알코올화합물의 최소자연발화점 예측에 관한 연구

하동명□, 최용찬*

세명대학교 안전공학과, 세명대학교 대학원 환경안전시스템공학과*

A Study on Prediction of Minimum Autoignition Temperature

for Alcohols

Dong-Myeong Ha and Yong-Chan Choi*

Dept. of Safety Eng., Semyung Univ., Jecheon 390-711, Korea

*Dept. of Environmental Safety System Eng., Graduate School,

Semyung Univ., 390-711 Jecheon, Korea*

1.  Introduction

Flammable substances are frequently used chemical industry processes. The fire and explosion properties necessary for safe storage, transport, process design and operation of handling  flammable substances are lower flammability limits(LFL), upper flammability limits(UFL), flash point, AIT( minimum autoignition temperature or spontaneous ignition temperature), minimum ignition energy, minimum oxygen concentration[1]. An accurate knowledge of the minimum auto ignition temperature is important in developing appropriate prevention and control measures in industrial fire protection.

Autoignition, or  spontaneous ignition, is of practical interest not only in relation to hazard but also in combustion system such as engine, turbine, reactor etc.. Also, autoignition is a phenomena of particular interest to the chemical engineer concerned with safe operation, it involves the occurrence of combustion in the absence of an external ignition sources.

Autoignition temperature describes the minimum temperature to which a substance must be heated, without the application of a flame or spark, which will cause that substance to ignite. The lower the ignition temperature, the greater the potential for a fire started by typical laboratory equipment. For the smaller hydrocarbons the autoignition temperature is inversely related to molecular weight, but it also increases with carbon chain branching.

The measurement AIT is dependent upon many factors, namely initial temperature, pressure, volume, fuel/air stoichiometry, catalyst material, concentration of vapor, ignition delay.

The objective of this study is to derive empirical equation to estimate the AIT of alcohol compounds from the normal boiling points and molecular weights.

2. Development of the predictive models

2.1 Selection of AIT reported data

In this study, the AIT and the normal boiling points base alcohol compounds for which literature values are obtained from Yogyu data[2] among the several literatures[2,3,4,5]. The data base contained 29 alcohol compounds for which reported the AIT are available.

Table 1 summarizes the several reported AIT data, normal boiling points, flash points and molecular weights for 29 alcohol compounds by means of several references.

2.2 Various possible prediction models

Multiple regression analysis can be looked upon as an extension of straight-line regression analysis(which involves only one independent variable) to the situation where there is more than one independent variable to be considered[6].

Multiple regression analysis is applied to obtain the correlation for estimation of the AIT by using the normal boiling points and the molecular weights.

We want to determine the best mathematical model for describing the relationship between the AIT and the normal boiling points or the molecular weights.

For the estimation of the AIT, various possible prediction models are as fellows :

[eqn: T_AIT = a + bT_b + c(MW~) +d LEFT ( 1 over T_b RIGHT )]                            (1)

[eqn: T_AIT = a + bT_b + c(MW~)]                                     (2)

[eqn: T_AIT = a + bT_b + c(MW~) +d LEFT ( 1 over T_b RIGHT ) +e LEFT ( 1 over MW RIGHT )]                  (3)

[eqn: T_AIT = a + b ln(T_b ) + c ln(T_b )^2]                                 (4)

[eqn: 1 over T_AIT = a + bT_b]                                              (5)

[eqn: 1 over T_AIT = a + bT_b + cT_b ^2]                                        (6)

[eqn: 1 over T_AIT = a + bT_b + cT_b ^2 +d (MW~)]                              (7)

[eqn: 1 over T_AIT = a + bT_b + cT_b ^2 +d (MW~)+e (MW~)^2]                   (8)

where [eqn: T_AIT] is the minimum auto ignition temperature, [eqn: T_b] is the normal boiling points and [eqn: MW] is the molecular weight.

The regression coefficients of these equations are obtained from the least square method. The method has been used occasionally to seek an optimum condition or for optimization.

