Rigorous models to optimise stripping gas rate in natural gas dehydration units

•Multilayer perceptron (MLP) neural network is used to estimate optimum gas stripping rate in natural gas dehydration units.•Least squares support vector machine (LSSVM) algorithm is used to estimate optimum gas stripping rate.•Both models have been developed and tested using 150 series of the data....

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Bibliographic Details
Published in:Fuel (Guildford) 2015-01, Vol.140, p.421-428
Main Authors: Ghiasi, Mohammad M., Bahadori, Alireza, Zendehboudi, Sohrab, Chatzis, Ioannis
Format: Article
Language:English
Subjects:
Dehydration
LSSVM
Natural gas
Stripping gas
Triethylene glycol
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Summary:•Multilayer perceptron (MLP) neural network is used to estimate optimum gas stripping rate in natural gas dehydration units.•Least squares support vector machine (LSSVM) algorithm is used to estimate optimum gas stripping rate.•Both models have been developed and tested using 150 series of the data.•The results of the rigorous models show excellent agreement with data. Natural gas is an extremely vital supply of energy. Compared to other fossil fuels, demand for natural gas is experiencing a considerable growth due to its accessibility, availability, versatility and smaller environmental footprint. Glycol dehydration is the most common and economic method of water removal from natural gas streams. The water content of the dehydrated gas depends primarily on the lean triethylene glycol (TEG) concentration. Injecting stripping gas into the reboiler is taken into account as an effective way to improve the glycol concentration. In this article, two intelligent approaches including multilayer perceptron (MLP) neural network and least squares support vector machine (LSSVM) algorithm are utilized to predict optimum stripping gas flow rate in natural gas dehydration systems. Furthermore, a simple mathematical tool is presented for the application of interest. Based on the statistical analysis, an excellent match is noticed between the values obtained from the predictive tools (e.g., MLP, LSSVM and the empirical equations) and the real data so that the average absolute relative deviation percent (AARD %) is determined to be lower than 0.01%.
ISSN:0016-2361
1873-7153
DOI:10.1016/j.fuel.2014.09.084