Prediction performance of natural gas dehydration units for water removal efficiency using a least-square support vector machine

被引：21

作者：

Ahmadi, Mohammad Ali ^{[1
]}

Bahadori, Alireza ^{[2
]}

机构：

[1] Petr Univ Technol, Ahwaz Fac Petr Engn, Dept Petr Engn, Abadan, Iran

[2] Southern Cross Univ, Sch Environm Sci & Engn, Lismore, NSW, Australia

来源：

INTERNATIONAL JOURNAL OF AMBIENT ENERGY | 2016年 / 37卷 / 05期

关键词：

TEG; natural gas; dew point; water; predictive modelling; least-squares support vector machine;

D O I：

10.1080/01430750.2015.1004105

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Natural gas dehydration unit is employed to eliminate water from natural gas liquids and natural gas, and it is needed to avoid condensation of free water and creation of hydrates in transportation and processing facilities, prevent corrosion, and meet a water content condition. In this paper, a least-square support vector machine (LSSVM) coupled with genetic algorithm (GA) was employed to estimate the water dew point of a natural gas stream in equilibrium with a triethylene glycol (TEG) solution at different TEG concentrations and temperatures. Results showed that GA-LSSVM accomplishes more reliable outputs compared with real recorded data in terms of statistical criteria.

引用

页码：486 / 494

页数：9

共 48 条

[1] A computational intelligence scheme for prediction equilibrium water dew point of natural gas in TEG dehydration systems [J].

Ahmadi, Mohammad Ali ;

Soleimani, Reza ;

Bahadori, Alireza .

FUEL, 2014, 137 :145-154

[2] Evolving predictive model to determine condensate-to-gas ratio in retrograded condensate gas reservoirs [J].

Ahmadi, Mohammad Ali ;

Ebadi, Mohammad ;

Marghmaleki, Payam Soleimani ;

Fouladi, Mohammad Mahboubi .

FUEL, 2014, 124 :241-257

[3] Prediction breakthrough time of water coning in the fractured reservoirs by implementing low parameter support vector machine approach [J].

Ahmadi, Mohammad Ali ;

Ebadi, Mohammad ;

Hosseini, Seyed Moein .

FUEL, 2014, 117 :579-589

[4] Evolving smart approach for determination dew point pressure through condensate gas reservoirs [J].

Ahmadi, Mohammad Ali ;

Ebadi, Mohammad .

FUEL, 2014, 117 :1074-1084

[5] Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization [J].

Ahmadi, Mohammad Ali ;

Zendehboudi, Sohrab ;

Lohi, Ali ;

Elkamel, Ali ;

Chatzis, Ioannis .

GEOPHYSICAL PROSPECTING, 2013, 61 (03) :582-598

[6] Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion [J].

Ahmadi, Mohammad Ali ;

Golshadi, Mohammad .

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2012, 98-99 :40-49

[7] Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir [J].

Ahmadi, Mohammad Ali ;

Ebadi, Mohammad ;

Shokrollahi, Amin ;

Majidi, Seyed Mohammad Javad .

APPLIED SOFT COMPUTING, 2013, 13 (02) :1085-1098

[8] Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm [J].

Ahmadi M.A. .

Journal of Petroleum Exploration and Production Technology, 2011, 1 (2-4) :99-106

[9] New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept [J].

Ahmadi, Mohammad Ali ;

Shadizadeh, Seyed Reza .

FUEL, 2012, 102 :716-723

[10] Neural network based unified particle swarm optimization for prediction of asphaltene precipitation [J].

Ahmadi, Mohammad Ali .

FLUID PHASE EQUILIBRIA, 2012, 314 :46-51

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