CFPP)
4.6. Oxidation stability
These properties are considered to be cold flow properties as
they establish the limit for the use of fuels under cold weather conditions [2,13,14,119,120]. The cloud point is the lowest temperature at which smallest observable cluster of wax crystal first
appears [120]. Pour point is the lowest temperature at which the
wax becomes semisolid and loses its flow characteristics. Cold filter plugging point is an estimation of lowest temperature at which
fuel 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 acid
composition [12,13]. The freezing point of biodiesel increases with
the increase of carbon atoms in carbon chain and decrease with
double bonds [29,121]. It is found from Fig. 6 that Moringa and
Palm have rising trend of cloud point while Croton gives the
reverse trend. Maximum cloud point is noted on Moringa (19 °C)
and it varies from 8 to 19 °C for the blends. The minimum cloud
point is observed on croton (4 °C).
The minimum pour point was observed for Coconut at 20% biodiesel blend (15 °C) and it increases with blend percentages as
shown in Fig. 7. While the highest pour point was found for Moringa 19 °C, sesame biodiesel has a little variation in pour point (0–
Oxidation stability is a prominent parameter that assesses the
fuels quality. Oxidation stability of biodiesel is generally influenced
by some factors such as presence of air, heat, traces of metal, peroxides, light and fatty acid composition [12]. The presence of double bonds in biodiesel results in a high level of reactivity with
oxygen, 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 the
oxidation stability is waning. Moringa biodiesel has the best stability (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 reverse
results were found for Calophyllum biodiesel (0.09 h at 110 °C). On
the other hand, Coconut biodiesel also has a good oxidation stability (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
59
M.A. Wakil et al. / Energy Conversion and Management 94 (2015) 51–67
important factor in the design of fuel spray, atomization and
combustion and emission system for diesel engines. It is also a
highly demanding parameter because research is going on with
various feedstocks for biodiesel production. These equations would
help to predict the property at any percentages of biodiesel in
biodiesel–diesel blend. Recently, several studies have been
conducted to examine the physical and chemical properties of
biodiesel–diesel blends. The following paragraph will summarize
the most important works done in this aspect.
Saxena et al. [14] reviewed various methods for the prediction
of 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 pressure for various biodiesel feedstocks.
Sivaramakrishnan and Ravikumar [125] developed an equation
to calculate cetane number of various vegetable oils and their biodiesel from their viscosity, density, flash point and higher calorific
value. They concluded that this equation gives an accuracy of 90%.
Atabani et al. [2] discussed the concept of biodiesel–biodiesel
blending to improve the properties of some feedstocks. For
instance, blending of Sterculia feotida methyl ester (SFME) and
coconut 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 conducted
on the effect of biodiesel–biodiesel blending on cloud point, pour
point and cold filter plugging point. The properties at different biodiesel–biodiesel blends percentages were estimated using the
polynomial curve fitting method. This paper concludes that blending of edible and non-edible biodiesel feedstocks could be considered as an approach to improve the properties of the final product.
Moser [17] indicated that the fuel properties of Soybean methyl
ester were improved by blending with Canola, Palm and Sunflower
methyl esters to satisfy the IV (<120) and OSI (>6 h) specifications
contained within EN 14214. The CFPP of Palm methyl ester was
improved by up to 15 °C through blending with Canola methyl
ester. Statistically significant relationships were elucidated
Table 6
Properties of edible and non-edible methyl esters.
