CO2 corrosion trend of natural gas based on its composition using Artificial Neural Networks

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Published: Jul 19, 2024
DOI: https://doi.org/10.29166/revfig.v18i2.5989
Keywords:
corrosion, prediction, natural gas, ANN, tendency

Main Article Content

Tomás Darío Marín-Velásquez
Universidad de Oriente. Maturín, Venezuela
https://orcid.org/0000-0002-3334-5895

Abstract

Corrosion is a recurrent problem in the natural gas industry, due to the presence of corrosive gases such as CO2 and H2S that in the presence of water can attack steel and produce damages and leaks that generate accidents and contamination, that is why the prediction of the tendency of natural gas towards corrosion is essential to develop strategies to inhibit and mitigate this process. The objective of the study was to develop an Artificial Neural Network (ANN) trained with composition data of natural gas samples to predict the corrosive tendency, as an alternative method that can be used as an efficient predictive tool. The ANN was trained with a sample of 46 natural gases and 11 components for each one, in addition to pressure and temperature as operational conditions, representing a total of 598 training data of the ANN, also 8 additional samples were used for external validation of the model. The ANN that developed was within the principles of Bayesian statistics, with a Multilayer Perceptron architecture. It was obtained that the ANN is able to correctly classify 95.65% of the corrosive tendency of the samples, being those with non-corrosive tendency the ones that presented the lowest prediction percentage (75%). The prediction of the external samples followed the expected trend, with lower capacity to classify the non-corrosive ones, and the samples with possible corrosion and corrosive were predicted in 80%. It is concluded that ANN is an efficient tool for predicting the corrosive tendency of natural gas in the presence of CO2 and its effectiveness depends on using samples with all the necessary components to train it, and it can be improved by introducing other parameters such as the amount of water present.

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How to Cite
Marín-Velásquez, T. D. (2024). CO2 corrosion trend of natural gas based on its composition using Artificial Neural Networks. FIGEMPA: Investigación Y Desarrollo, 18(2), 1–13. https://doi.org/10.29166/revfig.v18i2.5989
Issue
Vol. 18 No. 2 (2024): Innovation and Energy
Section
Artículos
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

https://creativecommons.org/licenses/by/4.0/

Author Biography

Tomás Darío Marín-Velásquez, Universidad de Oriente. Maturín, Venezuela

Universidad de Oriente. Unidad de Postgrado. 6201. Maturín, Estado Monagas, Venezuela

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