Predicting Water Content of High CO2 Content Natural Gas by Artificial
Neural Network

doi:10. 3863/j. issn. 1674 – 5086. 2013. 04. 017

Abstract

Abstract:

In this paper，a new method based on artificial neural network（ANN）for prediction of natural gas mixture water
content is presented. CO2 mole fraction，temperature，and pressure have been input variables of the network and water content
has been set as network output. The proposed ANN model is able to estimate water content as a function of CO2 composition up
to 70%，temperature between 20.0200.0 ℃ and pressure from 0.1 to 70.0 MPa. Comparisons show average absolute relative
error equal to 1.275%between ANN estimations and experimental data，which is smaller than the other three commonly used
empirical correlations. Furthermore，there is considerable deviation between experimental data and the other three commonly
used empirical correlations for prediction of high CO2 content natural gas water content. But artificial neural network has good
prediction results in high CO2 content natural gas. Results show ANN superiority to the common three correlations in literatures.

Key words: artificial neural network, high CO2 content, natural gas, water content

Hou Dali1, Sun Lei1, Pan Yi1, Qin Shanyu1, Dong Weijun2. Predicting Water Content of High CO2 Content Natural Gas by Artificial
Neural Network[J]. 西南石油大学学报(自然科学版), DOI: 10. 3863/j. issn. 1674 – 5086. 2013. 04. 017.

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