1
2
3
4
5
6
7
8
Properties
Aphanamixis
polystachya [89]
Calophyllum
[2]
Coconut
[2]
Croton [2]
Jatropha
[2]
Moringa
[2]
Palm
[2]
Rice
bran
[90]
Sesame
[90]
Neem [91]
Heating value (kJ/kg)
Kinematic viscosity
(mm2/s) at 40 °C
Kinematic viscosity
(mm2/s) at 100 °C
Viscosity Index (VI)
Density (kg/m3) at
40 °C
Flash point (°C)
CFPP (°C)
Cloud point (°C)
39,960
4.7177
39,513
5.5377
38,300
3.1435
39,786
4.0707
39,738
4.9476
40,115
5.0735
40,009
4.6889
39,957
5.3657
39,996
4.3989
39,810
3.70
1.8239
1.998
1.3116
1.6781
1.8557
1.9108
1.7921
1.9609
1.7236
–
220.7
0.8735
183.2
0.8776
230.8
0.8605
276.3
0.8704
194.6
0.8742
206.7
0.8597
203.6
0.8591
187
0.8681
229.0
0.8848
–
0.8680
188.5
5
8
162.5
11
12
118.5
1
1
164.0
4
3
186.5
10
10
176.0
18
21
214.5
12
13
174.50
0
208.5
1
1, 6[68]
19
67.07[71]
15
52[1]
3
73.6[13]
1, 14[68]
50.48[68]
76, 120[92]
11[96]
9[92],
14.4[96]
2[92]
48–53[92]
1.4494
1.4468
1.4541
–
–
9
10
Pour point (°C)
Cetane no.
8
–
13
57.3[13]
4
59[1]
2
46.6[52]
11
Refractory Index at 25
(°C)
Oxidation stability (h
at 110 °C)
Acid value (mg KOH/g)
[96]
Free glycerin (%mass)
[96]
Total glycerin (%mass)
[96]
Sulfur (ppm) [96]
Carbon residue [96]
–
1.4574
1.4357
1.4569
10
55.4[97],
57.1[13]
1.4513
0.16
6.12
8.01
0.71
4.84
12.64
23.56
1.61
1.14
7.1
0.448
0.30
0.106
0.156
0.185
0.046
0.586
0.3[67]
0.649[96]
–
–
0.025
0.16[94],
0.2[98]
0.019[51]
0.006
0.001
0.003
0.001
–
0.02[92]
–
–
0.065
0.22[51]
0.10
0.067
0.068
0.083
–
–
–
4.11
–
0.94
0.01
–
–
3.84
0.026
9.9
0.033
1.81
0.01
6.0
0.047
0.0[68]
0.6214[68]
0.158[96],
0.26[92]
473.8[96]
0.105[96]
12
13
14
15
16
17
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Properties
Stauntonia chinensis
[28]
Raphanus sativus
[74]
Annona diversifolia
[75]
Manchurian apricot
[99]
Siberian apricot
[99]
Heating value (kJ/kg)
Kinematic viscosity (mm2/s) at 40 °C
Kinematic viscosity (mm2/s) at
100 °C
Viscosity Index (VI)
Density (kg/m3) at 40 °C
Flash point (°C)
CFPP (°C)
Cloud point (°C)
Pour point (°C)
Cetane no.
Refractory Index at 25 (°C)
Oxidation stability (h at 110 °C)
Acid value (mg KOH/g)
Free glycerin (%mass)
Total glycerin (%mass)
Sulfur (ppm)
Carbon residue
N/D
4.48
N/D
N/D
N/D
N/D
36.3
4.451
N/D
N/D
4.32
N/D
N/D
4.34
N/D
N/D
N/D
167
-9
N/D
N/D
52.1
N/D
2
0.12
0.003
0.14
5 (mg/kg)
0.05
N/D
N/D
N/D
6
N/D
N/D
N/D
N/D
N/D
0.082
0.000
0.108
0.79 (mg/kg)
N/D
N/D
N/D
N/D
N/D
0
-9
44.7
N/D
N/D
0.5
N/D
N/D
N/D
N/D
N/D
N/D
180
-15
N/D
N/D
49.7
N/D
2.9
N/D
0.015
0.16
4.5 (mg/kg)
N/D
N/D
N/D
175
-14
N/D
N/D
49.2
N/D
2.7
N/D
0.017
0.14
4.7 (mg/kg)
N/D
60
M.A. Wakil et al. / Energy Conversion and Management 94 (2015) 51–67
Fig. 2. Kinematic viscosity at 40 °C (mm2/s).
Fig. 3. Density at 40 °C.
