Mathematical Modeling of Induction Heating of Waveguide Path Assemblies during Induction Soldering

The waveguides used in spacecraft antenna feeders are often assembled using external couplers or flanges subject to further welding or soldering. Making permanent joints by means of induction heating has proven to be the best solution in this context. However, several physical phenomena observed in the heating zone complicate any effort to control the process of making a permanent joint by induction heating; these phenomena include flux evaporation and changes in the emissivity of the material. These processes make it difficult to measure the temperature of the heating zone by means of contactless temperature sensors. Meanwhile, contact sensors are not an option due to the high requirements regarding surface quality. Besides, such sensors take a large amount of time and human involvement to install. Thus, it is a relevant undertaking to develop mathematical models for each waveguide assembly component as well as for the entire waveguide assembly. The proposed mathematical models have been tested by experiments in kind, which have shown a great degree of consistency between model-derived estimates and experimental data. The paper also shows how to use the proposed models to test and calibrate the process of making an aluminum-alloy rectangular tube flange waveguide by induction soldering. The Russian software, SimInTech, was used in this research as the modeling environment. The approach proposed herein can significantly lower the labor and material costs of calibrating and testing the process of the induction soldering of waveguides, whether the goal is to adjust the existing process or to implement a new configuration that uses different dimensions or materials

Paired Patterns in Logical Analysis of Data for Decision Support in Recognition

Logical analysis of data (LAD), an approach to data analysis based on Boolean functions, combinatorics, and optimization, can be considered one of the methods of interpretable machine learning. A feature of LAD is that, among many patterns, different types of patterns can be identified, for example, prime, strong, spanned, and maximum. This paper proposes a decision-support approach to recognition by sharing different types of patterns to improve the quality of recognition in terms of accuracy, interpretability, and validity. An algorithm was developed to search for pairs of strong patterns (prime and spanned) with the same coverage as the training sample, having the smallest (for the prime pattern) and the largest (for the spanned pattern) number of conditions. The proposed approach leads to a decrease in the number of unrecognized observations (compared with the use of spanned patterns only) by 1.5–2 times (experimental results), to some reduction in recognition errors (compared with the use of prime patterns only) of approximately 1% (depending on the dataset) and makes it possible to assess in more detail the level of confidence of the recognition result due to a refined decision-making scheme that uses the information about the number and type of patterns covering the observation

Paired Patterns in Logical Analysis of Data for Decision Support in Recognition

Logical analysis of data (LAD), an approach to data analysis based on Boolean functions, combinatorics, and optimization, can be considered one of the methods of interpretable machine learning. A feature of LAD is that, among many patterns, different types of patterns can be identified, for example, prime, strong, spanned, and maximum. This paper proposes a decision-support approach to recognition by sharing different types of patterns to improve the quality of recognition in terms of accuracy, interpretability, and validity. An algorithm was developed to search for pairs of strong patterns (prime and spanned) with the same coverage as the training sample, having the smallest (for the prime pattern) and the largest (for the spanned pattern) number of conditions. The proposed approach leads to a decrease in the number of unrecognized observations (compared with the use of spanned patterns only) by 1.5–2 times (experimental results), to some reduction in recognition errors (compared with the use of prime patterns only) of approximately 1% (depending on the dataset) and makes it possible to assess in more detail the level of confidence of the recognition result due to a refined decision-making scheme that uses the information about the number and type of patterns covering the observation

Prediction of Critical Filling of a Storage Area Network by Machine Learning Methods

The introduction of digital technologies into the activities of companies is based on software and hardware systems, which must function reliably and without interruption. The forecasting of the completion of storage area networks (SAN) is an essential tool for ensuring the smooth operation of such systems. The aim of this study is to develop a system of the modelling and simulation of the further loading of SAN on previously observed load measurements. The system is based on machine learning applied to the load prediction problem. Its novelty relates to the method used for forming input attributes to solve the machine learning problem. The proposed method is based on the aggregation of data on observed loading measurements and the formalization of the problem in the form of a regression analysis problem. The artificial dataset, synthesized stochastically according to the given parameter intervals and simulating SAN behavior, allowed for more extensive experimentation. The most effective algorithm is CatBoost (gradient boosting on decision trees), which surpasses other regression analysis algorithms in terms of R2 scores and MAE. The selection of the most significant features allows for the simplification of the prediction model with virtually no loss of accuracy, thereby reducing the number of confessions used. The experiments show that the proposed prediction model is adequate to the situation under consideration and allows for the prediction of the SAN load for the planning period under review with an R2 value greater than 0.9. The model has been validated on a series of real data on SAN

