Values Of Weight Coefficients Research Articles

Influence of biochemical composition of above-ground biomass of oilseed radish on the expediency of its green manure application

Over a ten-year period, the volumes of formed ground biomass of oil radish and its biochemical composition were estimated for spring and summer plantings in terms of its green manure application. The formed leaf and stem mass of the oil radish variety Zhuravka was studied using official AOAC international analysis methods, liquid chromatography, methods of field evaluation of yield and weight and equivalent indicators. The effectiveness of green manure application was assessed by the method of multicriteria-decision support. Oil radish was confirmed to have a high adaptive potential at the maximum level of determination of bioproductivity, with an optimal moisture and temperature supply of up to 40%. The long-term average productive and biochemical portfolio of oil radish was identified as follows: 21.19 t/ha of leaf-stem ground mass with 62.3% soil cover 70 days after sowing, 2.8% nitrogen, 22.8% fibre, 0.6% phosphorus, 3.8% potassium, 16.6 μmol/g glucosinolates at the C/N ratio of 14.7, 83% plant-mass quality, and 48.1 mol/ha biofumigation potential based on glucosinolate aucumulation. The study confirmed the high potential of oilseed-radish adaptability to the regimes of unstable moisture of territories, finding interannual variation of the total bioproductivity and biochemical parameters of leaf-mass quality with the value of the variation coefficient ranging 23.6–31.2%, depending on the sowing date. The effectiveness of oilseed radish was also revealed by comparison with the similar interannual variation of hydrothermal conditions of vegetation (the amount of precipitation 48.2%, average daily temperature 27.5%, moisture coefficient 68.1%, and aridity index 58.9%). Oilseed radish was found to be agrobiologically and agrotechnologically feasibile to be used in the system of both spring and summer (intermediate) green manure technologies in grey loess soils under conditions of unstable moisture, as indicated by its bioproductivity and biochemical quality of leaf mass, as well as the results of the components of the multicriteria assessment of the plant as green manure, the value of normalised weight coefficients ranging 0.62–0.99

Approximation of the Objective Function of Single-Machine Scheduling Problem

The problem of the approximation of the coefficients of the objective function of a scheduling problem for a single machine is considered. It is necessary to minimize the total weighted completion times of jobs with unknown weight coefficients when a set of problem instances with known optimal schedules is given. It is shown that the approximation problem can be reduced to finding a solution to a system of linear inequalities for weight coefficients. For the case of simultaneous job release times, a method for solving the corresponding system of inequalities has been developed. Based on it, a polynomial algorithm for finding values of weight coefficients that satisfy the given optimal schedules was constructed. The complexity of the algorithm is O(n2(N+n)) operations, where n is the number of jobs and N is the number of given instances with known optimal schedules. The accuracy of the algorithm is estimated by experimentally measuring the function ε(N,n)=1n∑j=1n∣wj−wj0∣wj0, which is an indicator of the average modulus of the relative deviation of the found values wj from the true values wj0. An analysis of the results shows a high correlation between the dependence ε(N,n) and a function of the form α(n)/N, where α(n) is a decreasing function of n.

Исследование двухмодульных взвешенных кодов с суммированием с произвольными разбиениями множества информационных символов

A method has been proposed for constructing two-modulus sum codes that are suitable for solving the synthesis problems of self-checking digital computing devices and systems, as well as the technical means of their diagnostics. In the process of constructing a code, a set of data symbols is divided into two subsets, forming separate vectors for which the total weight is calculated according to a predetermined modulus. Beforehand, in each of the selected vectors, one of the data symbols is weighted with a coefficient from a natural series. Codes obtained with different values of weight coefficients and modulus for determining the total weight form a separate family of uniform binary codes with a fixed number of check symbols. The article provides an example of constructing a sum codes in the ring of residues modulus M = 4. Using this modulus enables the construction of a two-modulus code with k = 4 check symbols for any value of the m number of data symbols. As the value of m increases, the relationship between the number of data symbols and check symbols will change significantly, and the resulting code will have low redundancy. This is important when using the considered class of codes in the construction of digital devices. Previously unknown properties of the discussed class of sum codes have been identified, and taking these into account is beneficial when addressing problems related to the synthesis and technical diagnostics of digital devices.