Fig. 4. Calorific value.
between oxidation stability and iodine value, oxidation stability
and saturated fatty acid methyl ester (Sunflower methyl ester)
content, oxidation stability and CFPP, CFPP and iodine value, and
CFPP and Sunflower methyl ester content. However, the only practically significant relationship was that of CFPP vs. Sunflower
methyl ester content when Sunflower methyl ester content was
greater than 12 wt%.
Oghenejoboh and Umukoro [126] indicated that blending of
biodiesel from some feedstocks such as palm, palm kernel, Jatropha and rubber oils with diesel has resulted in an increase in the
calorific value, decrease in density, cloud point, pour point, kinematic viscosity and flash point of biodiesel. The same work was
done by Krishna [127] to improve the cold flow properties of
biodiesel.
Sivaramakrishnan and Ravikumar [128] developed an equation
to predict the higher heating value of biodiesel based on its kinematic viscosity, flash point and density with 0.949 accuracy.
A review on the physical and chemical properties and the fatty
acid composition of 26 biodiesel feedstocks (including of 22 edible
and non-edible oils and four animal fats) was conducted by
M.A. Wakil et al. / Energy Conversion and Management 94 (2015) 51–67
61
Fig. 5. Flash point.
Fig. 6. Cloud point.
Fig. 7. Pour point.
Giakoumis [129]. The author derived an excellent correlation
between iodine number and the degree of unsaturation. Besides, a
small statistical correlation (R2 > 0.60) was also established for
cetane number, density, pour point, carbon content, number of carbon atoms, stoichiometric air–fuel ratio and T90 distillate
temperature.
Kalayasiri et al. [130] developed 2 empirical equations to predict the saponification number and iodine value of biodiesel based
on its fatty acid composition.
SN ¼
X 560 Ai MW i
ð1Þ
IV ¼
X 254 D Ai MW i
ð2Þ
where SN the saponification number, Ai the percentage of each
component, D the number of double bond, MWi the molecular
mass of each component and IV the iodine value.
Krisnangkura [131] illustrated a simple method to estimate the
cetane number of biodiesel which is based on their saponification
and iodine numbers. The range of the calculated values covers all
the cetane numbers of vegetable oil methyl esters determined
experimentally. When it was applied to individual fatty acid methyl
62
M.A. Wakil et al. / Energy Conversion and Management 94 (2015) 51–67
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
Biodiesel-diesel blending
APME + Diesel
Kinematic viscosity
Density at 40 °C
Flash point
Calorific value
CFPP
Cloud point
Pour point
CIME + Diesel
Kinematic viscosity
Density at 40 °C
Flash point
Calorific value
CFPP
Cloud point
Pour point
COME + Diesel
Kinematic viscosity
Density at 40 °C
Flash point
Calorific value
CFPP
Cloud point
Pour point
CMME + Diesel
Kinematic viscosity
Density at 40 °C
Flash point
Calorific value
CFPP
Cloud point
Pour point
JME + Diesel
Kinematic viscosity
Density at 40 °C
at 40 °C
at 40 °C
at 40 °C
at 40 °C
at 40 °C
Mathematical equation
R2
Variable
Ref.
