Energy Fuels 2010, 24, 3262–3266 Published on Web 04/29/2010
: DOI:10.1021/ef100143f
Predicting the Density of Straight and Processed Vegetable Oils from Fatty Acid Composition K. Anand, Avishek Ranjan, and Pramod S. Mehta* Department of Mechanical Engineering, Indian Institute of Technology (IIT ) Madras, Chennai 600036, Tamil Nadu, India Received February 3, 2010. Revised Manuscript Received April 7, 2010
Because of the renewable nature, emission advantage, and easy adaptation, vegetable-oil-based fuels are emerging as the most promising alternatives to fossil diesel for use in compression-ignition engines. In this study, a unified approach is proposed for predicting the densities of straight and processed vegetable oils from their fatty acid composition, obviating the need of measured values particularly for use in process modeling studies. The proposed methodology has been validated using the measured densities of 11 different vegetable oils and 13 different processed vegetable oils. The predictions are found to be in good agreement (∼1% error) even at higher temperatures up to 90 °C.
The density of a fuel is found to relate to other properties, such as the heating value and cetane number.6 In this regard, an accurate estimation of thermo-physical properties, such as viscosity and density of alternative fuels, particularly straight and processed vegetable oils, is quite essential in the context of their possible use in internal combustion engines. A predictive methodology for density prediction is essential in process modeling applications, wherein density variations with temperature are required, such as spray atomization in engines. The specific fuel consumption, brake thermal efficiency, and nitric oxide emissions vary widely with different biodiesel fuels investigated.7-11 Besides the engine types and its operating conditions, changes in the composition of biodiesel fuel derived from different sources are considered to be the major cause of these performance variations. Thus, there is a need of characterizing biodiesel fuels based on their origin and composition. This work proposes a unified methodology for predicting the density of straight and processed vegetable oils based on their fatty acid composition, which would facilitate density estimates of these emerging fuels. The approach will prove to be useful in situations where measured data are unavailable or measurements are deemed to be difficult. It requires a priori knowledge of fatty acid composition and is fairly general and robust to be used for any vegetable oil/ biodiesel. Further, it can also be used in analyzing the contribution of individual fatty acid constituents on the predicted density of vegetable oil/biodiesel where necessary.12
1. Introduction The intense search is on going for an environmentally friendly and economical alternative fuel. The renewable nature of both straight and processed vegetable oils with their higher density, almost zero sulfur content, cleaner burning, and comparable energy content to diesel fuel remains attractive as possible alternatives to diesel fuels in several applications, including internal combustion engines.1,2 Straight vegetable oils (SVOs) are primarily mixtures of triglycerides, wherein three fatty acids are bonded to a glycerol. The commonly occurring fatty acids in vegetable oils include myristic (C14:0), palmitic (C16:0), stearic (C18:0), oleic (C18:1), linoleic (C18:2), and linolenic (C18:3) acids. The molecular mass of any combination of three commonly occurring fatty acids contained in a triglyceride molecule is generally found to be between 94 and 97% of the total triglyceride molecular mass. The processed form of vegetable oil, i.e., biodiesel, is obtained by the transesterification process of SVO sources. The transesterification process removes the glycerol to bring their viscosity closer to diesel fuel, which in turn facilitates their injection into the engine cylinder without needing change in the injection equipment.1 This has made use of biodiesel fuel very attractive and is being widely investigated for use in engines.3 The biodiesel spray and combustion analysis require knowledge of its thermo-physical properties influencing these processes. Density is one of the important properties of a fuel that affects spray atomization.4 The changes in fuel density also affect the start of the dynamic injection event.5 The higher density fuels, such as straight and processed vegetable oils, are found to result in a higher mass of fuel injection because the metering of the fuel is performed on a volumetric basis.3
2. Existing Approaches The existing methodologies in the literature for estimating the density of straight and processed vegetable oils are generally (6) Tat, M. E.; Van Gerpan, J. H. J. Am. Oil Chem. Soc. 2000, 77, 115– 119. (7) Agarwal, A. K.; Das, L. M. Trans. ASME 2001, 123, 440–447. (8) Ramadhas, A. S.; Muraleedharan, C.; Jayaraj, S. Renewable Energy 2005, 30, 1789–1800. (9) Puhan, S.; Vedaraman, N.; Ram, B. V. B.; Sankaranarayanan, G.; Jeychandran, K. Biomass Bioenergy 2005, 28, 87–93. (10) Raheman, H.; Phadatare, A. G. Biomass Bioenergy 2004, 27, 393–397. (11) Canakci, M. Proc. Inst. Mech. Eng. 2005, 219, 1–8. (12) Goodrum, J. W.; Eitcman, M. A. Bioresour. Technol. 1996, 56, 55–60.
