Review pubs.acs.org/IECR
A Review of Flash Point Prediction Models for Flammable Liquid Mixtures Li Yee Phoon, Azizul Azri Mustaffa,* Haslenda Hashim, and Ramli Mat† Process Systems Engineering Centre (PROSPECT), Faculty of Chemical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia S Supporting Information *
ABSTRACT: Flash point has safety implications and is therefore used to ascertain associated explosion hazards and fire of a flammable solution. Technological advances in the synthesis of new blends and chemical waste handlers have created a high demand for the flash point database and the flash point estimation methods of flammable liquid mixtures have become important. The present study reviewed the estimation model of the flammable liquid mixture flash point. These models are based on the following parameters: (1) either a normal boiling point or a composition range, (2) molecular structure (molecular descriptors), and (3) vapor pressure. Models based on boiling points or the composition ranges are empirically obtained using a mathematical regression method or an artificial neural network (ANN) approach. The quantitative structure−property relationship (QSPR) method is used to analyze the relationship between the flash point and the molecular structures that exist in a flammable mixture. Vapor-pressure-based models, which were formulated using Le Chatelier’s rule are more reliable, compared to other prediction models. However, the prediction efficiencies of these vapor-pressure-based models for nonideal mixtures are strongly depend on the accuracy of the activity coefficient models used. Several activity coefficient models are discussed at the end of this paper. In summation, there is no universal flash point prediction model for all flammable mixtures.
1. INTRODUCTION The flash point indicates the lowest temperature at which a volatile fuel ignites or flashes when it contacts a spark or flame. Good experimental flash point data and estimation methods are vital for evaluating the handling of flammable liquids for safety regulations.1 Experimental flash point data can be obtained using either the open-cup method or the closed-cup method. The standard flash point testing methods for various types of samples that followed the American Society of Testing and Materials (ASTM) are listed in Table 1. An open-cup tester generally results in a higher flash point than that obtained using a closedcup tester,10 because an open-cup tester permits the escape of the low-boiling-point component in the sample mixture into the surrounding atmosphere prior to the application of the flame.11 By contrast, the sample in a closed-cup tester is separated from the surrounding atmosphere, thereby preventing mass transfer between the surrounding atmosphere and the sample. Reliable and consistent results can be obtained using a closed-cup tester, particularly for multicomponent mixtures.12 The experimental uncertainties reported using either the openor closed-cup methods are in the 5−8 °C range.13 The closedcup method is usually used14 to classify the flammability of liquids that flash over the specific temperature ranges given in Table 1. The open-cup method is recommended for heavy compounds with relatively high flash points, such as lubricating oil.13 While it is always preferable to obtain flash points experimentally, it is time-consuming and it can be extremely difficult to analysis the flash point of some toxic and radioactive compounds.13 The development of a more rapid estimation technique for determining the flash points of flammable liquid © 2014 American Chemical Society
mixtures is of great interest. Most of the available estimation methods are applicable only to pure compounds. Some of these methods are listed in Table 2. The flash point data for pure compounds are widely available in many online databases such as DIPPR,23 NIOSH,24 Merck,25 and the chemical database at the University of Akron.26 A similar work has been done by Vidal et al.27 and Liu et al.,28 respectively. Vidal et al.27 have provided a comprehensive review at the flash point and the flammability limit estimation methods of pure compounds and mixtures. In additional, the authors have discussed the properties of flash point and flammability limits, together with the combustion theory and flames. Liu et al.28 have presented an overview of the estimation model for pure components and mixtures by classified the model based on the vapor pressure, the composition range of the flammable component present in the mixture, the molecular structure, and the boiling point of the flammable liquids. The present study addresses to present the prediction models for estimating the closed-cup flash points of various types of combustible liquid mixtures. An extensive range of flammable mixtures will be covered in this study, including miscible or partial miscible mixtures, aqueous solution, petroleum and/or biodiesel blends. The reviewed models are based on the normal boiling point or the composition range of the flammable components in the mixture; the molecular structure; and vapor pressure of the individual components present in the mixture. In most cases, the solutions involved in Received: Revised: Accepted: Published: 12553
