Application of methods of forecasting fire hazard properties of refined petroleum products on the basis of molecular descriptors for the justification of temperature class of explosion-proof electrical equipment

The article raises the topical issue - the lack of physical and chemical properties of new synthesized substances. These properties will allow supervisors to develop fire safety systems at security facilities. The efficiency of such systems is achieved by eliminating the combustible environment or the ignition source. Using the example of esters of butyric acid, which are used practically in all areas of industry and produced according to reference data in the amount of more than several tens of millions of tons per year, it was possible to predict one of the most important fire hazard properties of a substance - the self-ignition temperature, using the technique for predicting the fire hazard properties of refined products based on molecular descriptors and artificial neural networks. The proposed methodology is implemented with the help of the author’s computer program “NeuroPacket KDS 1.0”. The program “NeuroPacket KDS 1.0” allows you to: download and view databases containing chemical compound structures and their properties; to correlate the input data; statistically evaluate the resulting models; use the obtained neuronet models to predict the properties of substances without conducting a complex experiment. This approach to predicting the fire hazard property of oil refining products describes the structure of the molecule with the help of molecular descriptors and establishes quantitative correlations between the values found using artificial neural networks. Based on some reference data, data was verified. Analysis of the results obtained showed that the average relative error does not exceed 3 %, which is a good indicator. In addition, the autoignition temperature of esters of butyric acid was predicted, information on which is absent in the reference and regulatory literature. This makes it possible to build on the values obtained in the development of fire safety systems. Based on the obtained results on the self-ignition temperature of the substance, there were determined the temperature classes of the explosion-proof electrical equipment, which, on the whole, ensures fulfillment of item 4, article 50 of the “Technical Regulations on Fire Safety Requirements” (Federal Law No. 123). It is also worth noting that the methodology for predicting the fire-hazardous properties of oil refining products based on the use of molecular descriptors and artificial neural networks allows us to conclude that this technique can be used to predict other fire-hazardous properties of organic substances.

прогнозирование, молекулярные дескрипторы, искусственные нейронные сети, эфиры, оборудование, prediction, molecular descriptors, artificial neural network, ethers, equipment

1. Ngoc Lan Mai, Yoon-Mo Koo. Quantitative prediction of lipase reaction in ionic liquids by QSAR using COSMO-RS molecular descriptors // Biochemical Engineering Journal. - 2014. - Vol. 87. - P. 33-40. DOI: 10.1016/j.bej.2014.03.010.

2. Правила устройства электроустановок (ПУЭ).-6-е изд.-СПб. : Изд-во ДЕАН, 2003.-928 с.

3. Varnek A., Fourches D., Hoonakker F., Solov’ev V. P. Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures // Journal of Computer-Aided Molecular Design. -2005.-Vol. 19, No. 9-10.-P. 693-703. DOI: 10.1007/s10822-005-9008-0.

4. Королев Д. С., Калач А. В., Каргашилов Д. В. Прогнозирование температуры вспышки с помощью нейропакета КДС 1.0 на примере сложных эфиров масляной кислоты // Пожаровзрывобезопасность. -2016.-Т. 25, № 3. -С. 21-26. DOI: 10.18322/PVB.2016.25.03.21-26.

5. Королев Д. С., Калач А. В., Сорокина Ю. Н. Сравнительный анализ способов прогнозирования физико-химических свойств веществ // Вестник Командно-инженерного института МЧС Республики Беларусь.-2016.-№ 1(23).-С. 78-84.

6. Королев Д. С., Калач А. В. Категорирование помещений на основе дескрипторов и метода нейронных сетей // Вестник Белгородского государственного технологического университета им. В. Г. Шухова. -2015.-№ 5. -С. 210-213.

7. Калач А. В., Карташова Т. В., Сорокина Ю. Н., Облиенко М. В. Прогнозирование пожароопасных свойств органических соединений с применением дескрипторов // Пожарная безопасность. -2013. -№ 1. -С. 70-73.

8. Свидетельство о государственной регистрации программы для ЭВМ№2016614070. Нейропакет КДС 1.0 / Королев Д. С., Калач А. В., Каргашилов Д. В., Жучков А. В.; правообладатель ФГБОУ ВО Воронежский институт ГПС МЧС России. - № 2016611455; заявл. 24.02.2016; опубл. 20.05.2016.

9. Корольченко А. Я., Корольченко Д. А. Пожаровзрывоопасность веществ и материалов и средства их тушения : справочник.-В2ч.-2-еизд., перераб. и доп.-М. : Пожнаука, 2004.-Ч. I.-713 с.

10. Корольченко А. Я., Корольченко Д. А. Пожаровзрывоопасность веществ и материалов и средства их тушения : справочник.-В2ч.-2-еизд., перераб. и доп.-М. : Пожнаука, 2004.-Ч. II.-774 с.

11. Алексеев С. Г., Смирнов В. В., Алексеев К. С., Барбин Н. М. Температура вспышки. Часть III. Методы расчета через температуру кипения // Пожаровзрывобезопасность.-2014.-Т. 23,№ 3. -С. 30-43.

12. Baskin I., Varnek A. Building a chemical space based on fragment descriptors // Combinatorial Chemistry and High Throughput Screening. - 2008. - Vol. 11, No. 8. - P. 661-668. DOI: 10.2174/138620708785739907.

13. Алексеев С. Г., Алексеев К. С., Смирнов В. В., Барбин Н. М. Температура вспышки. Часть IV. Дескрипторный метод расчета // Пожаровзрывобезопасность.-2014.-Т. 23,№ 5.-С. 18-37.