Construction of regional breeding indices for cows of the Ayrshire population of the Russian Federation

An assessment of the Estimation Breeding Value (EBV) of Ayrshire first-calving cows was carried out using the BLUP Animal Model, followed by the modeling of breeding indices and the calculation of weight coefficients, taking into account the segmentation of animal groups by breeding regions. As a result of the assessment of breeding value, differences were established for certain economically useful traits in the studied populations of first-calving cows. In the group of cows from the Republic of Karelia (KRL), estimates for milk yield for 305 days (MY) were +263 kg, fat yield (FAT) +9.2 kg, protein yield (PROT) +8.7 kg. Animals from the Leningrad region (LO) had the most desirable reproduction scores: days open (DO) and Intercalving period (ICP) were at the level of -0.15 and +0.08 days, respectively. The fertility index (FI) turned out to be at the level of +0.19, yielding in value only to first-calving cows from the KRL, whose estimates were +0.31. A negative genetic relationship between MY and the percentage of fat and protein (rg = -0.100, rg = -0.096) and a low phenotypic relationship (rp = +0.013, rp = +0.168, respectively), were revealed. The phenotypic correlation of MY with live weight at 10 months (W10) was +0.351, which is higher compared to other signs of development. The heritability coefficients of developmental indicators, such as W10, live weight at the first insemination (W1INS) and after the first calving (W1C) had insignificant differences between themselves and amounted to: 0.17, 0.20 and 0.18, respectively. On the basis of phenotypic and genetic correlation coefficients and heritability coefficients of the studied traits, on average for the population, a general index was used, and taking into account differences in regions, regional indices were developed. The main difference between the presented indices is the values of weight coefficients. In the Republic of Komi, in comparison with other regions, MY for 305 days (+54.94) and FI (+0.90) take more weight, in the Siberian Federal District (SFO) - FAT (+12.98), in the Southern Federal District (SFO) - PROT (+26.9), and in the index for the LO - the indicator W10 (+0.50). The values of weight coefficients for W10 differ between regions not only in terms of values, but also in direction. According to this indicator, negative weighting coefficients were noted in the Vologda (-0.30), Kirov regions (-0.10) and the Southern Federal Districts (-0.10). In the KRL, there is a greater need to work with the reproductive qualities of animals, as indicated by the weight coefficient for the fertility index equal to +0.25.

Comparison of Aggregation Operators in the Group Decision-Making Process: A Real Case Study of Location Selection Problem

Aggregation methods in group decision-making refer to techniques used to combine the individual preferences, opinions, or judgments of group members into a collective decision. Each aggregation method has its advantages and disadvantages, and the best method to use depends on the specific situation and the goals of the decision-making process. In certain cases, final rankings of alternatives in the decision-making process may depend on the way of combining different attitudes. The focus of this paper is the application and comparative analysis of the aggregation operators, specifically, arithmetic mean (AM), geometric mean (GM), and Dombi Bonferroni mean (DBM), to the process of criteria weights determination in a fuzzy environment. The criteria weights are determined using Fuzzy Multi-Criteria Decision-Making (F-MCDM) methods, such as Fuzzy Analytic Hierarchy Process (F-AHP), Fuzzy Pivot Pairwise Relative Criteria Importance Assessment (F-PIPRECIA), and Fuzzy Full Consistency Method (F-FUCOM), while the final alternative ranking is obtained by Fuzzy Weighted Aggregated Sum Product Assessment (F-WASPAS). A comparison of aggregation operators is done for the real case of location selection problem for a used motor oil transfer station in the regional center of Southern and Eastern Serbia, the city of Niš. The results obtained in this study showed that the views of different experts and application of a certain aggregation approach may have a significant impact on the values of criteria weight coefficients and further on the final ranking of alternatives. This paper is expected to stimulate future research into the impact of aggregation methods on final rankings in the decision-making process, especially in the field of waste management.

