scholarly journals FORECASTING OF PHYSICO-CHEMICAL PROPERTIES OF LADLE’S SLAGS ON THE BASIS OF THE CONCEPT OF THE DIRECTED CHEMICAL COMMUNICATION

2021 ◽  
pp. 195-197
Author(s):  
Dmytro Stepanenko ◽  
Oleksandr Verhun ◽  
Volodymyr Kysliakov ◽  
Viktoriia Petrusha ◽  
Mykyta Pushkarenko
Keyword(s):  
Blast Furnace ◽  
Chemical Composition ◽  
Practical Importance ◽  
Chemical Properties ◽  
Interatomic Interaction ◽  
Forecast Model ◽  
Model Parameters ◽  
Wide Range ◽  
Temperature Dependences ◽  
Slag Melts

The work is devoted to the development of a methodology for the operational forecast of the properties of the final blast furnace slag by its chemical composition and temperature to improve the quality of hot metal in terms of sulfur content.The analysis of the accumulated experimental data on the properties of modern blast furnace slags is performed, using the criteria of the theory of directed chemical bonding the dependences of liquidus temperature on model parameters are established and an adequate forecast model of bucket slag liquid temperature on its model parameters is obtained.The created technique allows to obtain temperature dependences of density, surface tension, viscosity and electrical conductivity of real blast furnace slags in the temperature range 1200-1400 ° С.The approach to modeling of slag melts at the level of interatomic interaction used in the article can be used to develop predictive models of different technological properties of furnace slags in a wide range of temperatures. The obtained results are of practical importance and can be used for rapid prediction of the liquidity temperature of furnace slags and adjustment of their chemical composition in accordance with technological requirements.

scholarly journals Influence of Fuel Chemical Properties on Soot Emissions From Gas Turbine Combustors

1989 ◽  
Author(s):  
J. S. Chin ◽  
A. H. Lefebvre
Keyword(s):  
Chemical Composition ◽  
Gas Turbine ◽  
Hydrogen Content ◽  
Continuous Flow ◽  
Empirical Relationship ◽  
Chemical Properties ◽  
Smoke Point ◽  
Fuel Composition ◽  
Wide Range ◽  
Soot Emissions

The influence of fuel composition on soot emissions from continuous flow combustors is examined. A study of the combustion characteristics of a wide range of present and potential aviation fuels suggests that smoke point provides a better indication of sooting tendency than does hydrogen content. It is concluded from this study that the best empirical relationship between fuel chemical composition and soot emissions is one which combines two fuel composition parameters — smoke point and naphthalene content — into a single parameter which is shown to correlate successfully soot emissions data acquired from several different fuels burning in a variety of gas turbine and model combustors.

The significance of relevance trees in the identification of artificial neural networks input vectors

2013 ◽  
Vol 24 (1) ◽  
pp. 27-34
Author(s):  
G. Manuel ◽  
J.H.C. Pretorius
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Methods ◽  
Demand Forecasting ◽  
Forecast Model ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Wide Range ◽  
Accurate Forecast ◽  
Artificial Neural

In the 1980s a renewed interest in artificial neural networks (ANN) has led to a wide range of applications which included demand forecasting. ANN demand forecasting algorithms were found to be preferable over parametric or also referred to as statistical based techniques. For an ANN demand forecasting algorithm, the demand may be stochastic or deterministic, linear or nonlinear. Comparative studies conducted on the two broad streams of demand forecasting methodologies, namely artificial intelligence methods and statistical methods has revealed that AI methods tend to hide the complexities of correlation analysis. In parametric methods, correlation is found by means of sometimes difficult and rigorous mathematics. Most statistical methods extract and correlate various demand elements which are usually broadly classed into weather and non-weather variables. Several models account for noise and random factors and suggest optimization techniques specific to certain model parameters. However, for an ANN algorithm, the identification of input and output vectors is critical. Predicting the future demand is conducted by observing previous demand values and how underlying factors influence the overall demand. Trend analyses are conducted on these influential variables and a medium and long term forecast model is derived. In order to perform an accurate forecast, the changes in the demand have to be defined in terms of how these input vectors correlate to the final demand. The elements of the input vectors have to be identifiable and quantifiable. This paper proposes a method known as relevance trees to identify critical elements of the input vector. The case study is of a rapid railway operator, namely the Gautrain.