Experimental Study of Oil Non-Condensable Gas Pyrolysis in a Stirred-Tank Reactor for Catalysis of Hydrogen and Hydrogen-Containing Mixtures Production

The present study is focused on improving the technology for deep oil sludge processing by pyrolysis methods, considered to be the most promising technology for their environmentally friendly utilization, in which a significant yield of fuel products is expected. The technology developed by the authors of this study is a two-stage process. The first stage, pyrolysis of oil sludge, was investigated in previous papers. A significant yield of non-condensable gases was obtained. This paper presents a study of the second stage of complex deep processing technology—pyrolysis of non-condensable gases (purified propane) using a stirrer with the help of the developed experimental setup. The expected benefit of using the stirrer is improved heat transfer due to circumferential and radial-axial circulation of the gas flow. The effect of a stirrer on the yield of final target decomposition products—H2-containing mixtures and H2 generated during non-catalytic (medium-temperature) and catalytic pyrolysis of non-condensable gases obtained by pyrolysis of oil sludge are estimated. Ni catalyst was used for catalytic pyrolysis. The study shows that the application of the stirrer leads to increasing in H2-containing mixtures and H2 concentrations. In particular, during the whole reaction time (10 h), the average H2 concentration in pyrolysis gas during catalytic pyrolysis increased by ~5.3%. In this case, the optimum reaction time to produce H2 was 4 h. The peak H2 concentration in the pyrolysis gas at reaction temperature 590 ± 10 °C was: 66.5 vol. % with the stirrer versus 62 vol. % without the stirrer with an error of ±0.4 %. A further increase in reaction time is cost-effective in order to obtain H2-containing mixtures

The Orb-Weaving Spider Algorithm for Training of Recurrent Neural Networks

The quality of operation of neural networks in solving application problems is determined by the success of the stage of their training. The task of learning neural networks is a complex optimization task. Traditional learning algorithms have a number of disadvantages, such as «sticking» in local minimums and a low convergence rate. Modern approaches are based on solving the problems of adjusting the weights of neural networks using metaheuristic algorithms. Therefore, the problem of selecting the optimal set of values of algorithm parameters is important for solving application problems with symmetry properties. This paper studies the application of a new metaheuristic optimization algorithm for weights adjustment—the algorithm of the spiders-cycle, developed by the authors of this article. The approbation of the proposed approach is carried out to adjust the weights of recurrent neural networks used to solve the time series forecasting problem on the example of three different datasets. The results are compared with the results of neural networks trained by the algorithm of the reverse propagation of the error, as well as three other metaheuristic algorithms: particle swarm optimization, bats, and differential evolution. As performance criteria for the comparison of algorithms of global optimization, in this work, descriptive statistics for metrics of the estimation of quality of predictive models, as well as the number of calculations of the target function, are used. The values of the MSE and MAE metrics on the studied datasets were obtained by adjusting the weights of the neural networks using the cycling spider algorithm at 1.32, 25.48, 8.34 and 0.38, 2.18, 1.36, respectively. Compared to the inverse error propagation algorithm, the cycling spider algorithm reduced the value of the error metrics. According to the results of the study, it is concluded that the developed algorithm showed high results and, in the assessment of performance, was not inferior to the existing algorithm

Experimental Study of Oil Non-Condensable Gas Pyrolysis in a Stirred-Tank Reactor for Catalysis of Hydrogen and Hydrogen-Containing Mixtures Production

The present study is focused on improving the technology for deep oil sludge processing by pyrolysis methods, considered to be the most promising technology for their environmentally friendly utilization, in which a significant yield of fuel products is expected. The technology developed by the authors of this study is a two-stage process. The first stage, pyrolysis of oil sludge, was investigated in previous papers. A significant yield of non-condensable gases was obtained. This paper presents a study of the second stage of complex deep processing technology—pyrolysis of non-condensable gases (purified propane) using a stirrer with the help of the developed experimental setup. The expected benefit of using the stirrer is improved heat transfer due to circumferential and radial-axial circulation of the gas flow. The effect of a stirrer on the yield of final target decomposition products—H2-containing mixtures and H2 generated during non-catalytic (medium-temperature) and catalytic pyrolysis of non-condensable gases obtained by pyrolysis of oil sludge are estimated. Ni catalyst was used for catalytic pyrolysis. The study shows that the application of the stirrer leads to increasing in H2-containing mixtures and H2 concentrations. In particular, during the whole reaction time (10 h), the average H2 concentration in pyrolysis gas during catalytic pyrolysis increased by ~5.3%. In this case, the optimum reaction time to produce H2 was 4 h. The peak H2 concentration in the pyrolysis gas at reaction temperature 590 ± 10 °C was: 66.5 vol. % with the stirrer versus 62 vol. % without the stirrer with an error of ±0.4 %. A further increase in reaction time is cost-effective in order to obtain H2-containing mixtures