Table 1.  Summary the several reported AIT data, normal boiling points,

flash points and molecular weights for alcohols

No.	Alcohols	MW	Tb Yagyu	Tf NFPA	Tf Sigma	TA Yagyu [2]	TA NFPA [3]	TA Sigma [4]	TA Hilado [5]
1	Methyl Alcohol	32	65	11	11	440	464	385	386
2	Ethyl Alcohol	46	78	13	9	400	363	362	363
3	n-Propyl Alcohol	60	97	23	15	400	412	440	432
4	iso-Propyl Alcohol	60	82	12	16	430	399	460	-
5	n-Butyl Alcohol	74	117	37	35	340	343	343	359
6	iso-Butyl Alcohol	74	108	37	27	405	343	405	414
7	sec-Butyl Alcohol	74	100	24	27	395	405	405	-
8	tert-Butyl Alcohol	74	82	11	5	460	478	480	478
9	n-Amyl Alcohol	88	138	33	49	300	300	300	300
10	iso-Amyl Alcohol	88	132	43	43	340	350	385	-
11	sec-Amyl Alcohol	88	120	34	34	330	343	347	-
12	tert-Amyl Alcohol	88	102	19	21	410	437	437	437
13	Neopentyl Alcohol	88	113	37	37	420	-	-	-
14	Diethyl carbinal	88	116	34	41	365	343	-	-
15	n-Hexyl Alcohol	102	158	63	60	285	-	293	-
16	Methyl-iso-Buty1carbinol	102	132	41	41	340	-	-	-
17	2-Metyl-2-Pentanol	102	121	-	21	415	-	-	-
18	n-Heptyl Alcohol	116	177	-	74	275	-	350	-
19	4-Heptanol	116	155	-	49	295	-	-	-
20	Di-iso-Propyl carbinol	116	140	49	37	395	-	-	-
21	n-Octyl Alcohol	130	195	81	81	260	-	273	-
22	sec-Octyl Alcohol	130	180	88	71	265	-	-	-
24	2 - Ethylhexyl Alcohol	130	183	73	77	270	231	288	-
24	n-Nonyl Alcohol	144	213	-	76	260	-	-	-
25	n-Decyl Alcohol	158	233	82	82	250	288	129	-
26	Lauryl Alcohol	186	257	127	-	250	275	-	-
27	Myrityl Alcohol	214	291	-	-	240	-	-	-
28	Cetyl Alcohol	242	344	-	-	245	-	-	-
29	Stearyl Alcohol	270	350	-	-	245	450	-	-

2.3 Optimum seeking of the predictive model

Optimization in selecting the predictive models of the functional relationship  between the AIT and the normal boiling points or the molecular weights are attempted.

Prior selection was made based on an A.A.P.E.(average absolute percent errors) and A.A.D.(average absolute deviations), namely the difference of the reported value [eqn: T_exp.] and estimated value[eqn: T_est.][6].

[eqn: A.A.P.E.={sum left□{T■_est. -T_exp.} over {T_exp.}right□} over N}□100]                     (9)

[eqn: A.A.D.={sum □{T_est. -T_exp.} □} over N}]                              (10)

[eqn: T_est] of Eqn. (9) is estimated AIT, [eqn: T_exp.]  of Eqn. (9) is reported AIT and [eqn: N]  of Eqn. (10) is the data numbers.

3. Comparison with  reported and estimated values

Statistical analysis of these 29 compounds resulted in best-fit empirical equation for predicting the AIT of alcohol compounds.

The best- fit empirical equation is as fellows :

[eqn: &1 over T_AIT = 6.5388 TIMES 10^-4 + 3.9655 TIMES 10^-5 T_b -5.7138 TIMES 10^-8 T_b ^2 # &~~~~~~~~~-2.3730 TIMES 10^-5 (MW~)+ 4.0647 TIMES 10^ -8 (MW~)^2]      (11)

Equation (11) is in agreement with the predicted AIT values of 29 alcohol compounds, average absolute percent error is 3.96 %, average absolute deviation is 14.59□.

4. Conclusions

1) The relationship  between the minimum autoignition temperature and             the normal boiling points or the molecular weights was found.

2) For better prediction of the minimum autoignition temperature of                  alcohol compounds, it is suggested to use the following equation :

[eqn: &1 over T_AIT = 6.5388 TIMES 10^-4 + 3.9655 TIMES 10^-5 T_b -5.7138 TIMES 10^-8 T_b ^2 # &~~~~~~~~~-2.3730 TIMES 10^-5 (MW~)+ 4.0647 TIMES 10^ -8 (MW~)^2]

References

1. Lee, S.K. and Ha, D.M.: □Newest Chemical Engineering Safety

Engineering□, Donghwagisul Press, Seoul(1997).

2. Yagyu, S.: Research Report of The Research Institute of Industrial

Safety, RR-26-5(1978).

3. NFPA: "Fire Hazard Properties of Flammable Liquid, Gases, and

Volatile solids", NFPA 325M, NFPA(1991).

4. Lenga, R.E. and Votoupal, K.L.: □The Sigma Aldrich Library of

Regulatory and Safety Data, Volume Ⅰ∼Ⅲ□, Sigma Chemical

Company and Aldrich Chemical Company Inc. (1993).

5. Hilado, C.J. and Clark, S.W.: Chemical Engineering, Vol. 4, 75(1972).

6. Ha, D.M. : J. of the Korean Institute for Industrial Safety, 15(3), 71(2000).