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The article rises the current question about the need to develop a fire safety system of explosive objects (buildings, structures, buildings, industrial communications), as well as organizing assistance to local self-government bodies in the implementation of primary fire safety measures. The emphasis is placed on a fire prevention system, which includes a lightning protection system designed to protect the countryside and other objects of protection against atmospheric electricity discharges. Therefore, for proper design, control and maintenance of health, it is proposed to apply the original computer program. Which allows an express analysis of lightning protection parameters and make appropriate conclusions.

To improve the accuracy of the obtained values when predicting the fire hazardous properties of organic substances, it was decided to modify the existing algorithm, supplementing it with deep learning technology. Verification of the data was carried out based on some reference and practical data. It was found that the values obtained in the course of computer simulation provide a satisfactory error, which means that the data obtained can be used in practice.

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.

This article presents an algorithm and mathematical model for an identifying flaming combustion automated process by an unmanned aerial vehicle. A digital image processed by software filters serves as a resource about an accident and fire at oil and gas industry facilities. The algorithm deals with the aircraft height, the movement speed relative to the observation point, as well as relative to the surface background, the combustion source brightness and the investigated area flicker frequency. The generated mathematical model allows to detect a fire hazard by a drone without operator intervention.

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.

Providing comprehensive security of the pipeline is the main task of monitoring linear facilities in the oil and gas industry. Proposed mathematical model is a part of an automated identification algorithm to determine the fire load type. The study identified four groups of combustible substances characteristic of oil and gas facilities: oil, refined products, liquefied petroleum gas and vegetation. There was a study of electromagnetic radiation of the optical range, recorded by an UAV’S digital camera for each type of substance. An algorithm for comparing the area color with a standard was proposed and implemented. The obtained mathematical correspondence allows identifying the fire color and the fire load type in conditions of different luminosity. The information on the flaming substance allows authorized rescue units to make operational decisions about the fire extinguishing method.