The article presents an original solution to the problem of fire hazard indicators of oil products determining using molecular descriptors and artificial neural networks. The modelling of the forecasting process was carried out on the basis of a neural network training algorithm with a teacher and an error back propagation algorithm. The resulting model makes it possible to determine the fire hazard properties of substances in real time without time span and other costs.
Within the framework of the study, the process of evaporation of heated high-temperature oil products was studied, the results were used to determine the calculated values of the mass of vapors formed during the evaporation of spills of liquids heated above the design temperature. The result of the study was the development of a theoretical model based on the Stefan formula describing the rate of evaporation. This made it possible to establish that when heating high-temperature organic compounds form explosive vapor-air mixtures that pose a fire hazard.
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