ОЦІНЮВАННЯ ФАКТОРІВ SWOT-АНАЛІЗУ ДОСЛІДНИЦЬКОГО УНІВЕРСИТЕТУ НА ОСНОВІ НЕЧІТКИХ ДАНИХ

The article proposes a methodical approach to the evaluation of SWOT factors of the organization based on the application of the toolkit of strategic diagnostics of the company's activity and the fuzzy-multiple theory. To achieve the set goals, the work uses classic methods of strategic analysis (PESTEL, SNW, SWOT, Wilson matrices – to form a list of factors for each direction of analysis) and a fuzzy method of multicriteria evaluation Fuzzy Extension of Simplified Best-Worst Method (F-SBWM) – for calculation of the weight coefficients of the identified factors. The developed model uses expert linguistic assessments based on a defined 8-level term set, which is transformed into fuzzy numbers with a triangular representation and triangular membership functions. The validity of the proposed model is ensured by the application of procedures for checking the achievement of consensus by the expert group regarding the selection of the best and worst factors for each direction of analysis, the consistency of the individual opinions of experts and the consistency of expert assessments for the best and worst approaches for each direction of analysis. In case of inconsistency between experts' assessments, it is suggested to use the Fuzzy Delphi method. An approach based on the α-intersection of fuzzy numbers is used to consider different levels of uncertainty and obtain interval values for factor estimation. For computer support of the methodological approach, a framework was developed in the Excel software application, which contains the following components: a block for entering linguistic expert information for each direction of analysis, a block for transforming linguistic data into fuzzy numbers in a triangular form, blocks for calculating the weighting coefficients of factors for best- and worst-approaches, a unit for checking the consistency of individual opinions of experts, a unit for calculating fuzzy integral values of factor weighting coefficients and their defuzzification. The methodical approach enables the top management of the enterprise to identify the SWOT factors of the enterprise, to determine the weighting factors of these factors based on vague expert information, and to expand the opportunities of their application in the strategic process.

Математическая модель оптимизации комплекса инструментов «зелёной» логистики

«Green» supply chain management involves development and implementation of environmental solutions. The objective of their substantiation and evaluation under conditions of limited resources for their implementation is of particular importance. In order to highlight a whole complex of environmental solutions, the generalized concept of «green» logistics tools» is used in this research paper. The selection of optimal «green» logistics tools for implementation of material, financial, information resources and services in the elements of a logistics system is described. A technique for combining linear programming and multicriteria decision-making methods (MCDM) is presented. A system of «green» logistics tools has been formed for the six standard elements of a logistics system. The calculation results of the values of weight coefficients for tools and logistics elements are provided. The calculations of the weight coefficients were made using the developed CDM model constructed by combining DEMATEL and MARCOS multicriteria methods. The dependencies of the efficiency of «green» logistics tools on changes in available logistics resources are obtained. It is illustrated that the redistribution of logistics resources for implementation of various «green» logistics tools in the range of 7,5-15 % allows achieving the maximum effect from implementation of these tools in a logistics system or supply chain. A calculated example of a choice of 105 «green» logistics tools for six logistics elements is given in the article.

Infrared Image Enhancement Based on Optimally Weighted Multi-Scale Laplacian of Gaussian and Local Statistics Using Particle Swarm Optimization

Infrared imagery is extensively used in defense, remote sensing and medical applications. While the infrared images have many advantages over RGB images, the details in these images are usually blurred which in turn leads to some difficulties for human operators. In this paper, a new method based on Laplacian of Gaussian scale-space and local variance is presented to improve the visual quality of the infrared images. At the first step, the Gaussian scale-space is constructed by convolving the original image with different Gaussian kernels. Then, the two-dimensional Laplacian kernels are convolved with the Gaussian scale-space to achieve details with both positive as well as negative contrasts. The weighted details are added to the original image to deblur the dim areas. At the final step, to increase the dynamic range of the image and have better visual quality, the local variance of the image is also added to the output of the previous step. Since finding optimum weighting coefficients is a difficult task empirically, here, we use a population-based meta-heuristic optimization algorithm called particle swarm optimization (PSO) to find the optimum values for weighting coefficient values. Beside qualitative comparison, Structural Similarity (SSIM) and second-derivative-like measure of enhancement (SDME) are used to quantitatively investigate the images quality. The proposed method outperforms the baseline algorithms in both qualitative and quantitative perspectives.

QoS ROUTING IN TELECOMMUNICATIONS NETWORKS

With the increasing demand for quality of service (QoS) in today's multiservice networks, more and more attention is paid to the routing facilities. With the transition to new generation networks, the issues of improving routing algorithms and protocols seem to be especially relevant. The existing routing algorithms have been developed quite fully and, on the whole, function effectively if the optimization of the route selection is carried out according to one parameter. In the case of multi-criteria routing, specialists are faced with the problem of reducing many criteria to a single one by means of additive convolution. Less commonly, multiplicative convolution is used, since it is not always possible to apply multiplicative transformation to real physical processes. The problem of applying multiplicative convolution to various QoS requirements is to choose the weight coefficients for each of the parameters. In this paper, we analyze the choice of the optimal path for multicriteria routing, taking into account the weight coefficients and cost parameters on the basis of the above twolevel approach. The results of numerical modeling of the search for the optimal path for various values of weight coefficients and cost coefficients are presented. It is shown that when choosing a path for multi-criteria optimization, it is necessary to choose the coefficients of the additive convolution as the product of the weight coefficients and the cost coefficients directly. It has been found that the value of the probability of packet transmission affects to a greater extent the choice of the optimal path than the values of the coefficients for the parameters of delay, delay variation, and bandwidth.

