Predicting Natural Gas Hydrate Formation Temperature Using Levenberg-Marquardt Algorithm

Amin, Javad Sayyad; Bahadori, Alireza; Mohamadi, E; Nia, B Hoseini

doi:10.1080/10916466.2015.1038568

Cited by 7 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ACE model produces better results than Adisasmito et al, CSMGem where log The coefficients of these correlations are indicated by A-H, a-d, and y. The detailed values of model coefficients refer to [10][11][12][13][14][15][16][17][18].…”

Section: Discussionmentioning

confidence: 95%

“…Up to now, various approaches have been presented to determine hydrate phase equilibrium pressure or temperature. These approaches can be mainly divided into five types: laboratory tests [6,7], graphical methods [8,9], empirical correlations [10][11][12][13][14][15][16][17][18], thermodynamic models [19][20][21][22][23][24][25][26], and artificial intelligence algorithms [27][28][29][30]. It is well known that the experimental determination of the hydrate formation phase equilibrium conditions is expensive and time-consuming, which limit the application of this 2 Mathematical Problems in Engineering method.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Graphical Alternating Conditional Expectation to Predict Hydrate Phase Equilibrium Conditions for Sweet and Sour Natural Gases

Zhong

Yao

Wang³

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Natural gas hydrate has been widely of concern due to its great potential in application to address problems including gas storage, transmission, separation techniques, and also as energy resource. Accurate prediction of hydrate formation phase equilibrium conditions is essential for the optimized design during natural gas production, processing, and transportation. In this study, a novel graphical alternating conditional expectation (ACE) algorithm was proposed to predict hydrate formation phase equilibrium conditions for sweet and sour natural gases. The accuracy and performance of the presented ACE model were evaluated using 1055 data points (688, 249, and 118 data points for sweet natural gas, CO2-CH4, and H2S-CO2-CH4 systems, respectively) collected from literature. Meanwhile, a comparative study was conducted between the ACE model and commonly used correlations, including thirteen models for sweet natural gases, three models for CO2-CH4 binary system, and seven thermodynamic models for H2S-CO2-CH4 ternary system. The obtained results indicated that the proposed ACE model produces the best results in prediction of hydrate phase equilibrium temperature for sweet natural gases and pressure for CO2-CH4 system with average absolute relative deviation (AARD) of 0.134% and 2.75%, respectively. The proposed quick and explicit ACE model also provides a better performance in prediction of hydrate phase equilibrium pressure for H2S-CO2-CH4 ternary systems with AARD=5.20% compared with seven thermodynamic methods considered in this work, except for CPA/Electrolyte/Chen–Guo combined model (AARD=4.45%).

show abstract

Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

A Graphical Alternating Conditional Expectation to Predict Hydrate Phase Equilibrium Conditions for Sweet and Sour Natural Gases

Zhong

Yao

Wang³

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…There are two widely used correlations for rapidly estimating the hydrate phase equilibrium conditions, both are attributed to Katz and co-workers, that is, the gas gravity method and K-factor method. 16,17 Many hydrate formation prediction correlations based on the gas gravity method could be found in literature, such as Hammerschmidt, 18 Makogon, 19 Kummamuru, 20 Motiee, 21 Towler and Mokhatab, 22 Bahadori and Vuthaluru, 4 Amin, 23 and Ghayyem. 24 However, the gravity method just work only in a particular range of gas gravity, pressure, and temperature.…”

Section: Introductionmentioning

confidence: 99%

Twin Support Vector Regression for Prediction of Natural Gas Hydrate Formation Conditions

2021

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Natural gas hydrates have become a threat to natural gas companies and oil industries for the formation of gas hydrates in transfer pipelines can lead to pipe blockage. Natural gas hydrate formation is favored by low temperature and high pressure; thus, if the right combination of temperature and pressure are well investigated, it is possible to solve the pipe blockage problems. Therefore, developing a precise, easy-to-use method to predict natural gas hydrate formation temperature (HFT) is very important. In this paper, we are intended to develop a novel machine learning model called twin support vector regression (TSVR) for predicting HFT in a wide range of natural gas mixtures. For constructing the TSVR model, 513 experimental data in the system methane, ethane, propane, butane, pentane, nitrogen, carbon dioxide/water, methane, carbon dioxide, propane, methanol, sodium chloride, and calcium chloride have been collected from open literature. We compared the performance of the TSVR model with least squares support vector regression (LSSVR) and three widely used correlations in the system methane, ethane, propane, butane, pentane, nitrogen, and carbon dioxide, which show that the TSVR model has fewer deviations than the LSSVR model and three correlations. The values of root-mean-square error (RMSE), mean absolute percentage error (MAPE), and correlation factor (R 2) are 2.4010, 0.0061, and 0.9108, respectively. In addition, statistical parameters show that the TSVR model can also surpass the LSSVR model in predicting HFT in the system water, methane, carbon dioxide, propane, methanol, sodium chloride, and calcium chloride.

show abstract

Prediction of hydrate formation temperature based on an improved empirical correlation by imperialist competitive algorithm

Amin

Nejad

Veiskarami

et al. 2016

Petroleum Science and Technology

View full text Add to dashboard Cite

Predicting Natural Gas Hydrate Formation Temperature Using Levenberg-Marquardt Algorithm

Cited by 7 publications

References 19 publications

A Graphical Alternating Conditional Expectation to Predict Hydrate Phase Equilibrium Conditions for Sweet and Sour Natural Gases

A Graphical Alternating Conditional Expectation to Predict Hydrate Phase Equilibrium Conditions for Sweet and Sour Natural Gases

Twin Support Vector Regression for Prediction of Natural Gas Hydrate Formation Conditions

Prediction of hydrate formation temperature based on an improved empirical correlation by imperialist competitive algorithm

Contact Info

Product

Resources

About