CC BY
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Шариков Ю.В.1, Самойлов Р.В.2
'Профессор, доктор технических наук; 2Студент, Национальный минерально-сырьевой университет «горный» (горный
университет)
ИССЛЕДОВАНИЕ АБСОРБЦИОННЫХ СВОЙСТВ МЕТИЛДИЭТАНОЛАМИНА В ПРОЦЕССАХ ОЧИСТКИ
ПРИРОДНОГО ГАЗА
Аннотация
В статье проводятся исследования свойств метилдиэтаноламина и рассматриваются реакции взаимодействия сорбента с сероводородом, сернистыми примесями и диоксида углерода в технологии очистки природного газа. Проведено сравнение с другими сорбентами и выявлены преимущества использования данного сорбента.
Ключевые слова: исследование свойств, очистка, сорбент, сравнение, преимущества.
Sharikov I.V. \ Samoylov R.V.2
1Professor, Dr. of Science (Chem. Engng.); 2Student. National mineral resources university (mining university)
STUDYING ABSORPTION CONDITION METHYLDIETHANOLAMINE TO PROCESS PURIFICATION NATURAL GAS
Abstract
The article contains investigation results of properties a metildietanolamin and using it in purification technology of natural gas by interaction of a sorbent with hydrogen, sulphurous impurity and carbon dioxide. The parallel of comparing with other sorbents is drawn and advantages of use this sorbent are revealed.
Keywords: investigation of properties, purification, sorbent, comparing, advantages.
Introduction
Extracted natural gas usually contains a large amount of impurities to be removed before transportation for reducing corrosive activity. The amount and composition of such impurities depend on the field and purification methods are determined largely by the composition and quantity. For example, for gas produced in Astrakhan field is characterized by a large number of the content of hydrogen sulfide and carbon dioxide. [1]
Production field H2S, % О p o4
Astrakhan 13.96 25.37
KGPU 4 2.5
Orenburg Gas-Condensate Field 1,7 0,60
Pokrovskoe 0,2 0,10
Gazlinskoe 0,07 0,16
Description of the properties of sorbents and their comparative characteristics.
Physics-chemical properties of MDEA and MEA.
Formula HOC2H4NH (HOC2H4)2CH3N
Density at a temperature T = 200°C , g / cm3 1,015 1,018
The boiling point at a pressure of 101.333 kPa , ° C 170 247
Congelation temperature 10,5 -21
Vapor pressure at temperature T = 60°C, Pas 660 24
Dynamic viscosity at a temperature T = 25°C, 103 Pa 19 80
The specific heat at temperature of T = 30°C, kJ / (kg °C) 2,72 2,32
Mass fraction of the amine in the working solution ,% 10-20 30-50
The heat of reaction kJ / kg
H2S 1511 1047
CO2 1919 1340
Advantages of MDEA:
• Low corrosiveness of the solution
• Degree of saturation of more than MEA , this means that the amount of the circulating solution and the energy consumption for its circulation and less regeneration
• The use of MDEA saves energy ( heating steam ) is also due to the lower heat of desorption MDEA compared to the IEA , the regeneration of the absorbent
• Reduce foaming
• On the inner surfaces of equipment no deposits arising due to degradation of the previously applied absorbents based MEA increases the heat exchange efficiency , which also reduces energy consumption
• The application of the absorbent solution based on the MEA observed loss due to entrainment vapor in the desorption step, where the temperature reaches a flow of 115 °C - 130 °C. In the application of MDEA having a boiling point (247 °C) is significantly higher than that of MEA (170 °C) during regeneration entrainment amine solution at the operating temperatures of the absorbers, is greatly reduced, to a value close to zero. [2]
Methods of studying the process of sorption purification and determination of optimal treatment regimes based on two methods.
The experimental method is much more expensive and labor resources for practicing modes.
Modeling method using specialized software package HYSYS.
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This approach allows for the analysis of the thermodynamic properties and phase equilibrium, as well as the existing models of bank machines to build the technological scheme of purification, the resulting scheme to conduct a study to determine the optimum operating conditions purification scheme with different composition of the source of natural gas. Using this approach, we have created an input stream of gas, based on the composition of natural gas Astrakhan field and taking into account performance. Further technological scheme has been developed, a software package HYSYS, the purification process using MDEA as absorbent. For the calculation of the thermodynamic properties and equilibrium states was used amine method and Peng -Robinson.
With the resulting circuit (Figure 1) studied the effect of pressure, flow absorbent and effectiveness of plates on the degree of purification of natural gas and bring it to the desired composition of the impurity.
Fig. 1 - Scheme of treatment of natural gas
C
&
-=
О
c
;©
c
(J
C
О
(J
Zj
-=
H
0 20 40 60 80
The pressure drop in the absorber
Fig. 2 - The dependence of the pressure difference from the concentration in the absorber of the product obtained at the outlet of the
absorber
Using the diagram (Fig. 1) produced digital calculations, in which the method of selection chosen concentration of MDEA for gas treatment. At a concentration of MDEA - 17% of the obtained results (Fig.4.5). Under the same conditions, and use as a sorbent DEA output results obtained (Fig. 3).