Short text automatic scoring system based on BERT-BiLSTM model

Aiming at the problems of sparse features, polysemy of one word and less context related information in short text automatic scoring, a short text automatic scoring model based on bidirectional encoder representations from transformers - bidirectional long short-term memory (BERT-BiLSTM) is proposed. Firstly, the large-scale corpus is pre-trained with bidirectional encoder representations from transformers (BERT) language model to acquire the semantic features of the general language. Then the semantic features of short text and the semantics of keywords in a specific context are acquired through the short text data for the pre-fine tuning downstream specific tasks set pre-fined by BERT. And then the deep-seated context dependency is captured through bidirectional long short-term memory (BiLSTM). Finally, the obtained feature vectors are input into Softmax regression model for automatic scoring. The experimental results show that compared with other benchmark models of convolutional neural networks(CNN), character-level CNN (CharCNN), long short-term memory (LSTM) and BERT, the short text automatic scoring model based on BERT-BiLSTM achieves the best average value of quadratic weighted kappa coefficient.

Экспериментальные исследования кодов с суммированием взвешенных разрядов с произвольными весовыми коэффициентами в кольце вычетов по заданному модулю

Some results of the study of weighted codes with summation of different values of weight coefficients in the ring of deductions by an arbitrary modulus are presented. Such codes have not been studied in detail in the research literature. The rules for constructing weighted codes with summation are given. The peculiarities of the choice of module values and weighting coefficients for building a code with the detection of any single errors in code words are explained. The article focuses on experimental studies of the use of summation codes to detect errors at the outputs of control combinational circuits. During the experiment with control combinational schemes, the patterns inherent in this class of codes are established and described. The patterns are associated with specific ratios between the weighting coefficients, which give the same detection characteristics of the integrated control scheme. It is shown that, despite a great variety of ways of weighing the digits, some of the resulting weighted codes with summation have the same characteristics of error detection at the outputs of combinational circuits. The results of the study should be taken into account when building digital devices and systems with fault detection, when improving the means of automated design and synthesis of modern and developed microelectronic control systems.

МОДЕЛИРОВАНИЕ СЕЛЕКЦИОННОГО ИНДЕКСА ДЛЯ АЙРШИРСКОЙ ПОРОДЫ МОЛОЧНОГО СКОТА С ИСПОЛЬЗОВАНИЕМ ЭКСТЕРЬЕРНЫХ ПОКАЗАТЕЛЕЙ

The article presents results of selection efficiency of potential parents in Ayrshire breed using IAYR multiple-factor selection index, which includes productivity traits such as milk yield, fat and protein yield and integrated exterior parametres: UDC udder index and FLC leg index. As an original data array, a sample was generated with parameters for milk productivity and exterior characteristics of 574 Ayrshire first-calf heifers, which are daughters of 21 bulls from the leading breeding farm of Leningrad region. Calculation of phenotypic and genetic correlations was carried out in the course of the study, which revealed the greatest paratypical relationship between milk yield and fat yield rp = 0.912 at p <0.001, and the highest genetic relationship was noted between milk yield and protein yield rg = 0.960 at p <0.001. A high value of heritability coefficient for milk yield was established, which amounted to h2 = 0.506, which is connected with usage of imported servicing bulls with high genetic potential for herd reproduction. To determine the values of index weight coefficients, the variance and covariance components of variability of the studied parametres were calculated. Economic components of the parametres included in the developed equation are formed. As a result of selection modeling with an intensity of 10% according to the constructed equation of the productive-exterior index IAYR, a significant and reliable advantage in milk production by 1899 kg of milk was established in comparison with other animals, in terms of fat yield - by 64.7 kg and protein yield - by 51.7 kg.

Vibration control of improved LQG for wheel drive electric vehicle based on uncertain parameters

To improve the ride comfort of wheel drive electric vehicle, a quarter dynamic model is established for vehicle active and passive suspension. Based on the response root mean square (RMS), the evaluation index of vehicle ride comfort is derived. According to the influence of uncertain parameters on system minimum RMS, the optimal vehicle parameters are determined. To avoid experience value of uncertain weight coefficient affecting the LQG control, analytic hierarchy process (AHP) is adopted to determine the weighting coefficient of vehicle performance evaluation index. And the LQG controller of vehicle active suspension is designed based on optimal control theory. Compared with the analysis results of passive suspension, the effectiveness of active control scheme is verified.