y = 6E05x2 + 0.0169x + 3.3722
y = 2E07x2 + 0.0005x + 0.8298
y = 0.0137x2 0.6219x + 89.225
y = 0.2778x2 41.011x + 45,223
y = 0.0004x2 + 0.0566x 5.3142
y = 9E05x2 + 0.1131x 4.3545
y = 0.0008x2 + 0.1681x 4.4431
y = 7E05x2 + 0.0141x + 3.191
y = 2E07x2 + 0.0004x + 0.8348
y = 0.0048x2 + 0.0445x + 69.912
y = 0.0869x2 69.155x + 45,336
y = 0.0017x2 0.167x + 7.3147
y = 0.0007x2 0.0629x + 8.3846
y = 0.0003x2 + 0.1194x 0.1888
y = 2E05x2 + 0.0045x + 3.3625
y = 9E08x2 + 0.0003x + 0.8351
y = 0.008x2 0.1823x + 73.239
y = 0.008x2 74.066x + 45,292
y = 0.0017x2 + 0.0494x + 6.1818
y = 0.001x2 + 0.0153x + 7.5524
y = 0.0031x2 0.3092x 2.007
y = 4E05x2 + 0.0044x + 3.3503
y = 1E08x2 + 0.0004x + 0.8271
y = 0.0118x2 0.2759x + 79.312
y = 0.0362x2 61.61x + 45,377
y = 0.0018x2 + 0.0532x + 6.1469
y = 0.0009x2 0.0374x + 7.0699
y = 0.002x2 + 0.1696x 1.3706
y = 5E05x2 + 0.0059x + 3.4774
y = 2E07x2 + 0.0004x + 0.8274
0.9947
1
0.9683
0.9898
0.9161
0.978
0.9893
0.9989
0.9998
0.9948
0.9989
0.5621
0.8207
0.9606
0.9075
0.9994
0.9655
0.9994
0.9536
0.9083
0.4009
0.919
0.9997
0.9293
0.9968
0.8972
0.9609
0.7637
0.8463
0.9997
x is the dependent variable; x biodiesel%
[113]
63
M.A. Wakil et al. / Energy Conversion and Management 94 (2015) 51–67
Table 7 (continued)
Biodiesel blends
Property
Flash point
Calorific value
CFPP
Cloud point
Pour point
Kinematic viscosity
Density at 40 °C
Flash point
Calorific value
CFPP
Cloud point
Pour point
Kinematic viscosity
Density at 40 °C
Flash point
Calorific value
CFPP
Cloud point
Pour point
Kinematic viscosity
Density at 40 °C
Flash point
Calorific value
CFPP
Cloud point
Pour point
Kinematic viscosity
Density at 40 °C
Flash point
Calorific value
CFPP
Cloud point
Pour point
Kinematic viscosity
Density at 40 °C
Flash point
Calorific value
CFPP
Cloud point
Pour point
Kinematic viscosity
Density at 40 °C
Flash point
Calorific value
MOME + Diesel
NME + Diesel
POME + Diesel
RBME + Diesel
SME + Diesel
CIME + Diesel
R2
Mathematical equation
2
at 40 °C
at 40 °C
at 40 °C
at 40 °C
at 40 °C
at 40 °C
Biodiesel-biodiesel blending
SFME-POME
Kinematic viscosity at 40 °C
SFME-COME
POME-CME
Cloud point
JCME-CME
CIME-CME
POME-CME
Pour point
JCME-CME
CIME-CME
POME-CME
Cold filter plugging point
JCME-CME
CIME-CME
Variable
y = 0.0085x + 0.081x + 74.015
y = 0.176x2 68.831x + 45,205
y = 0.0007x2 + 0.0719x + 4.6853
y = 0.0008x2 0.09x + 6.7238
y = 0.0004x2 + 0.013x 1.0594
y = 3E05x2 + 0.0192x + 3.2815
y = 1E07x2 + 0.0003x + 0.8272
y = 0.0075x2 + 0.0604x + 74.8
y = 0.0444x2 56.284x + 45,223
y = 0.0006x2 0.1293x + 4.3843
y = 8E05x2 + 0.1146x + 6.6224
y = 0.0013x2 + 0.316x + 0.042
y = 0.0002x2 + 0.0423x + 2.9568
y = 6E07x2 + 0.0005x + 0.8374
