Mathematical Modeling of Fire-Proof Efficiency of Coatings Based on Silicate Composition

As a result of alkaline neutralization of oils, a significant amount of soapstock is formed, the utilization of which creates an environmental and economic problem. The production of fatty acids from soapstock using sulfuric acid decomposition is investigated in this work. The peculiarity of the work is the determination of regression dependences of the yield and neutralization number of fatty acids on the soapstock processing conditions: temperature and duration. Soapstock obtained after neutralization of sunflower oil was used as raw material. Soapstock indicators: mass fraction of moisture – 15.4 %, total fat – 71.9 %, fatty acids – 64.5 %, neutral fat – 7.4 %. Rational conditions of soapstock processing are determined: temperature (90–95) °С, duration 40 min. Under these conditions, the fatty acid yield is 79.0 %, the neutralization number is 180.0 mg KOH/g. Quality indicators of the obtained fatty acids: mass fraction of moisture and volatile substances – 1.8 %, mass fraction of total fat – 97.0 %, cleavage depth – 64.5 % of oleic acid, the presence of mineral acids – no. Fatty acids correspond to fatty acids of the first grade according to DSTU 4860 (CAS 61788-66-7). An increase in the temperature and duration of soapstock contact with sulfuric acid increases the yield and neutralization number of fatty acids. This is due to a decrease in the viscosity of the reaction medium, an increase in the depth of cleavage of soapstock soaps with sulfuric acid, an increase in the intensity and duration of mass transfer. The developed rational conditions allow obtaining fatty acids from soapstock, which correspond in composition to fatty acids from refined deodorized sunflower oil. The results allow solving a number of economic and environmental problems associated with soapstock utilization and can be implemented in oil refineries and fatty acid production

Section: Identification Of the Dependence Of The Yield And Neutralization Number Of Fatty Acids On The Soapstock Processing Conditionsmentioning

confidence: 99%

Rational conditions of fatty acids obtaining by soapstock treatment with sulfuric acid

Sytnik

Kunitsіa

Mazaeva

et al. 2021

“…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].…”

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

Self Cite

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

“…2. Convolutional -3) Conv2D: Entry block (filters=16, kernel_size= [3,3], strides= [1,1], padding='same', activa-tion='relu').…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The use of unmanned aerial vehicles (UAVs) makes it possible to accelerate the process of monitoring critical infrastructure objects [1]. Such facilities include industrial enterprises [2], energy plants [3], chemically hazardous industries [4], and other strategic objects [5]. Disruption of the functioning of these facilities can threaten the national inter-…”

Section: Introductionmentioning

confidence: 99%

Improving a neural network model for semantic segmentation of images of monitored objects in aerial photographs

Slyusar

Protsenko

Chernukha

et al. 2021

Self Cite

This paper considers a model of the neural network for semantically segmenting the images of monitored objects on aerial photographs. Unmanned aerial vehicles monitor objects by analyzing (processing) aerial photographs and video streams. The results of aerial photography are processed by the operator in a manual mode; however, there are objective difficulties associated with the operator's handling a large number of aerial photographs, which is why it is advisable to automate this process. Analysis of the models showed that to perform the task of semantic segmentation of images of monitored objects on aerial photographs, the U-Net model (Germany), which is a convolutional neural network, is most suitable as a basic model. This model has been improved by using a wavelet layer and the optimal values of the model training parameters: speed (step) ‒ 0.001, the number of epochs ‒ 60, the optimization algorithm ‒ Adam. The training was conducted by a set of segmented images acquired from aerial photographs (with a resolution of 6,000×4,000 pixels) by the Image Labeler software in the mathematical programming environment MATLAB R2020b (USA). As a result, a new model for semantically segmenting the images of monitored objects on aerial photographs with the proposed name U-NetWavelet was built. The effectiveness of the improved model was investigated using an example of processing 80 aerial photographs. The accuracy, sensitivity, and segmentation error were selected as the main indicators of the model's efficiency. The use of a modified wavelet layer has made it possible to adapt the size of an aerial photograph to the parameters of the input layer of the neural network, to improve the efficiency of image segmentation in aerial photographs; the application of a convolutional neural network has allowed this process to be automatic.