*To whom correspondence should be addressed. Telephone: þ91-442257-4670. Fax: þ91-44-2257-4652. E-mail:
[email protected]. (1) Ma, F.; Hanna, M. A. Bioresour. Technol. 1999, 70, 1–15. (2) Ramadhas, A. S.; Jayaraj, S.; Muraleedharan, C. Renewable Energy 2004, 29, 727–742. (3) Graboski, M. S.; McCormick, R. L. Prog. Energy Combust. Sci. 1998, 24, 125–164. (4) Peterson, C. L.; Auid, D. L. FACT. American Society of Mechanical Engineers (ASME), 1991; Vol. 12, pp 45-54. (5) Tat, M. E.; Van Gerpan, J. H.; Soylu, S.; Canakci, M.; Monyem, A.; Wormley, S. J. Am. Oil Chem. Soc. 2000, 77, 285–289. r 2010 American Chemical Society
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Energy Fuels 2010, 24, 3262–3266
: DOI:10.1021/ef100143f
Anand et al.
Table 1. Available Correlations for SVO Density error (%) investigator 19
Lund Halvorsen19 Rodenbush et al.21
SVO type
basis of correlation
controlling variables
temperature range (°C)
average
maximum
all 18 vegetable oilsa 14 vegetable oilsb
empirical modified Rackett reduced density reduced density modified Rackett
SV and IV Tc and Pc Tc and Pc MW Tc and Pc
15 from -10 to 200 from -19.8 to 110.2
0.16 0.14 0.21 0.28 0.30
0.57 0.70 1.03 1.00 1.15
a
Crambe, olive, ouricurry, rapeseed, safflower, sesame, soybean, stillingia, sunflower, tower rapeseed, babassu, coconut, corn, cottonseed, palm, peanut, palm kernel, and babassu oils. b Coconut, corn, cottonseed, soybean, sesame, peanut, palm, safflower, olive, rapeseed, sunflower, palm kernel, babassu, and rice bran oils.
based on the formulation proposed by Rackett13 for pure liquids. In his work, Rackett proposed a generalized equation estimating the molar volume of saturated liquids in terms of their critical properties as Vs ¼ Vc Zc ½ð1 - Tr Þ
2=7
Swern20 proposed the correlations for SV and IV in terms of the molecular weight of oils as 168312 ð4Þ SV ¼ MWoil
ð1Þ IV ¼
where Vc (=RTc/Pc) is the critical volume and Zc is the critical compressibility factor. Tr, the reduced temperature, is the ratio of the temperature T at which the density is required to be estimated and the critical temperature Tc. The values of the critical temperature Tc and pressure Pc needed for estimating the molar volume and, therefore, density are either taken from an experimental database or calculated using correlations available in the literature.14,15 Subsequently, Spencer and Danner16 modified the Rackett equation to broaden its applicability by including a parameter ZRA in place of Zc. In a critical review17 on predictions of the saturated liquid density for different hydrocarbons, it is opined that the modified Rackett equation gives the best predictions over a range of temperatures. The Rackett parameter ZRA is either experimentally determined from the measured/reference density wherever available or estimated using an available correlation in terms of the acentric factor18 ZRA ¼ 0:29056 - 0:08775ω ð2Þ
76142:7D MWoil
ð5Þ
where D is the number of double bonds and MWoil is the molecular weight of vegetable oil obtained from the expression X xi MWi þ 38:0488 ð6Þ MWoil ¼ 3 where xi and MWi are the mole fraction and molecular weight of fatty acid constituents of vegetable oil. Using the modified Rackett equation for liquid mixtures, Halvorsen et al.19 estimated the density of the fatty acid mixture and introduced a density correction factor for the presence of glycerol in the triglycerides. They evaluated the critical temperature by the Fedors method14 and the critical pressure by the Joback method.14 Their method of obtaining the value of the Rackett parameter ZRA required in the correlation is rather indirect and depends upon the measured vegetable oil reference density (FR) at a reference temperature TR. Thus 2=7 - 1 MWPc ½1þð1 - TRr Þ ð7Þ ZRA ¼ FR RTc