March 24, 2014 June 27, 2014 July 10, 2014 July 10, 2014 dx.doi.org/10.1021/ie501233g | Ind. Eng. Chem. Res. 2014, 53, 12553−12565
Industrial & Engineering Chemistry Research
Review
Table 1. ASTM Standard Flash Point Testing Methods for a Flammable Liquid test method
ASTM designation
Cleveland Open-Cup Tester Tag Open-Cup Apparatus Tag Closed-Cup Tester Pensky−Martens Closed-Cup Tester
D92 D1310 D56 D93
Modified Continuously Closed-Cup (MCCCFP) Tester Continuously Closed-Cup (CCCFP) Tester Small Scale Closed-Cup Tester Small Scale Closed-Cup Tester
scope
reference 2 3 4 5
D7094
petroleum products (except fuel oils) with flash points between 79 °C and 400 °C liquids with flash points between 18 °C and 165 °C liquids with viscosities below 5.5 mm2/s and flash points below 93 °C petroleum products with flash points between 40 °C and 360 °C and biodiesels with flash points between 60 °C and 190 °C fuels, lube oils, solvents, and other liquids with flash points between 35 °C and 225 °C
D6450
fuels, lube oils, solvents and other liquids with flash points between 10 °C and 250 °C
7
D3828 D3278
petroleum products and biodiesel liquid fuels with flash points between −30 °C and 300 °C paints, enamels, lacquers, varnishes, and related products with viscosities lower than 150 St at 25 °C and flash points between 0 °C and 110 °C
8 9
6
Table 2. Flash Point Prediction Model for Pure Compounds model
description
based on the boiling point of pure compound
the model was tested on 1221 compounds from different chemical families, and a unique correlation was associated with each chemical family the flash points of 124 linear and branched acyclic alkenes were predicted based on the number of carbon atoms, normal boiling a general flash point prediction equation for organic compounds from different chemical point, and standard enthalpy of vaporization at 298.15 K families based on normal boiling temperature and heats of the model was tested on 611 chemical species from different families vaporization and combustion based on the number of carbon atoms and normal boiling a total of 77 chemical families with 1471 pure compounds were used to establish a general point temperature correlation based on the structural group contribution or molecular 530 substances were used to train the hybrid algorithm, which included an artificial neural structure network (ANN) with particle swarm optimization quantitative structure−property relationship (QSPR) approaches were used to estimate the flash points of 1294 pure compounds. the flash points of different classes of unsaturated hydrocarbons containing alkenes, alkynes, and aromatics are estimated based on the number of carbon and hydrogen atoms
reference 15 16 17 18 19 20 21 22
determined according to Pi = xiγiPsat i , where xi is the liquid is the phase mole fraction of component i, and γi and Psat i activity coefficient and the saturated vapor pressure of pure compound i at Teb. The value of Psat i can be obtained by using the Antoine equation:
the industries are usually nonideal mixtures. Hence, the activity coefficient model, which is used to govern the liquid-phase nonideality, will be discussed at the end of this study.
2. EMPIRICAL FLASH POINT PREDICTION MODELS BASED ON NORMAL BOILING POINTS OR COMPOSITION RANGES The flash point of a fuel mixture can be empirically correlated with the normal boiling point of the fuel mixture and the composition range of the flammable component that is present in a liquid mixture. Empirical flash point prediction models are formally simple and easy to construct from experimental data. Generally, the models are formulated using mathematical regression methods or artificial neural network (ANN) techniques. 2.1. Flash Point Prediction by Mathematical Regression Methods. The expressions for the flash point prediction models by mathematical regression are summarized and compared in Table 3. 2.1.1. Catoire Model. The Catoire model, which was originally constructed for pure organic liquids,17 has been extended to estimate the flash points of combustible solvent blends.29,30 This model is depends on the standard enthalpy of ° , in kJ/mol); the normal vaporization at 298.15 K (ΔHvap boiling point of the fuel mixture (Teb, in K); and the number of carbon atoms present in the fuel vapor mixture that above the liquid phase (n). The vapor mixture was considered as a fictitious compound in this case. In the Catoire model, Teb is calculated by ∑iPi = 1 atm, where Pi is the partial pressure of component i at Teb. Pi is
