PREDICTING THE FIRE EXTINGUISHING TIME OF EXPLOSIVE COMPONENTS USING FIRE EXTINGUISHING HYDROGELS

Author:

Vorontsov Taras1ORCID,Ivanov Aleksey2

Affiliation:

1. Ivanovo fire and rescue academy of State fire service of EMERCOM of Russia

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

Abstract

. Using methods of regression analysis and neural network research, it was possible to preserve the physical properties of water-gene compositions and ensure a minimum time for extinguishing a fire in a model fire with components of industrial explosives. Neural network modeling was performed in the STATISTICA Application 10 program. The maximum discrepancy between the results of the neural network model and experimental data is 0,18 %. Regression analysis was performed in the REGRAN program. The maximum error in target results was 4,4 %. Analysis of experimental data and mathematical modeling results showed that the most significant properties of fire extinguishing agents based on hydrogels, providing minimal extinguishing time, are density and surface tension. The concentrations of the gelling agent were determined at which the water-gel composition acquires optimal physical properties for extinguishing a model outbreak of a component of an industrial explosive. Recommendations have been developed for creating hydrogel formulations with desired properties.

Publisher

St. Petersburg University of the State Fire Service of EMERCOM of Russia

Reference16 articles.

1. Бучельников Д.Ю., Бучельников С.Ю. Тушение пожаров на объектах с наличием взрывчатых веществ и материалов: учеб.-метод. пособие. Екатеринбург: филиал Акад. ГПС МЧС России, 2002. 64 с., Buchel'nikov D.Yu., Buchel'nikov S.Yu. Tushenie pozharov na ob"ektah s nalichiem vzryvchatyh veshchestv i materialov: ucheb.-metod. posobie. Ekaterinburg: filial Akad. GPS MCHS Rossii, 2002. 64 s.

2. Власов Д.А. Взрыв и его последствия: учеб. пособие. СПб.: С.-Петерб. гос. технол. ин-т (техн. ун-т), 2001. 151 с., Vlasov D.A. Vzryv i ego posledstviya: ucheb. posobie. SPb.: S.-Peterb. gos. tekhnol. in-t (tekhn. un-t), 2001. 151 s.

3. High-power acoustic fire extinguisher with artificial intelligence platform / J. Wilk-Jakubowski [et al.] // International Journal of Computational Vision and Robotics. 2022. Vol. 12. № 3. P. 236–249., High-power acoustic fire extinguisher with artificial intelligence platform / J. Wilk-Jakubowski [et al.] // International Journal of Computational Vision and Robotics. 2022. Vol. 12. № 3. P. 236–249.

4. Use of fire-extinguishing balls for a conceptual system of drone-assisted wildfire fighting / B. Aydin [et al.] // Drones. 2019. Vol. 3. № 1. С. 17., Use of fire-extinguishing balls for a conceptual system of drone-assisted wildfire fighting / B. Aydin [et al.] // Drones. 2019. Vol. 3. № 1. S. 17.

5. Воронцов Т.С., Иванов А.В. Исследование физико-химических свойств водногелевых огнетушащих составов в условиях ликвидации горения промышленных взрывчатых веществ и их компонентов // Современные проблемы гражданской защиты. 2022. № 2 (43). С. 50–58., Voroncov T.S., Ivanov A.V. Issledovanie fiziko-himicheskih svojstv vodnogelevyh ognetushashchih sostavov v usloviyah likvidacii goreniya promyshlennyh vzryvchatyh veshchestv i ih komponentov // Sovremennye problemy grazhdanskoj zashchity. 2022. № 2 (43). S. 50–58.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3