A Modified Group Contribution Method for Accurate Prediction of Flash

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ACCURATE PREDICTION OF FLASH POINTS OF PURE ORGANIC COMPOUNDS USING CHEMICAL STRUCTURE AND NORMAL BOILING POINT DATA Amin Alibakhshi, Hamidreza Mirshahvalad, and Sara Alibakhshi Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.5b02786 • Publication Date (Web): 23 Oct 2015 Downloaded from http://pubs.acs.org on October 26, 2015

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Industrial & Engineering Chemistry Research

ACCURATE PREDICTION OF FLASH POINTS OF PURE ORGANIC COMPOUNDS USING CHEMICAL STRUCTURE AND NORMAL BOILING POINT DATA . ℎ ℎ , ,∗, . ℎℎ , . ℎ ℎ 1 Young Researchers & Elite Club, Pharmaceutical Sciences Branch, Islamic Azad University, Tehran, Iran 2 Department of Chemical Engineering, Amirkabir University of Technology, Tehran, Iran 3 Department of Mechanical Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran 4 Young Researchers & Elite Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

Corresponding author: Amin Alibakhshi e-mail: [email protected]; [email protected] Telephone: (+98) 9107606545 Fax: (+98)2177151969

Abstract

Flash point is one of the most widely used properties in risk assessment and safe design of process industries. In the current work, we have presented a novel and accurate model to predict the flash points of pure organic compounds from diverse families. The proposed model is a linear correlation between flash point, normal boiling point and 42 pre-defined functional groups constituting the molecule. Evaluation of the model through a dataset of 1533 pure organic compounds shows an average absolute deviation, average absolute relative error and correlation coefficient of 5.83, 1.61 and 0.992, respectively. Comparing the results of present study with other works shows that the model proposed in this work is among the most accurate and reliable ones to date. 1. Introduction Flash point (FP) is one of the most important flammability characteristics of compounds for safe design of process industries. FP is defined as the lowest temperature at which vapor of a compound can make a combustible mixture with air. Through FP one can evaluate the possibility of a compound to be inflamed in the presence of an ignition source as well as the rate of flame spread after ignition which both are important factors from a fire safety viewpoint.1

1 ACS Paragon Plus Environment


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The lack of experimental data of FP for most newly introduced compounds in industry has driven development of many FP predictive models which can be generally categorized into empirical, group contribution and quantitative structure property relationship (QSPR) models. Empirical models contain correlations which predict FP by other physical properties that are more accessible or can be measured more easily; among which Normal Boiling Point (NBP) is the most widely used property for FP prediction. NBP is amongst the most readily available thermophysical properties. Besides, its ease of measurement and being highly correlated with FP are of other reasons of using NBP in FP predictive correlations. Empirical models proposed in the literature include quadratic,2, 3 exponential4 and other non-linear 5-7 correlations between FP and NBP as well as other correlations proposed to predict FP using NBP and liquid density,

8, 9

NBP and number of carbon atoms,10 NBP and enthalpy of vaporization ( ),11-13 and NBP, critical pressure ( ), critical temperature ( ), acentric factor (ω) and molecular weight ( ). 14 Second category contains models which are developed based on the Group Contribution Method (GCM). According to GCM, properties are calculated as a function of number and type of predefined functional groups constituting the compound. GCM in its simplest form which is called linear GCM henceforward offers the following linear equation to predict the property : = + ∑ "# # ,

(1)

where "# and # are respectively the number and contribution of functional group and is a constant. The linear contribution of functional groups has been previously applied in some works to predict FP.15-17 However, considering linear contribution often results in considerable errors and some authors have proposed non-linear contribution of functional groups mapped through Artificial Neural Networks (ANN). 15, 18-19 The third category is QSPR models in which molecular descriptors which are numerical quantities calculated from 2-D or 3-D structure of compounds are used in predictive correlations. The QSPR method has been previously applied to predict FP20-27 and NBP.

28-29

However, this

method is less popular than empirical and group contribution based models due to its lower accuracy (see table 3) and more sophisticated procedure which includes drawing chemical structure of compounds, energy minimization, calculating molecular descriptors and selecting the most effective ones among hundreds of available molecular descriptors. 2 ACS Paragon Plus Environment

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The aim of current work is to propose a simple and accurate model to predict FP for a large dataset of organic compounds. A combination of the molecular descriptor atom connectivity index (CI) and functional groups which is known as GC & method was previously applied by Hukkerikara et al., to predict various properties.30-31 In another work, it was shown that a combination of the square root of molecular weight and functional groups can improve prediction of NBP.32 In the current work and in a similar approach, normal boiling point (NBP) and contribution of 42 pre-defined functional groups are used to predict FPs of pure organic compounds accurately. 2. Method and results

