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FLAMMABILITY CHARACTERISTICS OF PURE HYDROCARBONS Table 3. SGC values for estimation of the flash point temperature. Tareq A. Albahri HC type Serial Group (FPT)i no. Chemical Engineering Dept. - Kuwait University Paraffins 1 −CH3 0.4832 P.O.Box 5969 - Safat 13060, Kuwait 2 >CH2 0.5603 3 α- >CH− 0.5275 Introduction 4 β- >CH− 0.5499 The flammability characteristics of chemical substances are very 5 g- >CH− 0.4778 important for safety considerations in storage, processing, and 6 δ- >CH− 0.4543 handling. These characteristics which include the flash point 7 α- >C< 0.4008 temperature (FPT), the auto ignition temperature (AIT), and the 8 β- >C< 0.5281 upper and lower flammability limits (UFL, LFL) are some of the 9 −C2H5 (branch) 1.0370 most important safety specifications that must be considered in Olefins 10 =CH 0.4078 2 assessing the overall flammability hazard potential of a chemical 11 =CH− 0.6037 substance, defined as the degree of susceptibility to ignition or 12 =CH− (cis) 0.5913 release of energy under varying environmental conditions. 13 =CH− (trans) 0.6216 Experimental values of these properties are always desirable, 14 α- >C= 0.7135 however, they are scars and expensive to obtain. When experimental 15 β- >C= 0.6550 values are not available and determining them by experimental means 16 =C= 0.8659 is not practical, a prediction method which is desirably convenient 17 ≡CH 0.4475 and fast must be used to estimate them. 18 ≡C− 0.8387 Cyclic 19 >CH 0.6080 2 Technical Development 20 >CH− 0.4217 When the flammability characteristics cannot be determined 21 α- >CH− (cis) 0.7148 experimentally, empirical equations for their determination are 22 α- >CH− (trans) 0.6986 available. However, these methods available in the literature for 23 β- >CH− (cis) 0.6518 estimation of the flash point1,2,3,4,5,6, the autoignition temperature7, the 24 β- >CH− (trans) 0.4601 upper and lower flammability limits1,8,9,10,11 are not very accurate and 25 g- >CH− (cis) 0.7167 sometimes produce serious errors. Here we develop a structural group 26 g- >CH− (trans) 0.5899 contribution (SGC) method12 to predict the FPT, AIT, UFL, and LFL 27 >C< 0.1847 of pure hydrocarbons with higher accuracy than the above methods 28 =CH− 0.5287 that can be applied with less difficulty using only the molecular Aromatics 29 =CH− 0.6205 structure of the compound. The method is notable for the absence of 30 >CH 1.5159 2 any theoretical procedure which has previously been used to estimate 31 >C= (fused) 0.8898 the AIT of pure substances from only their molecular structure. 32 >C= 0.6150 The structural groups derived from the Joback group 33 >C= (ortho) 0.7535 contribution approach12, with some modification, and their 34 >C= (meta) 0.7384 contribution values are shown in Table 1 for calculating the flash 35 >C= (para) 0.7675 point as an example. Other structural groups and their contribution Groups 32 through 35 are all non-fused. In non-cyclic compounds, α-, β-, values have been developed for the AIT, LFL, and UFL. The target g-, and δ- refer to the second, third, fourth, and fifth positions along the property are calculated using the following non-linear equation13, HC chain, respectively. In cyclic compounds, α-, β-, and g- refer to the second, third, and fourth position along the cyclic ring with respect to     2  3  4 (1) group 20, respectively. Φ = a + b∑(Φ) + c∑(Φ)  + d∑(Φ)  + e∑(Φ)     i i   i i   i i   i i   Table 2. Coefficients for Equation (1). where Φ is FPT, AIT, UFL, or LFL, ∑(Φ)i is the sum of the Property a b c d e molecular structure group contributions for FPT (Table 1), AIT, UFL, FPT 84.65 64.18 -5.6345 0.360 -0.0101 or LFL (not shown), and a, b, c, d, and e are constants from Table 2. AIT 780.42 26.78 -2.5887 -0.3195 -0.007825 The data on the flammability properties of more than 500 pure UFL 18.14 3.4135 0.3587 0.01747 3.403E-04 substances were obtained from the property databanks of the AIChE- LFL 4.174 0.8093 0.0689 0.00265 3.76E-05 DIPPR14 on the AIT and the API-TDB15 on the FPT, UFL, and LFL. An optimization algorithm based on the least square method was used Table 3. Statistical analysis for the flammability predictions of to probe the structural groups and calculate their contribution values Equation (1) to the target property. The nonlinear regression algorithm minimizes Propert No. of data Property R2 Average the sum of the difference between the calculated and experimental y points Range deviation values of the target properties using the solver function in Microsoft FPT 287 165 - 511 K 0.99 1.68 % a Excel. Convergence was always achieved in less than one minute on AIT 131 473 - 828 K 0.92 4.20 % a a Pentium IV-1.7GHz PC. The average deviations in the predicted UFL 464 4 - 100 V% 0.96 1.25 V%b properties for all types of hydrocarbons are shown in Table 3. The LFL 454 0.11 - 6 V% 0.93 0.04 V%b accuracy of the model predictions are shown in Figures 1 and 2 as a Ave. % error, b Difference between experimental and calculated values. examples. Prepr. Pap.