Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature

2017 ◽  
Vol 25 (9) ◽  
pp. 1238-1248 ◽  
Author(s):  
Mohammad Mesbah ◽  
Ebrahim Soroush ◽  
Mashallah Rezakazemi
Keyword(s):  
Support Vector Machine ◽  
Natural Gas ◽  
Gas Hydrate ◽  
Support Vector Machine Model ◽  
Hydrate Formation ◽  
Support Vector ◽  
Formation Temperature ◽  
Natural Gas Hydrate ◽  
Machine Model ◽  
Gas Hydrate Formation

Progress in use of surfactant in nearly static conditions in natural gas hydrate formation

2020 ◽  
Vol 14 (3) ◽  
pp. 463-481
Author(s):  
Zhen Pan ◽  
Yi Wu ◽  
Liyan Shang ◽  
Li Zhou ◽  
Zhien Zhang
Keyword(s):  
Natural Gas ◽  
Gas Hydrate ◽  
Hydrate Formation ◽  
Natural Gas Hydrate ◽  
Gas Hydrate Formation ◽  
Static Conditions

scholarly journals Integrating Support Vector Regression with Genetic Algorithm for Hydrate Formation Condition Prediction

Processes ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 519
Author(s):  
Jie Cao ◽  
Shijie Zhu ◽  
Chao Li ◽  
Bing Han
Keyword(s):  
Genetic Algorithm ◽  
Natural Gas ◽  
Gas Hydrate ◽  
Hydrate Formation ◽  
Support Vector ◽  
Gas Hydrate Formation ◽  
Svm Model ◽  
Data Points ◽  
Formation Conditions ◽  
Relative Molecular Weight

To predict the natural gas hydrate formation conditions quickly and accurately, a novel hybrid genetic algorithm–support vector machine (GA-SVM) model was developed. The input variables of the model are the relative molecular weight of the natural gas (M) and the hydrate formation pressure (P). The output variable is the hydrate formation temperature (T). Among 10 gas samples, 457 of 688 data points were used for training to identify the optimal support vector machine (SVM) model structure. The remaining 231 data points were used to evaluate the generalisation capability of the best trained SVM model. Comparisons with nine other models and analysis of the outlier detection revealed that the GA-SVM model had the smallest average absolute relative deviation (0.04%). Additionally, the proposed GA-SVM model had the smallest amount of outlier data and the best stability in predicting the gas hydrate formation conditions in the gas relative molecular weight range of 15.64–28.97 g/mol and the natural gas pressure range of 367.65–33,948.90 kPa. The present study provides a new approach for accurately predicting the gas hydrate formation conditions.

Evolving a robust modeling tool for prediction of natural gas hydrate formation conditions

2015 ◽  
Vol 12 ◽  
pp. 45-55 ◽  
Author(s):  
Ebrahim Soroush ◽  
Mohammad Mesbah ◽  
Amin Shokrollahi ◽  
Jake Rozyn ◽  
Moonyong Lee ◽  
...  
Keyword(s):  
Natural Gas ◽  
Gas Hydrate ◽  
Hydrate Formation ◽  
Natural Gas Hydrate ◽  
Modeling Tool ◽  
Robust Modeling ◽  
Gas Hydrate Formation ◽  
Formation Conditions
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