2.1 Dataset Flash point and normal boiling point data of 1533 organic compounds from various chemical families were supplied from DIPPR 801 database 33. DIPPR 801 provides reliable and evaluated data of physical properties for large number of compounds. Analyzing the chemical structures of dataset compounds, number of presence for the 42 functional groups listed in table 1 were counted for each compound to be used as model inputs. The functional groups used in the current study are defined such that they can characterize the whole structure of all dataset compounds and are almost similar to those applied by Albahri

15

and Lazzús.18 2.2 Model development As discussed before, prediction of FP through linear GCM accompanied with high errors. To make a comparison, we evaluated the performance of this method for our dataset. Optimized values of and # of equation (1) were determined by multivariate linear regression. According to the results, constant was 211.89 and values of # are reported in table 1.

The accuracy of the predicted FPs was then evaluated using the statistical parameters Average Absolute Deviation (AAD), Average Absolute Relative Error (AARE) and the correlation coefficient (') which are defined as follows:



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)*+ +,); = < ∑=>(# − (# >@,

'A = '=

10 Relative error (%) 9-10

8-9 7- 8 6-7 5- 6

Proposed model equation (1)

4-5 3-4 2-3 1-2 0-1 0

200

400

Number of compounds Figure 2- Distribution of relative errors through dataset compounds


600

800

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Figure 3. Chemical structures of 2-Formylbenzoic acid and 4-Carboxybenzaldehyde



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Table 1- Functional groups and their contribution to FP and NBP

Functional Group

RS equation (1)

RS equation (5)

1

–CH

3.82

-2.97

2

–CH –

10.98

-1.14

3

>CH–

8.56

-1.64

4

>C
N–

18.96

0.91

21

═N–

23.35

1.93

22

–C≡N

69.63

5.84

23

–NO

66.15

7.41

24

–F

-7.85

2.84


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25

–Cl

24.12

7.33

26

–Br

42.73

7.66

27

–I

62.65

12.17

28

–SH

35.23

-1.77

29

–S–

37.64

-0.84

30

–CH – (ring)

10.13

-2.49

31

–HC
C
N–

(ring)

39.77

14.67

40

═N– (ring)

33.21

6.03

41

–S– (ring)

28.41

-0.6

42

-CO-O-CO- (anidride)

96.25

10.6

(ring)



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Table 2- Compounds for which absolute relative error of prediction of FP using equation (4) is greater than 10 % Compound

Predicted flash point (K)

Acetylsalicylic acid Hexamethylenetetramine Diolein Tartaric acid Ascorbic acid Acetoacetanilide Pimelic acid Trichloroacetaldehyde

Absolute relative error (K)

436.32 461.45 594.95 547.93 530.96 487.78 489.00 310.43


86.83 61.70 79.95 64.78 65.81 64.63 65.83 37.57

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Table 3- A comparison between the results of our models with other works Model

Method

No. data

AAD (k)

AARE (%)

Max. AARE (%)

R

Current work

Semi- empirical

1533

5.83

1.61

15.56

0.992

Albahri15

GCM+ANN

375

3.55

1.1

6.62

0.9961

Correlation ( +NBP)

1062

4.65

1.32

–

–

GCM+ANN+ PSO

505

6.2

1.8

8.6

–

Correlation ( +NBP)

600

6.36

1.84

–

–

Correlation (NBP, , , ω, )

1471

–

1.94

7.5

0.9935

QSPR

1472

7.2

–

–

0.991

Correlation

1471

8.1

2.4

–

0.9895

GCM+ANN

1378

8.1

–

26

0.9878

Correlation ( +NBP)

1062

9.68

2.84

–

–

Tetteh et al. 27

QSPR+ANN

400

9.59

–

–

Hukkerikar et al.31

GC &

512

10.66

3.27

–

0.89

QSPR

230

12

–

–

0.943

QSPR+ANN

758

12.6

–

–

0.989

Chen et. al. 23

QSPR

230

–

–

22.9

0.964

Hshieh 3

Correlation (NBP)

494

–

–

–

0.966

Bagheri et al. 20

QSPR

1651

19.31

5.94

–

0.94

Katritzky et al.26

QSPR

271

–

–

–

0.91

Patil2

Correlation (NBP)

593

–

–

7.5

0.90

Rowley et al.

11

Lazzús 18 Catoire &Naudet 13 14

Gharagheizi et al.

Gharagheizi et al.21 Gharagheizi et al.

10

Gharagheizi et al. 19 Rowley et al.

12

5

Mathieu

Katritzky et al.

25


–


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TOC Graphic:


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A Modified Group Contribution Method for Accurate Prediction of Flash

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