-Am. Chem. Soc., Div. Fuel Chem. 2003, 48(2), 683 probe the structural groups that have significant contribution to the 600 overall flammability property of pure components. This work demonstrates that the complex flammability properties can be modeled by a simple SGC method using non-linear- 500 R2 = 0.99 regression optimization models. Considering the difficulty and complexity of developing a first principles model of the flammability K). characteristics involving the kinetics and dynamics of combustion on nt ( the molecular level, the SGC method can be an effective alternative. oi 400 P The SGC predictive models can learn about inherent relationships h as among various structural groups and their contribution to the overall Fl d flammability property of the molecule such as group interactions, dicte 300 structural orientation, skew, hindrance, steric, resonance, inductive, re and chiral effects that are usually unknown. Furthermore, the method P is based on the molecule’s structural data which is always known. 200 Once properly developed, SGCs offer predictions quickly and accurately on a personal computer using a spreadsheet. The SGC method can also be used for synthesizing molecules (i.e. choosing a molecule with a desired property). This can be done by invoking the 100 inverse property of the model; what is the best combination of inputs 100 200 300 400 500 600 that lead to certain outputs. Experimental Flash Point (K) The SGC method is especially useful for the automatic generation and reliable estimation of AIT, FPT, UFL, and LFL of Figure 1. Parity plot for the flash point temperature of 299 pure pure component for which no data exists. The method may be used in hydrocarbon liquids using SGC values from Table (1). predictive models to estimate the flammability characteristics for light petroleum fuels such as naphtha and gasoline, using proper 4 mixing rules and interaction parameters, when the molecular composition is known16. Finally, the method is notable for the 3.5 absence of any group contribution method which has previously been R2 = 0.93 used to estimate the AIT of pure hydrocarbon liquids from only their 3 structure. 2.5 Acknowledgment. This work is part of a project on the %) simulation of light petroleum fractions supported by Kuwait V L ( 2 University research grant no. EC 04/01. LF 1.5 d References e dict 1 (1) Bodhurtha, F. P. Industrial Explosion Prevention and Protection, Mc- re Graw-Hill, New York, 1980. P 0.5 (2) Riazi, M. R. and Daubert, T. E. Hydrocarbon Processing. 56, 81-83, 1987. 0 (3) Patil, G. S. Fire and Materials, 12, 127-131, 1988. (4) Satyanarayana, K. and Kakati, M. C. Fire and Materials, vol. 15, pp. 97- -0.5 100, 1991. -1 (5) Jones, J. C. Journal of FIRE SCIENCES, vol. 16, pp. 222-229, May/June 1998. -1 0 1 2 3 4 (6) Suzuki, T.; Ohtaguchi, K.; Koide, K. J. of Chem. Eng. of Japan, 1991, Experimental LFL (V%) 24 (2), 258-261. (7) Suzuki, T. Fire & Materials. 1994, 18, 81-88. Figure 2. Parity plot for the LFL of 472 pure hydrocarbon liquids (8) Crowl, D. A. and Louvar, J. F. Chemical Process Safety, Fundamentals using the SGC method. with Applications, Prentice Hall, Englewood Cliffs, 1990. (9) Santamaría-Ramiro, J. M. and Braňa-Aísa, P. A. Risk Analysis and Reduction in the chemical Process Industry, Chapman & Hall, New York, 1998, p.64. Discussion (10) Martin S. High, Ronald P. Danner, Ind. Eng. Chem. Res. 1987, 26, The model predictions were in excellent agreement of the 1395-1399. experimental data for all the properties investigated as shown in (11) William H. Seaton, J. of Hazardous Materials, 1991, 27, 169-185. Table 3. The correlation of the predictions of the proposed model (12) Reid, R.C.; Prausnitz, J.M.; Polling B.E.; The properties of gases and shows the superiority of the SGC method over the other methods in liquids, Hill: N.Y., 1987. the literature. The maximum errors and deviations for all the (13) Albahri, T. A. Ind. and Eng. Chem. Res., in press. predicted properties are also satisfactory. The structure-based group (14) American Institute of Chemical Engineers, DIPPR Project 801Pure Component Data in DIPPRO, public version, January 31, 1996. contribution technique proves to be a powerful tool for predicting the (15) The American Petroleum Institute and EPCON international, API flammability characteristics of pure hydrocarbon liquids. The clear Technical Database V2.1.1, 2000. advantage of the method is its ability to estimate the FPT, AIT, UFL, (16) Neurock, M; Nigam, A.; Trauth, D.; Klein, M.T, Chemical Engineering and LFL of a hydrocarbon substance provided that the chemical Science, 1994, 49(24A), 4153-4177. structure is known. Another advantage is the ability of the method to Prepr. Pap.-Am. Chem. Soc., Div. Fuel Chem. 2003, 48(2), 684