The synthesis of strategies for the efficient performance of sophisticated technological complexes based on the cognitive simulation modelling

Purpose. Improving the productivity and energy efficiency of complex technological complexes through the development and use of scenario-cognitive modeling in control systems. Methodology. Fuzzy cognitive maps, in the form of a weighted oriented graph, were used to develop a scenario-cognitive model. As a result of the conducted research studies, a new strategy of generalization of an expert estimation of mutual influences of concepts on the basis of methods of the cluster analysis is offered. Findings. Based on experimental research and object-oriented analysis of a complex technological complex, a structure of a fuzzy cognitive model is created. A scenario-cognitive model in the form of a weighted oriented graph (fuzzy cognitive map) has been developed, which illustrates a set of connections and the nature of the interaction of expertly determined factors. To solve the problem of impossibility of operative interrogation of experts in case of change in parameters of functioning of difficult technological complexes, expert estimations of values of weight coefficients of mutual influence of concepts are received. Cluster analysis methods were used to group expert assessments and determine a single value as a result of the research. The results of the scenario-cognitive modeling of the enterprise showed that production shutdowns and abnormal situations related to the failure of electrical equipment, deviations of the technological regime and the quality of wastewater treatment have a significant impact on the dynamics of productivity, energy efficiency and efficient use of equipment. Originality. The new scenario-cognitive model developed for forecasting the situation in the absence of accurate quantitative information consists in creating a fuzzy cognitive map, for modeling which many parameters of complex technological complexes are expertly determined. Using the developed methodology, a degree of interaction of these parameters is found, which allows determining dynamics of change in target criteria of functioning under various management strategies. Practical value. On the basis of the created scenario-cognitive model, software has been developed which allowed analyzing dynamics of change in productivity, energy efficiency and efficiency of use of the equipment under possible scenarios of functioning of difficult technological complexes is developed.

Синтез нейросетевого регулятора управления нелинейной моделью перевернутого маятника на тележке

In this article, we consider a method for selecting a structure of a neural network used to regulate an "inverted pendulum on a cart" object taking into account its additional features of a mathematical description, namely, nonlinear parameters. The algorithm is illustrated by the example of control synthesis which includes two neuroregulators. One of them is responsible for bringing the cart to the specified position, and the second is responsible for holding the pendulum in a vertical position. The structure transformations will be performed for the controller responsible for bringing the cart to the specified position. The architecture of a neural network controller is based on a discrete controller synthesized using polynomial matrix decomposition. For the original controller, we define the limits of its possible control of a nonlinear system. To increase the range of control of a nonlinear object, we perform transformations of the neural network structure of the original controller. We will make some complications in the structure of the neural network of the regulator, namely, increase the number of neurons and replace some activation functions with nonlinear ones (hyperbolic tangent). Next, we suggest one of the ways to select initial values of weight coefficients. Then we train the neural network and check the performance of the resulting controller on a nonlinear object. At the next stage, we compare the obtained performance of a controller having a complicated neural network structure with the performance of a classical controller. Thus, the purpose of this study is to formalize the synthesis procedure for a neural network controller for controlling a nonlinear object using a calculated classical controller for a linearized object model. The proposed method of generating the architecture of a neural network of controllers makes it possible to increase the range of control by a nonlinear object in comparison with the controller obtained by the method of polynomial matrix decomposition for a linear object. Compared to the typical ones, the proposed neural network structure is not redundant and therefore does not require additional computing resources to configure it.

Content adaptation neural network method cause-specific the state of users

The aim of the study is to develop recommendations for adapting educational content based on the use of a neural network method for recognizing the human autonomic nervous system degree of activity. The initial data for decision making are the vectors of cardiointervals obtained with the help of the pulse sensor. The state of the autonomic nervous system is monitored by a two-layer artificial neural network of direct propagation. The artificial neural network was trained by combining gradient and stochastic training methods. A trial sample was used as a training set, consisting of 168 records of people’s cardiointervals belonging to different social and age groups. The presented method for recognizing the autonomic nervous system activity degree was tested via the shared programming support environment Lazarus. As a result of network training, a set of weighting coefficient values was found, for which 100% correct recognition is performed over the entire trial sample. A personalized training system that monitors the functional state of the student varies the presentation of educational content. When the functional state changes during the training load, the virtual agent organizes the training process so as to avoid stress load.

