Predicting Water Content Outcomes in Natural Gas Dehydration Systems Using Artificial Intelligence
|International Journal of Chemical Engineering Research|
|© 2021 by SSRG - IJCER Journal|
|Volume 8 Issue 1|
|Year of Publication : 2021|
|Authors : Olayemi Kehinde Miracle, Oduola Mujeeb Koyejo|
How to Cite?
Olayemi Kehinde Miracle, Oduola Mujeeb Koyejo, "Predicting Water Content Outcomes in Natural Gas Dehydration Systems Using Artificial Intelligence," SSRG International Journal of Chemical Engineering Research, vol. 8, no. 1, pp. 15-21, 2021. Crossref, https://doi.org/10.14445/23945370/IJCER-V8I1P103
This paper offers a comprehensive evaluation aimed at predicting the water content outcome of natural gas systems using artificial intelligence. In this study, an artificial intelligence model- a three layer artificial neural network model- has been developed using past gas dehydration process data to predict the water content outcomes in natural gas dehydration systems. The water content outcomes are Class 0 which represent data points that meet the water content specification of 7 lb/MMscf or Class 1, which do not. The input features of the model are temperature of the reboiler in ºF, stripping gas flow rate in scf/gal triethylene glycol (TEG), number of equilibrium stages in the contactor, and TEG circulation rate in gal TEG/lb H2O. An exploratory data analysis was carried out on the training data and the optimum process parameters found are TEG recirculation rates between 3.2 and 3.8 gal TEG/lb H20, reboiler temperatures between 380ºF and 400ºF, stripping rate of 0 – 3 scf/gal TEG, and two and three equilibrium stage contactors. The model was evaluated against test data and experimental data from literature and F1 scores of 0.969 and 0.987 were obtained respectively. This showed that the model was able to predict correctly the expected water content outcomes of new gas dehydration data points.
Natural gas dehydration, water content, artificial intelligence, artificial neural network, modelling.
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