Journal
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
Volume 136, Issue -, Pages 78-87Publisher
ELSEVIER
DOI: 10.1016/j.petrol.2015.11.002
Keywords
Hydrate formation pressure; Binary mixtures; Artificial neural network; CSMHYD program
Categories
Ask authors/readers for more resources
In the present study, we investigated forecasting of hydrate formation pressure of binary mixtures including at least one hydrocarbon using a feed-forward multi-layer artificial neural network. For this purpose, 895 experimental data which cover a wide range of temperatures and compositions were collected from different studies cited in the literatures. In order to find the best model, different ANN types are tested through the absolute average relative deviation percent (AARD), mean square error (MSE) and the regression coefficient (R-2) and the optimal configuration is selected. It is found that the selected ANN model is based on the statistical analysis has an excellent agreement (AARD=1.02, MSE=1.27 x 10(-5) and R-2=0.9938) with the collected experimental data. The obtained results reveal that the developed MLPNN model is an applicable and feasible tool to predict hydrate formation pressure with high accuracy with respect to Colorado School of Mines Hydrate (CSMHYD) Program. (C) 2015 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available