N/D
y = 0.5887x2 118.16x + 46,138
N/D
N/D
N/D
y = 7E05x2 + 0.0042x + 3.3741
y = 1E07x2 + 0.0002x + 0.8351
y = 0.0098x2 0.2335x + 77.701
y = 0.1495x2 62.708x + 45,106
y = 0.0022x2 0.1529x + 6.007
y = 0.0023x2 0.1882x + 8.7622
y = 0.0006x2 + 0.0578x 1.3692
y = 4E05x2 + 0.0237x + 3.0904
y = 7E08x2 + 0.0004x + 0.8319
y = 0.0165x2 0.6966x + 80.524
y = 0.1462x2 63.082x + 45,358
y = 0.0007x2 0.0947x + 4.4311
N/D
N/D
y = 2E05x2 + 0.0102x + 3.1682
y = 3E08x2 + 0.0003x + 0.8319
y = 0.0168x2 0.7353x + 81.618
y = 0.0635x2 59.489x + 45,381
y = 0.0007x2 0.0049x + 4.2296
y = 0.0008x2 + 0.0137x + 5.2554
y = 0.0018x2 + 0.145x 0.2907
y = 0.1664x + 2.8361
y = 3.9209x + 825.46
y = 0.6678 x2 1.0049x + 71.355
y = 0.5934x + 45.848
0.9808
0.9869
0.9709
0.2857
0.8353
0.9919
0.9994
0.9464
0.9901
0.9361
0.9606
0.9869
0.9559
0.9993
y = 0.5159x2 1.1195 + 6.3599
y = 0.9533 x2 4.1182x + 6.3457
y = 3.4286x2 20.629x + 13.429
y = 1.1429x2 12.857x + 10.457
y = 3.4286x2 12.171x + 12.171
y = 2.2857x2 20.114x + 14.114
y = 13.714x2 6.2857x + 10.286
y = 13.714x2 8.6857x + 13.286
y = 6.8571x2 15.543x + 11.943
y = 6.8571x2 14.743x + 10.543
y = 5.7143x2 16.286x + 11.486
0.9908
0.9981
0.9704
0.979
0.9867
0.9784
0.9785
0.9972
0.9843
0.9639
0.9918
Ref.
0.994
0.8893
0.998
0.9305
0.9696
0.8763
0.7907
0.9076
0.9599
0.9999
0.9521
0.9849
0.932
0.9983
0.9999
0.9438
0.9989
0.9854
0.7033
0.6526
0.9978
0.9998
0.9965
0.9994
[101]
x POME%
x COME%
x CME%
[2]
N/D Not determined.
esters from C8 to C24, a straight line parallel to that of Klopfenstein
was obtained. The developed equation was as follows:
CN ¼
46:3 þ
5458
ð0:225 IVÞ
SN
ð3Þ
where CN the cetane number, SN the saponification number,
and IV the iodine value.
Ramírez-Verduzco et al. [132] attempted to develop 4 empirical
correlations that can be used estimate the cetane number, kinematic viscosity, density and higher heating value of biodiesels
based on their molecular weight and degree of unsaturation. The
estimated values were found to be in a good agreement with the
experimental values and an average absolute deviation (AAD) of
5.95%, 2.57%, 0.11% and 0.21% for the cetane number, kinematic
viscosity, density, and higher heating value were found. Those
derived equations were as follows:
;i ¼ 7:8 þ 0:302 M i 20 N
ð4Þ
lnðni Þ ¼ 12:503 þ 2:496 lnðMi Þ 0:178 N
ð5Þ
4:9
0:0118 N
Mi
ð6Þ
Pi ¼ 0:8463 þ
di ¼ 46:19 1794
0:21 N
Mi
ð7Þ
64
M.A. Wakil et al. / Energy Conversion and Management 94 (2015) 51–67
Table 8
Mathematical equation for predicting properties for various biodiesel feedstocks.
Biodiesel blends
CMME
CIME
COME
POME
MOME
CMME
CIME
COME
POME
MOME
Various biodiesel
Feedstocks
R2
Ref.