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Fig. 3 - Concentration at the inlet of the absorber
Ethane Propane
Fig. 4 - The concentration of the product.
Fig. 5 - The concentration of purification products sorbent DEA
Established scheme model may be used also in operator advisor mode when running on natural gas purification plants, for adjusting the technological mode when changing the composition of the inlet gas and productivity of the plant.
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Conclusion: In the course of the study were presented advantages of quality and economy of use MDEA product as it is stronger and more effective sorbent that allows to achieve higher purity. Comparisons of graphs which show comparative diagrams for refining natural gas by different sorbents.
References
1. Мановян А.К. Технология первичной переработки нефти и природного газа. 2-е Издание 2001г. - 567 с.
2. Анализ применения новых сорбентов в процессах абсорбционной очистки технических и природных газов от сероводорода и углекислого газа. [Электронный ресурс] URL: http://www.nifhi.ac.ru/~kinetics/Mdea/Seminar1.html
Зотов К. Н.1, Кузнецов И. В.2, Симбирцева Д. С.3, Жданов Р.Р.4
'Кандидат технических наук, 2Доктор технических наук, 3Студент спец. Инфокоммуникационные технологии и системы связи, ^Кандидат технических наук, Уфимский государственный авиационный технический университет РАЗРАБОТКА АЛГОРИТМА ОПРЕДЕЛЕНИЯ ПРОСТРАНСТВЕННО-ВРЕМЕННОЙ РАЗЛАДКИ В СЕТЯХ
ОПЕРАТОРА МОБИЛЬНОЙ СВЯЗИ
Аннотация
В статье рассмотрена возможность использования теории разладки для нахождения пространственно-временных изменений трафиковых процессов на заранее позиционированных массах абонентов сети связи.
Ключевые слова: теория разладки, пространственно-временная разладка, трафиковые процессы.
Zotox K N.1, Kuznetsov I. V.2, Simbirtceva D. S.3, Zhdanov R.R.4 1 Candidate of Technical Sciences, 2Doctor of Technical Sciences, 3Student, 4Candidate of Technical Sciences, Ufa State Aviation
Technical University
DEVELOPMENT OF THE ALGORITM TO IDENTIFY SPACE AND TEMPORAL CHANGES IN CELLULAR OPERATOR
NETWORKS
Abstract
Current abstract observes possibility of use the theory of change to identify space and temporal changes in traffic processes on the subscribers positioned in advance.
Keywords: theory of change, traffic process, space and temporal changes.
The functioning process of cellular systems is accompanied by sudden congestion in separate parts caused by moving of the subscribers. It makes necessary to provide the operational control of radio resources.
First thing for efficient management of radio resources is positioning of mobile stations (MS) with enough accuracy to identify areas of change [1, 5].
There are many different ways to identify the position of subscribers, such as means of the mobile network [2] or global navigation systems [3].
The data obtained from positioning of subscribers are represented in a big range of array as it is impossible to process every subscriber. The efficient management system proposes clustering of all the subscribers of given, find the parts with abnormal change of MS concentration and identify node of demand (ND) inside each of the clusters. Node of demand (ND) shows the most concentrated loading level in the local part of service area that needs additional radio resources.
The NDs obtained are subject to connection with existing network of cellular operator. It makes by creating the models of situational and adaptive planning [4] it also helps to control the whole network with efficiency.
Each cluster consists of points (x;y), where x,y are solid axes showing the location of subscriber characterized by its feature.
The theory of changed [6] which has some limitations is to define borders for abnormal areas during the process of initial clustering:
1. Every existing case should be chosen the criterion of algorithm of change.
2. It is difficult enough to find the starting value to obtain from model more or less robust information of change.
3. Opening of clusters in abnormal areas.
Main task of clustering concerning management of radio resources of cellular network is to define borders (coordinates) of clusters that are aggregative sources of messages and classify traffic of these sources. In addition task salvation should take place in real time. Clustering algorithm based on change will help to identify quantity of necessary clusters. This quantity will give opportunity to apply the algorithms of fuzzy clustering. Hence the prior task in finding NDs is to define borders of abnormal areas in time and space.
Features of the theory of change:
1. Invariance for the law of distribution (aprioristic uncertainty conditions).
2. Static processing by analyzing data arrays that can change.
3. Invariance for counting (does not depend on mark point) [7].
4. Possibility to define the quantity of iterations by introduction of constraint system.
The clustering algorithm based on change shown in following steps:
1. Definition of positioning of MS (x;y) during the step.
2. Splitting investigated area into sub-areas with a step equal to the average value of positional error (frame overlay).
3. Definition of change boarders in sample. Functional T(S) is fulfilled by formula:
T (S (N; M)) В
2S - S3 - S2
S3 + S2
(1)
2 S - S - S T(S(N;M))y = % S
S1 ^ S2
(2)
where T(S (N; M))В -existed in t = |^ф, tф + At^
value of functional with increasing loading, T(S (N; M))У - with decreasing loading, and both functions j , where tф - actual time, At д - discrete time, defined by real conditions:
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