Objective methods for determining criteria weight coefficients: A modification of the CRITIC method

Determining criteria weight coefficients is a crucial step in multi-criteria decision making models. Therefore, this problem is given great attention in literature. This paper presents a new approach in modifying the CRiteria Importance Through Intercreteria Correlation (CRITIC) method, which falls under objective methods for determining criteria weight coefficients. Modifying the CRITIC method (CRITIC-M) entails changing the element normalization process of the initial decision matrix and changing data aggregation from the normalized decision matrix. By introducing a new normalization process, we achieve smaller deviations between normalized elements, which in turn causes lower values of standard deviation. Thus, the relationships between data in the initial decision matrix are presented in a more objective way. By introducing a new process of aggregation of weight coefficient values in the CRITIC-M method, a more comprehensive understanding of data in the initial decision matrix is made possible, leading to more objective values of weight coefficients. The presented CRITIC-M method has been tested in two examples, followed by a discussion of results via comparison to the classic CRITIC method.

Determination of the snow cover weight and altitude coefficient values in mountainous and poorly studied areas for assessing loads on buildings and structures

The materials use in structures and calculations for buildings and structures in Russia is determined, inter alia, by the normative loads of snow cover on the construction site. An urgent task is to determine its significance for the unexplored areas. The results of determining the normative value of the snow cover weight and changes with the height of the snow weight terrain for the designed objects of the “Udokan” Mining and Metallurgical Combine at the altitudes from 600 to 2000 m are presented. To determine the estimated snow load on buildings and structures in the territory of the facility creation, we used the series of the water supply annual maximums in the snow cover obtained as a result of the snow-measuring route observations at high-altitude weather stations located close to the designed objects. Based on the data of weather stations, a regression linear model of the water supply dependence in the snow cover on the height above the sea level was built. The main calculation method for finding the coefficients of a linear equation is the least squares method. It was revealed that with an increase in the height above the sea level by 1 m, the water supply in the snow cover increases by about 0.1 mm.

Enhancing Efficiency of Space-Time Processing of Radar Signals under Exposure of Combined Interferences

The noise immunity of radars is essentially reduced under the simultaneous exposure to active noise and passive interferences. This is stipulated by the passive interference decorrelating the active noise and also by the disruption of interperiod correlation of passive interference during the adaptation of weight coefficients of spatial filter. This paper proposes and investigates a new method of forming the classified training sample (CTS) based on the interchannel correlation analysis of range signal. This method makes it possible (in the current sounding period in terms of the maximum magnitude of interchannel correlation coefficient) to determine the range interval, within which the passive interference has the minimum level, and to form the optimal value of weight coefficient of spatial filter for its use in the next sounding period. In addition, the method allows us to form an optimal weight coefficient for compensation of active noise interference in all sounding periods of the next frequency burst during the burst-mode signal processing in the last sounding period of the current burst. The simulation process has revealed that in this case the modulation of passive interference present in compensation channel is also eliminated. It can essentially enhance the efficiency of extraction of useful signals against the background of passive interferences during the time (frequency) processing at the second stage of space-time signal filtering in radars. It has been established that the use of CTS makes it possible to significantly reduce the duration of transient during the adaptation of weight coefficients of spatial filter that enables us to enhance the efficiency of active noise suppression under the simultaneous exposure to nonstationary passive interferences.

Impact of micro-explosive atomization of fuel droplets on relative performance indicators of their combustion

The idea of producing water-containing slurries and emulsions based on Diesel fuel, oil, coal, and coal processing waste has spread around the world. One of the ways to make the combustion of such fuels more efficient is through the micro-explosive droplet breakup, that is, the secondary atomization directly in the car combustion chambers and furnaces of boiler units. Here we study experimentally how micro-explosion affects the energy-related and environmental parameters of the combustion of slurry fuel droplets based on coal and its processing wastes. We present the results of experiments determining the threshold temperatures and ignition delay times, dimensions of the flame cloud of the dispersing particles, and anthropogenic emissions. The results obtained indicate that the micro-explosive atomization of fuel droplets can improve the environmental performance by up to 40%. The comprehensive analysis of the prospects of using coal-water slurries containing petrochemicals instead of coal has shown that the relative performance indicator of slurry fuel is 1.7–3.3 times higher than that of coal. With varying values of weight coefficients (environment, energy, and cost), the dimensionless slurry fuel performance coefficient may increase by 3–30% as compared to the case of equal values for these coefficients.