log Pisat = Ai −
Bi T + Ci
(1)
ΔH°vap in the Catoire model is determined from the slope of ln P = f(1/T) (the Clausius−Clapeyron method), where T = −ΔH°vap/R; and P = ∑iPi at T. The parameter n is defined by n = ∑iyini, where ni refers to the number of carbon atoms present in compound i and yi is the vapor phase molar fraction of compound i at the flash point of the fuel mixture. The flash point of the mixture is a variable; therefore, yi is always indicated at the normal boiling point of the fuel mixture, unless the difference between the yi at the flash point and the normal boiling point of the fuel mixture is significant.30 The Catoire model has been shown to accurately predict the flash points for ideal and nonideal binary and ternary mixtures and even for mixtures29,30 that exhibit minimum flash point behavior (i.e., the fuel mixture exhibits a lower flash point than the individual compounds).36 2.1.2. Wickey Model. Wickey and Chittenden31 developed a model to estimate the flash points of petroleum blends. This model is established based on the index of the mixture, Imix. Imix is obtained by averaging the value of the flash point indices of the petroleum components with their volume fraction (νi). It is mathematically expressed as Imix = ∑iνiIi, where the flash point 12554
dx.doi.org/10.1021/ie501233g | Ind. Eng. Chem. Res. 2014, 53, 12553−12565
binary aqueous alcohol mixtures of methanol, ethanol, propanol and isopropanol
Hristova model33
Kim and Lee model35 (partial least-squares (PLS) method)
binary liquid mixtures of n-butanol−n-propionic acid blend; methylethylketone−toluene blend; 2-propanol−toluene blend; n-butanol−p-xylene blend; and n-propanol−n-propionic blend
diesel−biodiesel blends of diesel−palm oil biodiesel blend; diesel-castor oil biodiesel blend; and palm oil biodiesel−castor oil biodiesel blend
butyric, propionic, and acetic acids blends mixed with their corresponding anhydride or water
Garland and Malcolm model32
Mejı ́a model34
petroleum blends
Wickey and Chittenden model31
mixture types
binary and ternary miscible mixtures
Catoire model29,30
model
2414 6.118 + log10(Imix )
° × ΔH vap
+0.16845
×n
−0.05948
mathematical equation
+ 42.59
+0.79686
1 ; a + bx
y=
Tfp Ti , fp
12555
linear relationship between the input data scores and output data scores: uh = bhth + eh where bh is the regression coefficient between the input data score vector and output data score vector
data blocks for the output variables, Y: h=1 Y = ∑a uhqhT + F
data blocks for the input variables, X: h=1 X = ∑a thPhT + E
(3) palm oil biodiesel−castor oil biodiesel blend: Tfp = 430.00 − 294.14vc + 167.86vc 2 where νp is the volume fraction of biodiesel in the blend and νc is the volume fraction of castor oil biodiesel
(2) diesel-castor oil biodiesel blend: Tfp = 350.28 + 0.0046e10.72vc
(1) diesel−palm oil biodiesel blend: Tfp = 343.03 − 48.41vp + 120.9vp 2
y=
(2) reciprocal function:
(1) 3rd-order polynomial function: Tfp = b0 + b1x + b2x 2 + b3x 3
× (wt% butyric acid)
− 1.0934 × (wt% butyric acid) − 0.0027 × (wt% acetic acid)
Tfp = 267.53 − 1.5927 × (wt % acetic acid) − 1.3897 × (wt% propionic acid)
Tfp =
Tfp = 1.477 × Teb
Table 3. Comparison of the Mathematical Regression Flash Point Prediction Methods comments
more data should be included to develop a robustness model
prediction outside the consideration range is not valid
simple correlation; more data should be included for develop a robustness model
limited to the acid mixtures that are used to develop the correlation
limited to hydrocarbon mixtures that behave almost as ideal solutions; the model is not recommended for nonideal mixtures
a general empirical correlation for flash point prediction; however, more experimental validation is needed
Industrial & Engineering Chemistry Research Review
dx.doi.org/10.1021/ie501233g | Ind. Eng. Chem. Res. 2014, 53, 12553−12565
Industrial & Engineering Chemistry Research
Review
Table 4. Comparison of the Performance of the ANN Approach with that of Other Statistical Analysis Methods for Flash Point Estimation findings
reference
jet fuels
multiple linear regression (MLR) correlation
• the mean of the absolute relative error obtained using the MLR equations was >2.8%, whereas that obtained using ANN was always
