Journal article
Evolving a robust modeling tool for prediction of natural gas hydrate formation conditions
Journal of Unconventional Oil and Gas Resources, Vol.12, pp.45-55
2015
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Source: InCites
Abstract
<p><p id="x-x-sp0010">Natural gas is a very important energy source. The production, processing and transportation of natural gas can be affected significantly by gas hydrates. Pipeline blockages due to hydrate formation causes operational problems and a decrease in production performance. This paper presents an improved artificial neural network (ANN) method to predict the hydrate formation temperature (HFT) for a wide range of gas mixtures. A new approach was used to define the variables for formation of a hydrate structure according to each species presented in natural gas mixtures. This approach resulted in a strong network with a precise prediction, especially in the case of sour gases. <p id="x-x-sp0015">This study also presents a detailed comparison of the results predicted by this ANN model with those of other correlations and thermodynamics-based models for an estimation of the HFT. The results showed that the proposed ANN model predictions are in much better agreement with the experimental data than the existing models and correlations. Finally, outlier detection was performed on the entire data set to identify any defective measurements of the experimental data.</p>
Details
- Title
- Evolving a robust modeling tool for prediction of natural gas hydrate formation conditions
- Creators
- Ebrahim Soroush - Sahand University of TechnologyMohammad Mesbah - Sharif University of TechnologyAmin Shokrollahi - Sharif University of TechnologyJake Rozyn - Southern Cross UniversityMoonyong Lee - Yeungnam UniversityTomoaki Kashiwao - National Institute of Technology, JapanAlireza Bahadori - Southern Cross University
- Publication Details
- Journal of Unconventional Oil and Gas Resources, Vol.12, pp.45-55
- Identifiers
- 3651; 991012822099802368
- Academic Unit
- School of Environment, Science and Engineering; Faculty of Science and Engineering
- Resource Type
- Journal article