scholarly journals Properties of glasses in the system BaO–B2O3–SiO2–xAl2O3 (x=0; 5; 10 mol.%)

2021 ◽  
pp. 83-89
Author(s):  
Yu.S. Hordieiev ◽  
◽  
E.V. Karasik ◽  
А.A. Amelina ◽  
◽  
...  
Keyword(s):  
Chemical Composition ◽  
Glass Ceramic ◽  
Glass Formation ◽  
Linear Thermal Expansion ◽  
Chemical Properties ◽  
Ceramic Materials ◽  
Heat Resistant ◽  
Glass Ceramic Materials ◽  
Wide Range ◽  
Physical And Chemical

This article shows the prospect of the system BaO–Al2O3–B2O3–SiO2 as the basis of vitreous and glass ceramic materials, which are widely used in rocket production for high-temperature protection of heat resistant alloys, in the power industry for sealing solid oxide fuel cells, and in the production of heat resistant glass ceramic materials. We examined the conditions of glass formation and properties of glasses with the following content of components (mol.%): BaO 30–70, B2O3 10–50, SiO2 20–60, and Al2O3 0–10. We established experimentally that the physical and chemical properties of glass, depending on its chemical composition, vary within the following limits: coefficient of linear thermal expansion of (71–122)10–7 К–1; glass transition temperature of 500–6500С; dilatometric softening point of 540–6700С; and density of 3.20–4.21 g cm–3. The volume resistivity of the studied glasses is within 1011–1013 Ohmcm at the temperature of 1500С. Generalization of the dependences of glass properties on their chemical composition was carried out with the use of the additive equations, for which the partial contributions of oxides to the values of the corresponding properties were determined by experimental and statistical methods. The established patterns of influence of components and conditions of glass formation on the physical and chemical characteristics of glasses allows implementing the process of designing of a wide range of glass compositions with the complex of specified properties in order to solve the tasks of their practical use.

scholarly journals The individual effect of different production systems, age and sex on the chemical composition of wild boar meat

2018 ◽  
Vol 87 (4) ◽  
pp. 395-402 ◽  
Author(s):  
Simona Tesařová ◽  
František Ježek ◽  
Radka Hulánková ◽  
Radim Plhal ◽  
Jakub Drimaj ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Wild Boar ◽  
Production Systems ◽  
Chemical Properties ◽  
Fat Content ◽  
Wide Range ◽  
Significant Difference ◽  
Wild Boar Meat ◽  
The Individual ◽  
Age And Sex

Meat from wild boar (musculus teres major, n = 160) originating from two localities with different production systems was analysed. The contents of crude protein, pure protein, fat, collagen, dry matter and ash were determined in each sample. The effect of locality, age and sex on the chemical properties of the wild boar meat was studied with the use of statistical analysis. The values obtained for the chemical composition of the muscle tissue of the wild boar from localities A and B corresponded to the results obtained in other countries. The protein content fell within the range of 20.49–21.26% at locality A and 18.77–20.34% at locality B. The fat content fell within the wide range of 0.83–2.38% (0.83–1.67% at locality A and 1.51–2.38% at locality B). It is clear from the statistical evaluation that wild boar hunted in the enclosed locality had a significantly higher (P < 0.05) fat content and a lower content of crude (P < 0.05) and pure (P < 0.05) protein in comparison with wild boar from the unenclosed locality. A significant difference in the fat content was also demonstrated between localities in animals aged 0–12 months (P < 0.05), though only in females (P < 0.05) when younger animals (0–12 months) were divided by sex, and also in females aged 12–24 months (P < 0.05). The results confirm that the composition of wild boar meat in the Czech Republic is very variable and influenced by multiple factors.

scholarly journals Optimisation of chemical composition of high-strength structural steels for achieving mechanical property requirements

2021 ◽  
Author(s):  
Oleksandr Babachenko ◽  
Hanna Kononenko ◽  
Iryna Snigura ◽  
Nataliya Togobytska
Keyword(s):  
Mechanical Properties ◽  
Mechanical Property ◽  
Chemical Composition ◽  
High Strength ◽  
Interatomic Interaction ◽  
Structural Steels ◽  
Integral Model ◽  
Model Parameters ◽  
National Academy Of Sciences ◽  
Iron And Steel