A summary of correlations existing in the literature for density estimation of vegetable oils is given in Table 1. A very early correlation for predicting the density of vegetable oils is attributed to Lund given in ref 19. Lund correlation provides the specific gravity of the vegetable oil at 15 °C compared to water at 15 °C in terms of their saponification value (SV) and iodine value (IV) as specific gravity ¼ 0:8475 þ 0:00030SV þ 0:00014IV ð3Þ
where TRr (=TR/Tc) is the reduced temperature based on the reference temperature TR. Halvorsen et al. reported fairly accurate estimates of densities for 18 vegetable oils, with an average error of 0.14% as compared to an average error of 0.16% from the Lund correlation. Subsequently, Rodenbush et al.21 developed a methodology to estimate the density of vegetable oils in terms of the reduced density, which is a ratio of the experimental density (F) to critical density (Fc). The critical density estimates are first made using P xi MWi ð8Þ Fc ¼ P xi Tci R Pci where Tci and Pci are the critical temperature and critical pressure of the fatty acid constituents. In their procedure, this reduced density estimate is corrected such that it assumes a constant value for the oils investigated. The dependence of the corrected reduced densities F0 and F00 on reduced temperatures is plotted to obtain the oil density from the correlations based on the consideration of
About this correlation, Swern20 observed that the tests for SVs are obsolete and the parameters SV and IV could be related to the composition of oils, which can be measured accurately by a chromatography technique. Thus, the density variations of the straight and processed vegetable oils can be related to their fatty acid compositions. (13) Rackett, H. G. J. Chem. Eng. Data 1970, 15, 514–517. (14) Reid, R. C.; Prausnitz, J. M.; Poling, B. E. The Properties of Gases and Liquids, 4th ed.; McGraw-Hill: New York, 1986. (15) .Poling, B. E.; Prausnitz, J. M.; O’Connell, J. P. The Properties of Gases and Liquids, 5th ed.; McGraw-Hill: New York, 2001. (16) Spencer, C. F.; Danner, R. P. J. Chem. Eng. Data 1972, 17, 236– 241. (17) Spencer, C. F.; Adler, S. B. J. Chem. Eng. Data 1978, 23, 82–89. (18) Yamada, T.; Gunn, R. D. J. Chem. Eng. Data 1973, 18, 234–236. (19) Halvorsen, J. D.; Mammel, W. C.; Clements, L. D. J. Am. Oil Chem. Soc. 1993, 70, 875–880. (20) Swern, D. Bailey’s Industrial Oil and Fat Products; WileyInterscience: New York, 1979.
(21) Rodenbush, C. M.; Hsieh, F. H.; Viswanath, D. S. J. Am. Oil Chem. Soc. 1999, 76, 1415–1419.
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Energy Fuels 2010, 24, 3262–3266
: DOI:10.1021/ef100143f
Anand et al.
Table 2. Available Correlations for Biodiesel Density error (%) investigator
biodiesel type
Yuan et al.22 Baroutian et al.23
soybean palm
Alptekin and Canacki24
sunflower, soybean, canola, corn, cottonseed, waste palm blends (2, 5, 10, 20, 50, 75%) with diesel soybean blends (20, 40, 60, 80%) with diesel and neat soybean jatropha
Yoon et al.25 Veny et al.26
either the critical density or the composition details as ! 29:73 0 F ¼F þ2 Fc X xi MWi þ 0:58Þ F00 ¼ Fð0:00022
basis of correlation
controlling variables Tc and Pc
temperature range (°C)
modified Rackett ANN modified Rackett empirical
Tc and Pc biodiesel fraction
15
empirical
blending ratio
0-200
modified Rackett
Tc and Pc
15-90
average
0-100 14-90
maximum
