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Mathematical method for polymerised styrene butadiene rubber 1502 pyrolysis residue and gasoline differentiation

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Abstract

During fire investigation, gasoline, as a common accelerant, is produced by petroleum cracking. However, the pyrolysis residues of other petrochemicals may interfere with gasoline identification. Polymerised styrene butadiene rubber (SBR) 1502 has combustion characteristics highly consistent with those of gasoline, thus having a great effect on gasoline identification. This study investigated the pyrolysis process of SBR 1502 by using thermogravimetric–differential scanning calorimetry (TG–DSC) and gas chromatography–mass spectrometry (GC–MS) to examine the residues during combustion stages, including pyrolysis and nonpyrolysis stages. The results indicated that 2,3-dimethylnaphthalene in the pyrolysis residues of SBR 1502 in pyrolysis stages 2 and 3 was lacked. However, when SBR 1502 only undergoes the first pyrolysis stage or even earlier (Nonpyrolysis stage), the characteristic components in the residue are similar to gasoline. In addition, mathematical methods were applied to analyse relevance and differences between SBR 1502 and gasoline. The conclusion was that the Pearson product-moment correlation was > 0.990, which may interfere with the identification, and principal component analysis could efficiently distinguish them. The current results can provide an accurate and feasible basis for fire investigation.

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Abbreviations

\( F_{\text{i}} \) :

Correction factor

\( n \) :

Number of variables

\( r_{\text{xy}} \) :

Correlation coefficient of PPMC

\( S_{\text{wi}} \) :

Percentage of components (%)

\( S_{\text{i}} \) :

Peak area of component

T 1 :

Initial pyrolysis temperature (°C)

T 2 :

Pyrogenic decomposition temperature (°C)

T 3 :

Rapid pyrolysis temperature (°C)

T 4 :

Burnout temperature (°C)

X :

Variables

\( x \) :

Need standardised value

\( \bar{x} \) :

Average of X variables

\( x_{\text{n}} \) :

The nth variable of the X variable

\( x_{\text{i}} \) :

Sample value of X

Y :

Variables

\( \bar{y} \) :

Average of Y variables

\( y_{\text{n}} \) :

The nth variable of the Y variable

\( y_{\text{i}} \) :

Sample value of Y

\( z \) :

Standardised value

\( \mu \) :

Average value

\( \sigma \) :

Standard deviation

\( \lambda \) :

Calculated feature values

\( \gamma \) :

Feature vectors

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Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2018YFC0807900), National Natural Science Foundations of China (No. 5177-4232), Innovation Capability Support Project of Shaanxi Province (2018PT-33), and Higher Education Teaching Reform Research Project of Shaanxi Province (19BY062).

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Authors and Affiliations

  1. School of Safety Science and Engineering, Xi’an University of Science and Technology, 58, Yanta Mid. Rd, Xi’an, 710054, Shaanxi, People’s Republic of China

    Jun Deng, Hui-Fei Lü, Yang Li, Wei-Feng Wang, Yang Xiao & Lei Bai

  2. Shaanxi Key Laboratory of Prevention and Control of Coal Fire, XUST, Xi’an, 710054, People’s Republic of China

    Jun Deng, Hui-Fei Lü, Yang Li, Wei-Feng Wang, Yang Xiao & Lei Bai

  3. Department of Fire Protection Engineering, China People’s Police University, 220, Xichang Rd, Langfang, 065000, Hebei, People’s Republic of China

    Yang Li

  4. Department of Safety, Health, and Environmental Engineering, National Yunlin University of Science and Technology, 123, University Rd., Sec. 3, Douliou, 64002, Yunlin, Taiwan, ROC

    Chi-Min Shu

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  1. Jun Deng
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Deng, J., Lü, HF., Li, Y. et al. Mathematical method for polymerised styrene butadiene rubber 1502 pyrolysis residue and gasoline differentiation. J Therm Anal Calorim 142, 685–694 (2020). https://doi.org/10.1007/s10973-020-10014-4

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