The Orb-Weaving Spider Algorithm for Training of Recurrent Neural Networks

The quality of operation of neural networks in solving application problems is determined by the success of the stage of their training. The task of learning neural networks is a complex optimization task. Traditional learning algorithms have a number of disadvantages, such as «sticking» in local minimums and a low convergence rate. Modern approaches are based on solving the problems of adjusting the weights of neural networks using metaheuristic algorithms. Therefore, the problem of selecting the optimal set of values of algorithm parameters is important for solving application problems with symmetry properties. This paper studies the application of a new metaheuristic optimization algorithm for weights adjustment—the algorithm of the spiders-cycle, developed by the authors of this article. The approbation of the proposed approach is carried out to adjust the weights of recurrent neural networks used to solve the time series forecasting problem on the example of three different datasets. The results are compared with the results of neural networks trained by the algorithm of the reverse propagation of the error, as well as three other metaheuristic algorithms: particle swarm optimization, bats, and differential evolution. As performance criteria for the comparison of algorithms of global optimization, in this work, descriptive statistics for metrics of the estimation of quality of predictive models, as well as the number of calculations of the target function, are used. The values of the MSE and MAE metrics on the studied datasets were obtained by adjusting the weights of the neural networks using the cycling spider algorithm at 1.32, 25.48, 8.34 and 0.38, 2.18, 1.36, respectively. Compared to the inverse error propagation algorithm, the cycling spider algorithm reduced the value of the error metrics. According to the results of the study, it is concluded that the developed algorithm showed high results and, in the assessment of performance, was not inferior to the existing algorithm

Biofuel Technologies and Petroleum Industry: Synergy of Sustainable Development for the Eastern Siberian Arctic

This article is a compilation of interdisciplinary studies aimed at ensuring the environmental, political, and economic sustainability of oil and gas-producing countries with a focus on areas with many years of permafrost. One of the main concepts adopted in this research was the desire to show that confronting various energy lobbies is not mandatory and that it is necessary to find compromises by finding and introducing innovative technologies for integrated development for the benefit of society, industry, and the state. This is particularly relevant due to the increasing share of hard-to-recover hydrocarbon reserves, widely represented in the fields of the Eastern Siberian Arctic, and because Russia is the leader in flare emissions. We thus present the relevance of using these gases as industrial waste while reducing the carbon footprint. The technology of biofuel production based on the use of supercritical liquid extraction in a well extractor is presented as a result of the development of the presented experimental devices representing the investigation of the processes of extraction in wells and reactors for the distillation of hydrocarbons from heavy oil components. The obtained yield of the desired product (hydrogen) of the thermocatalytic pyrolysis of the test extract was in the range of 44 to 118 L/h, depending on the catalyst. This information can help inform the direction of future ecological engineering activities in the Eastern Siberian Arctic region

Increasing the Efficiency of Foundry Production by Changing the Technology of Pretreatment with Quartzite

The efficiency of the production of foundry products depends on the reliable operation of the melting furnace including, therefore, the durability of its lining. The most common material adopted for the production of an acid furnace crucible lining is quartzite, in which during the pretreatment (heating to 800 °C followed by holding), a tridymite phase appears that maintains a constant volume at 840–1470 °C for a long time and provides high lining durability of 300–350 melts, but only when using melting temperature regimes not exceeding 1500 °C. However, the absence of iron scrap leads to the smelting of synthetic iron from only one steel scrap using higher melting temperatures (1550–1570 °C), which sharply reduces the lifetime of the lining to 220 melts. This work is devoted to research aimed at establishing technology for the pretreatment with the original quartzite, which ensures the formation of a phase state that successfully withstands elevated temperatures for a long time. The studies were carried out using a Bruker D8 ADVANCE diffractometer and a Shimadzu XRF-1800 X-ray wave-dispersive spectrometer. The work consisted of drying samples of the original quartzite at temperatures of 200 and 800 °C with subsequent exposure to temperatures of 200, 400, 600, 870, 1000, 1200, 1470 and 1550 °C. As a result, the conditions for pretreatment of quartzite were established, during which during its further use, a cristobalite phase can be obtained, which makes it possible manufacture a high-temperature lining that ensures its high durability. The introduction of this technology will ensure the efficiency of the production of foundry products for enterprises operating induction crucible furnaces at industrial frequency