In addition to thermomechanical treatment, one of the main factors affecting the mechanical properties of steel is the chemical composition. The chemical composition may vary for a special high-strength low-alloy steel to meet certain mechanical property requirements. This work presents an approach, based on the method of physical-chemical modelling developed at the Z.I. Nekrasov Iron and Steel Institute of the National Academy of Sciences of Ukraine, to optimise the chemical composition of high-strength structural steels. The principle of this method is to describe the chemical composition of a melt by a complex of integral model parameters of interatomic interaction, characterising the chemical and structural state of the melt. The experimental data were analysed to obtain the regression model for mechanical properties based on the parameters of interatomic interaction. Finally, a multi-criteria optimisation method was applied to obtain an optimal set of microalloying elements which ensure the required mechanical properties.

scholarly journals Analytical and experimental study of the thermophysical properties of blast furnace slag melts in the conditions of blast furnaces of Ukraine

2018 ◽  
pp. 137-150
Author(s):  
D.A. Stepanenko ◽  
N.A. Tsyupa ◽  
A.I. Belkova ◽  
A.S. Skachko
Keyword(s):  
Blast Furnace ◽  
Chemical Composition ◽  
Predictive Model ◽  
Thermophysical Properties ◽  
Experimental Studies ◽  
Enthalpy Changes ◽  
Optimal Value ◽  
Technical And Economic Indicators ◽  
Slag Melts ◽  
Made In

The aim of the work is to establish patterns of influence of the chemical composition of blast-furnace slags on the thermophysical properties of their melts, which is relevant to ensure high technical and economic indicators of the blast furnace. Experimental studies of the temperature of molten iron and slag at their release from a blast furnace with a volume of 1500 m3 have been carried out. It is shown that the temperatures of iron and slag have almost identical values and vary in the range of 1451÷14870С. On a Anton Paar rotary rheometer, viscosity measurements were made in the temperature range of 1320 ÷ 15000С and it was shown that the viscosity of slags from the blast furnace output exceeds its optimal value of 0.3 Pa.second. Based on the correlation-regression analysis of the literature data and the performed experimental studies, the enthalpy of the melts was determined as a function of the chemical composition of blast-furnace slags, which are represented through stoichiometry (ρ) and temperature. It is shown that when temperature fluctuations of slag melts in the range of 1453 ÷ 14870С their enthalpy changes in the range of 1933÷2031 kJ/kg. A predictive model for calculating the enthalpy of blast-furnace slags is proposed. On the basis of the proposed predictive model, the enthalpy of blast furnace slags for a blast furnace with a volume of 1500 m3 was calculated taking into account their actual temperatures at the outlet.

scholarly journals DEPENDENCE OF WHITENESS OF DECORATIVE SLAG-ALKALINE CEMENT FROM THE CHEMICAL COMPOSITION OF BLAST-FURNACE SLAGS

2021 ◽  
pp. 58-66
Author(s):  
P.V. Krivenko ◽  
◽  
A.G. Gelevera ◽  
A.Yu. Kovalchuk ◽  
N.V. Rogozina ◽  
...  
Keyword(s):  
Blast Furnace ◽  
Chemical Composition ◽  
Iron Oxides ◽  
High Strength ◽  
High Energy ◽  
Environmental Friendliness ◽  
High Adhesion ◽  
Wide Range ◽  
The Cost ◽  
White Portland Cement

The construction industry is demanding more and more quality decorative cements. The demand for them and the requirements for their performance are constantly growing. But since decorative cements are based on white Portland cement, their production is associated with the disadvantages of the production of all clinker cements  low environmental friendliness, high energy consumption and high prices. They are not always able to provide decorative ecological and comfortable coatings with increased performance. In addition, many countries do not produce it and have to import it. An effective alternative to decorative clinker cements can be decorative slag-alkaline cement obtained from industrial waste. It also provides a number of special properties  a wide range of colors, color fastness, high strength, high adhesion, durability and many others. But the problem associated with the use of slag-alkaline cements as decorative cements with high linen ( 70%) is the unstable chemical composition of the slag and, first of all, the different presence of iron oxides in it. It is shown that the presence of iron oxides can reduce the whiteness of decorative slag-alkaline cements due to the synthesis of compounds in them, which give the samples of blue-green color and due to the presence of iron oxides proper, which are inherent in color from brown to dark brown. The paper shows the regularities of the influence of the chemical composition of blast-furnace slags on the whiteness of an artificial slag-alkaline stone. Possibilities of obtaining decorative alkali-activated cements with a wide range of whiteness  from 70 to 97% are shown. Methods of reducing the cost of slag-alkaline decorative cements by using a complex bleaching additive, where part of the expensive TiO2 can be replaced by kaolin or CaCO3, are shown. A new method for determining the whiteness of hardened materials is proposed.

