Construction of an algorithm for building regions of questionable decisions for devices containing gases in a linear multidimensional space of hazardous factors

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This paper reports a procedure devised to forecast the level of chemical pollution of the atmosphere, which includes a mathematical model for the distribution of the concentration of dangerous gas in the atmosphere at its active deposition by dispersed jets of liquid, as well as a technique for its implementation. Based on the differential equations of gas distribution in space, a phased model of the propagation of a cloud of a dangerous chemical substance was built. The model describes stages in the discharge of a dangerous gaseous substance from emergency technological equipment, the deposition of dangerous gas by a finely-dispersed flow, and free propagation of the cloud in the air. The reported mathematical model makes it possible to calculate the size of pollution zones while determining the boundary safety conditions. When forecasting, the main meteorological parameters, the width of the deposition zone, and the chemical properties of both the gas and liquid are taken into consideration. The comparative analysis of the results of forecasting a conditional zone of chemical damage with the free propagation of the cloud, and at the active deposition by precipitation or technical devices, was carried out. The simulation results revealed that with an increase in the wind speed from 1 m/s to 5 m/s, the size of the affected area increases by 2.7 times, while the concentration of dangerous gas in the cloud falls by 2.5‒3 times. An algorithm has been proposed for integrating the devised methodology of forecasting the level of chemical pollution of the atmosphere into a general cycle of emergency management. It should be especially noted that the devised procedure contains the entire range of components that are necessary for its practical application. It includes a description of the procedure and practical recommendations for the use of the proposed technique in the elimination of emergencies, as well as a list of probable events when the use of the developed procedure would be most effective.

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Devising a procedure to forecast the level of chemical damage to the atmosphere during active deposition of dangerous gases

Melnychenko

Kustov

Basmanov

et al. 2022

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“…Under the conditions of an enterprise where periodic or continuous production schemes are in place, a significant volume of products is manufactured with continuous quality control over products. Therefore, it is important to ensure the smooth operation of equipment, to avoid defects, to rationally utilize material and energy resources, to reduce the cost of products, to manage production safety [6]. All these factors affect the quality and economic feasibility of production.…”

Section: The Aim and Objectives Of The Studymentioning

confidence: 99%

Establishing rational conditions for obtaining potassium glycerate

Sytnik¹,

Kunitsіa

Mazaeva³

et al. 2021

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This paper reports a study into the dependence of efficient glycerate potassium production involving glycerin and potassium hydroxide solution on the process conditions. The concentration of potassium glycerate in the resulting product has been used as a parameter of the efficient glycerate potassium production process. Glycerates of metals are applied to produce articles in the construction industry, electronics, medicine; they are employed as transesterification catalysts to obtain special fats for various purposes, as well as biodiesel fuel. In order to derive potassium glycerate, heating was applied while agitating a mixture of glycerin and potassium hydroxide. The analysis of potassium hydroxide was performed, in which the basic substance mass fraction was 85.5 %, the mass fraction of carbonate potassium – 0.9 %. The p.a.-grade glycerin was applied in this work, whose basic substance mass fraction was 99.5 %. The effect of the heating temperature of the reaction mixture on the concentration of potassium glycerate in the product has been determined. It has been shown that the rational heating temperature is 145 °C. The dependence of potassium glycerate concentration in the resulting product on the following conditions of the process has been established: a change in the molar concentration of glycerin and a reaction mixture heating duration. Such rational conditions for obtaining potassium glycerate have been defined as a molar concentration of glycerin of 60 % and a heating duration of 4 hours. The experimentally established concentration of potassium glycerate in the product under these conditions was 75.77 %. For potassium glycerate, the melting point (69 °C) and the mass fraction of moisture (0.8 %) have been determined. The results of experimental studies would make it possible to obtain potassium glycerate directly at enterprises where the glycerates of metals are used, from available raw materials, under rational conditions. The defined conditions for obtaining potassium glycerate could make it possible to efficiently utilize material and energy resources.

“…engineering [3], and chemically hazardous production [4,5]. Disruption of the normal functioning of these facilities can threaten vital national interests [6][7][8][9]. In this regard, it is necessary to develop artificial intelligence systems and improve methods of their implementation in unmanned aerial vehicle complexes (UAVCs) [10].…”

Section: The Aim and Objectives Of The Studymentioning

confidence: 99%

Improvement of the model of object recognition in aero photographs using deep convolutional neural networks

Slyusar

Protsenko

Chernukha

et al. 2021

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Detection and recognition of objects in images is the main problem to be solved by computer vision systems. As part of solving this problem, the model of object recognition in aerial photographs taken from unmanned aerial vehicles has been improved. A study of object recognition in aerial photographs using deep convolutional neural networks has been carried out. Analysis of possible implementations showed that the AlexNet 2012 model (Canada) trained on the ImageNet image set (China) is most suitable for this problem solution. This model was used as a basic one. The object recognition error for this model with the use of the ImageNet test set of images amounted to 15 %. To solve the problem of improving the effectiveness of object recognition in aerial photographs for 10 classes of images, the final fully connected layer was modified by rejection from 1,000 to 10 neurons and additional two-stage training of the resulting model. Additional training was carried out with a set of images prepared from aerial photographs at stage 1 and with a set of VisDrone 2021 (China) images at stage 2. Optimal training parameters were selected: speed (step) (0.0001), number of epochs (100). As a result, a new model under the proposed name of AlexVisDrone was obtained. The effectiveness of the proposed model was checked with a test set of 100 images for each class (the total number of classes was 10). Accuracy and sensitivity were chosen as the main indicators of the model effectiveness. As a result, an increase in recognition accuracy from 7 % (for images from aerial photographs) to 9 % (for the VisDrone 2021 set) was obtained which has indicated that the choice of neural network architecture and training parameters was correct. The use of the proposed model makes it possible to automate the process of object recognition in aerial photographs. In the future, it is advisable to use this model at ground stations of unmanned aerial vehicle complex control when processing aerial photographs taken from unmanned aerial vehicles, in robotic systems, in video surveillance complexes and when designing unmanned vehicle systems