Проанализирована базовая методика расчета категории помещений по взрывопожарной и пожарной опасности. Установлено, что она является ограниченной ввиду отсутствия в ней достоверных справочных данных, в частности по максимальному давлению взрыва вещества, что влияет на конечный результат избыточного давления. Принято решение усовершенствовать ее путем внедрения методики прогнозирования пожароопасных свойств продуктов нефтепереработки на основе молекулярных дескрипторов и искусственных нейронных сетей. В качестве верификации спрогнозировано максимальное давление взрыва для ряда кислородсодержащих производных углеводородов и проведен расчет категории с учетом полученных значений. Установлено, что вновь полученные значения избыточного давления ниже заявленных, а следовательно, финансовые затраты на разработку системы обеспечения пожарной безопасности также будут снижены.
The article raises the urgent issue - the lack of physicochemical properties of the new synthesized substances. These properties will allow employees of supervisory activities 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 oxygen-containing hydrocarbons, which are used practically in all areas of industry and are produced according to reference data in the amount of more than several tens of millions of tons per year, it was possible to predict the maximum explosion pressure, i. e. one of the most important fire hazard properties of a substance, with the help of a technique for predicting the fire hazard properties of oil refining products based on molecular descriptors and artificial neural networks. The proposed methodology is implemented using the author’s computer program “NeuroPacket KDS 1.0”. The program “NeuroPacket KDS 1.0” allows you to: download and view databases containing the tructures of chemical compounds and their properties; to correlate the input data; to evaluate the received models statistically; 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 refined 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. In addition, the maximum explosion pressure was predicted for substances that are not known 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 results obtained, the category of premises for explosion and fire hazard was calculated. It was found that the estimated value of excess pressure was less than the claimed value, and therefore the financial costs of developing a fire safety system would also be reduced. It should be noted 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.
The authors declare that there are no conflicts of interest present.