Fire detection is an important task in the field of safety and emergency prevention. In recent years, deep learning methods have shown high efficiency in solving various computer vision problems, including detecting objects in images. In this paper, monitoring wildfires was considered, which allows you to quickly respond to them and prevent their spread using deep learning methods. For the experiment, images from the satellite and images from the FireWatch sensor were taken as initial data. In this work, the deep learning algorithms you only look once (YOLO), convolutional neural network (CNN), and fast recurrent neural network (FastRNN) were considered, which makes it possible to determine the accuracy of a natural fire. As a result of the experiments, an automated fire recognition algorithm using YOLOv4 deep learning methods was created. It is expected that the results of the study will show that deep learning methods can be successfully applied to detect fire in images. This may lead to the development of automated monitoring systems capable of quickly and reliably detecting fire situations, which will help improve safety and reduce the risk of fires.

The aim of this work is to analyze and model the performance of the distillation column of a catalytic cracking plant using fuzzy initial information. A system of mathematical models of the studied columns was developed, and we discussed the issues of modeling the distillation column of a catalytic cracking plant operating in conditions of fuzzy initial information. The system of mathematical models of the studied columns was developed on the basis of statistical and fuzzy information. The mathematical models of columns K-1, K-2 and K-3 were identified with regression and fuzzy regression equations, i.e., combined models of the main columns of the catalytic cracking plant were constructed. The purpose of this study was to create an optimal control system for the distillation column of a catalytic cracking unit using a mathematical model of the process. The results obtained in this work can be helpful during the design, modernization and optimization of equipment and installations, especially those used in the chemical and petrochemical industries. They can be useful both from a scientific and practical point of view, and they are also significant in terms of environmental protection, economy and mechanical engineering.