FP = 183.95 (KV) + 1221.6 (KV) + 2099.5
FP = 0.4884 (KV)2 + 5.1448 (KV) + 47.913
FP = 33.934 (KV)2 + 188.35 (KV) + 325.3
FP = 74.797 (KV)2 + 517.44 (KV) + 968.12
FP = 13.79 (KV)2 + 73.438 (KV) + 164.68
CV = 2410.4 (KV)2 + 10, 323 (KV) + 37.233
CV = 560.27 (KV)2 7392.4 (KV) + 63. 326
CV = 33.934 (KV)2 188.35 (KV) + 325.3
1413.7 (KV)2 + 15, 028 (KV) + 79.180
3063.7 (KV) + 55. 367
0.9534
0.9887
0.9933
0.9569
0.9724
0.9891
0.9975
0.9933
0.996
0.9912
[113]
Higher heating value (HHV) vs. Kinematic viscosity (KV)
Kinematic viscosity (KV) vs. Density (DN)
Kinematic viscosity (KV) vs. Flash point (FP)
Higher heating value (HHV) vs. Density (DN)
Higher heating value (HHV) vs. Flash point (FP)
Density (DN) vs Kinematic viscosity (KV)
Flash point (FP) vs. Kinematic viscosity (KV)
Density (DN) vs. Flash point (FP)
Density (DN) vs. Calorific value (CV)
HHV = 0.4625 (KV) + 39.450
KV = –16.155 (DN) + 930.78
KV = 22.981 (FP) + 346.79
HHV = –0.0259 (DN) + 63.776
HHV = 0.021 (FP) + 32.12
DN = 15.77 (KV) + 929.59
FP = 12.36 (KV) + 176.3
FP = 1.46 (DN) 1099.9
CV = 0.0207 (DN) + 23.28
0.9677
0.9902
0.9819
0.7982
0.9530
0.9724
0.964
0.91
0.9568
[134]
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]
Property
Flash point (FP) vs. kinematic viscosity (KV)
Calorific value (CV) vs. kinematic viscosity (KV)
Mathematical equation
2
[135]
N/D Not determined.
where ;i the cetane number of the ith FAME, Mi the molecular
weight of the ith FAME, N the number of double bonds in a given
FAME, ni the kinematic viscosity at 40 °C of the ith FAME in mm2/
s, Pi the density at 20 °C of the ith FAME in g/cm3 and di the
higher heating value of the ith FAME in MJ/kg.
Talebi et al. [133] developed a new software package (the BiodieselAnalyzerÓ) that can predict 16 different properties of biodiesel based on the fatty acid methyl ester profile of the oil feedstock
used in making it.
The polynomial curve fitting method has been used in several
studies [2,101,113,134,135] to predict the properties of biodiesel–diesel blends. Mathematically, a polynomial of order k in X is
expressed in the form of:
Based on the review work that is conducted in this paper, for
future work it can be recommended to investigate the optimization
of biodiesel blends (both biodiesel–diesel and biodiesel–biodiesel)
as different biodiesel feedstocks possess some superior qualities as
well as some inferior qualities. Moreover, in depth instrumental
analysis for instance, effect of temperature, reaction time and catalyst type on biodiesel yield can help researchers to select more
potential candidate for biodiesel to be used commercially.
Y ¼ Co þ C1X þ C2X2 þ þ CkXk
Acknowledgements
where X is the variable as a function of available data and Y is the
predicted value. Table 7 shows some examples of the generated
equations for various biodiesel blends. Table 8 shows some mathematical equations for predicting properties of various biodiesel
feedstock.
6. Conclusion
In recent time, the research on biodiesel is reaching to the peak
because it is found as a good complementary substitute to diesel
than other sources. A number of research have been conducted
on biodiesel from different feedstock’s by various researchers and
some are still ongoing for a considerable level of usage. Accordingly, this study highlighted the physicochemical properties under
various biodiesel–diesel blend. For clear understanding, a short
description on feedstock has been also carried out. A polynomial
curve fitting method is used to generate mathematical equation
for different biodiesel–diesel blend in order to predict the properties of any percentage of biodiesel in the blend. This would help the
researchers to optimize the blend percentage which is necessary to
meet the impending scarcity of petro-diesel. The other profitable
advantage would be the proper selection of combined feedstock
to improve the performance of engine relative to diesel without
any or little modification. This is necessary as there is the challenge
of using single feedstock as biodiesel for better performance along
with some demerits of edible feedstock.
7. Recommendation
The authors would like to thank the Ministry of Higher Education and University of Malaya, Malaysia for the financial assistance
through High Impact Research Grant titled: Development of alternative and renewable energy carrier (DAREC) with Grant Number
UM.C/HIR/MOHE/ENG/60.
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