A computational intelligence scheme for prediction equilibrium water dew point of natural gas in TEG dehydration systems

被引:64
作者
Ahmadi, Mohammad Ali [1 ]
Soleimani, Reza [2 ]
Bahadori, Alireza [3 ]
机构
[1] Petr Univ Technol, Dept Petr Engn, Ahwaz Fac Petr Engn, Tehran, Iran
[2] Petr Univ Technol, Dept Gas Engn, Ahwaz Fac Petr Engn, Ahvaz, Iran
[3] So Cross Univ, Sch Environm Sci & Engn, Lismore, NSW 2480, Australia
关键词
Gas dehydration; Triethylene glycol; Equilibrium water dew point; Particle swarm optimization; Artificial neural network; ARTIFICIAL NEURAL-NETWORK; VAPOR-LIQUID-EQUILIBRIUM; PARTICLE SWARM OPTIMIZATION; BINARY-SYSTEMS; ASPHALTENE PRECIPITATION; CARBON-DIOXIDE; PERMEATE FLUX; ALGORITHMS; VLE;
D O I
10.1016/j.fuel.2014.07.072
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
Raw natural gases are frequently saturated with water during production operations. It is crucial to remove water from natural gas using dehydration process in order to eliminate safety concerns as well as for economic reasons. Triethylene glycol (TEG) dehydration units are the most common type of natural gas dehydration. Making an assessment of a TEG system takes in first ascertaining the minimum TEG concentration needed to fulfill the water content and dew point specifications of the pipeline system. A flexible and reliable method in modeling such a process is of the essence from gas engineering view point and the current contribution is an attempt in this respect. Artificial neural networks (ANNs) trained with particle swarm optimization (PSO) and back-propagation algorithm (BP) were employed to estimate the equilibrium water dew point of a natural gas stream with a TEG solution at different TEG concentrations and temperatures. PSO and BP were used to optimize the weights and biases of networks. The models were made based upon literature database covering VLE data for TEG-water system for contactor temperatures between 10 degrees C and 80 degrees C and TEG concentrations ranging from 90.00 to 99.999 wt%. Results showed PSO-ANN accomplishes more reliable outputs compared with BP-ANN in terms of statistical criteria. (C) 2014 Elsevier Ltd. All rights reserved.
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页码:145 / 154
页数:10
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