scholarly journals Inferring Linkage Disequilibrium Between a Polymorphic Marker Locus and a Trait Locus in Natural Populations

Genetics ◽  
2000 ◽  
Vol 156 (1) ◽  
pp. 457-467 ◽  
Author(s):  
Z W Luo ◽  
S H Tao ◽  
Z-B Zeng
Keyword(s):  
Linkage Disequilibrium ◽  
Allele Frequency ◽  
Random Mating ◽  
Natural Populations ◽  
Polymorphic Marker ◽  
Marker Locus ◽  
Model Parameters ◽  
Phenotypic Variance ◽  
Wide Range ◽  
Trait Locus

Abstract Three approaches are proposed in this study for detecting or estimating linkage disequilibrium between a polymorphic marker locus and a locus affecting quantitative genetic variation using the sample from random mating populations. It is shown that the disequilibrium over a wide range of circumstances may be detected with a power of 80% by using phenotypic records and marker genotypes of a few hundred individuals. Comparison of ANOVA and regression methods in this article to the transmission disequilibrium test (TDT) shows that, given the genetic variance explained by the trait locus, the power of TDT depends on the trait allele frequency, whereas the power of ANOVA and regression analyses is relatively independent from the allelic frequency. The TDT method is more powerful when the trait allele frequency is low, but much less powerful when it is high. The likelihood analysis provides reliable estimation of the model parameters when the QTL variance is at least 10% of the phenotypic variance and the sample size of a few hundred is used. Potential use of these estimates in mapping the trait locus is also discussed.

scholarly journals Antifungal Activity of Propyl Disulfide from Neem (Azadirachta indica) in Vapor and Agar Diffusion Assays against Anthracnose Pathogens (Colletotrichum gloeosporioides and Colletotrichum acutatum) in Mango Fruit

2021 ◽  
Vol 9 (4) ◽  
pp. 839
Author(s):  
Muhammad Rafiullah Khan ◽  
Vanee Chonhenchob ◽  
Chongxing Huang ◽  
Panitee Suwanamornlert
Keyword(s):  
Antifungal Activity ◽  
Mycelial Growth ◽  
Colletotrichum Gloeosporioides ◽  
Colletotrichum Acutatum ◽  
Model Parameters ◽  
Pathogenicity Test ◽  
Agar Diffusion ◽  
Wide Range ◽  
Significant Difference ◽  
Diffusion Assay

Microorganisms causing anthracnose diseases have a medium to a high level of resistance to the existing fungicides. This study aimed to investigate neem plant extract (propyl disulfide, PD) as an alternative to the current fungicides against mango’s anthracnose. Microorganisms were isolated from decayed mango and identified as Colletotrichum gloeosporioides and Colletotrichum acutatum. Next, a pathogenicity test was conducted and after fulfilling Koch’s postulates, fungi were reisolated from these symptomatic fruits and we thus obtained pure cultures. Then, different concentrations of PD were used against these fungi in vapor and agar diffusion assays. Ethanol and distilled water were served as control treatments. PD significantly (p ≤ 0.05) inhibited more of the mycelial growth of these fungi than both controls. The antifungal activity of PD increased with increasing concentrations. The vapor diffusion assay was more effective in inhibiting the mycelial growth of these fungi than the agar diffusion assay. A good fit (R2, 0.950) of the experimental data in the Gompertz growth model and a significant difference in the model parameters, i.e., lag phase (λ), stationary phase (A) and mycelial growth rate, further showed the antifungal efficacy of PD. Therefore, PD could be the best antimicrobial compound against a wide range of microorganisms.

scholarly journals A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Karim El-Laithy ◽  
Martin Bogdan
Keyword(s):  
Reinforcement Learning ◽  
Spike Timing ◽  
Neural Representation ◽  
Model Parameters ◽  
Learning Framework ◽  
Reference Target ◽  
Wide Range ◽  
Spiking Network ◽  
Dynamic Synapses ◽  
Exclusive Or

An integration of both the Hebbian-based and reinforcement learning (RL) rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

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