NEURAL NETWORK SOLUTION OF INVERSE PROBLEM OF IDENTIFICATION OF LIGHT PETROLEUM PRODUCTS BY RAMAN SPECTROSCOPY FOR FIRE INVESTIGATION

Otgon F V; Dementiev F A; Ivanov A V

doi:doi:

Home / Journals / NATURAL AND MAN-MADE RISKS (PHYSICO-MATHEMATICAL AND APPLIED ASPECTS) / Volume 2018 Issue 1 / NEURAL NETWORK SOLUTION OF INVERSE PROBLEM OF IDENTIFICATION OF LIGHT PETROLEUM PRODUCTS BY RAMAN SPECTROSCOPY FOR FIRE INVESTIGATION

NEURAL NETWORK SOLUTION OF INVERSE PROBLEM OF IDENTIFICATION OF LIGHT PETROLEUM PRODUCTS BY RAMAN SPECTROSCOPY FOR FIRE INVESTIGATION

Submit manuscript Download PDF
Text

To cite

Citations:

NEURAL NETWORK SOLUTION OF INVERSE PROBLEM OF IDENTIFICATION OF LIGHT PETROLEUM PRODUCTS BY RAMAN SPECTROSCOPY FOR FIRE INVESTIGATION

Journal: NATURAL AND MAN-MADE RISKS (PHYSICO-MATHEMATICAL AND APPLIED ASPECTS) Volume 2018 № 1 , 2018

Rubrics: PHYSICAL AND MATHEMATICAL ASPECTS OF EMERGENCY RESPONSE

Otgon F V ¹

Dementiev F A ²

Ivanov A V ³

Author and publication information

Authors:

1. Saint-Petersburg university of State fire service of EMERCOM of Russia

2. Saint-Petersburg university of State fire service of EMERCOM of Russia

3. Saint-Petersburg university of State fire service of EMERCOM of Russia

Type:

Article

Pages:

from 15 to 22

Status:

Published

Received:

12.03.2018

Accepted:

20.03.2018

Published:

25.03.2018

Language:

Russian

Keywords:

light petroleum products, Raman spectrometry, neural network architecture, fire investigation

Abstract and keywords

Abstract (English):
Present results of research of the identification of light petroleum products by the Raman scattering method at different intervals for the purposes of fire investigation. A simulation method was used to evaluate results with the help of neural networks.

Keywords:
light petroleum products, Raman spectrometry, neural network architecture, fire investigation

Text

Text (PDF): Read Download

References

1. Gavrilov D.A., Gavrilova T.S., Preobrazhenskiy N.B. Ekspress-analiz: odnim vzglyadom // Nauka iz pervyh ruk. 2011. № 4 (40).

2. Krylov A.S., Vtyurin A.N., Gerasimova Yu.V. Obrabotka dannyh infrakrasnoy fur'e-spektroskopii: metod. posobie. Krasnoyarsk: Institut fiziki SO RAN, 2005.

3. Tarasevich B.N. Osnovy IK-spektroskopii s preobrazovaniem Fur'e. Podgotovka prob v IK-spektroskopii. M.: MGU, 2012.

4. Ahmadjian M., Brown C.W. Petroleum identification by laser Raman spectroscopy // Analytical Chemistry. 1976. T. 48. № 8. S. 1 257-1 259.

5. Vinarskiy V.A. Hromatografiya. Kurs lekciy v dvuh chastyah. Ch. 1: Gazovaya hromatografiya. Minsk: BGU, 2002.

6. Mironov V.L. Osnovy skaniruyuschey zondovoy mikroskopii. M.: Tehnosfera, 2009.

7. Gavrilov D.A. O provedenii analiza sostava vody v zone neftedobychi v real'nom masshtabe vremeni // Tehnologii tehnosfernoy bezopasnosti. 2012. № 2. S. 16-16.

8. Rand S.J. Significance of tests for petroleum products. ASTM International, 2003. T. 1.

9. Golovko V.A., Galushkin A.I. Neyronnye seti: obuchenie, organizaciya i primenenie // Neyrokomp'yutery i ih primenenie. 2001. Kn. 4.

10. Adaptivnoe postroenie ierarhicheskih neyrosetevyh klassifikatorov / S.A. Dolenko [i dr.] // Neyrokomp'yutery: razrabotka, primenenie. 2005. № 1-2. S. 4-11.

11. Speight J.G. Handbook of petroleum product analysis. John Wiley & Sons, 2015.

Submit manuscript Download PDF
Text

To cite

Citations:

Confirmation

Регистрация