TY - JOUR
T1 - Modeling of hydrate formation prediction in binary components of natural gas
AU - Abbasi, Aijaz
AU - Hashim, Fakhruldin Mohd
AU - Machmudah, Affiani
N1 - Publisher Copyright:
© 2022 Taylor & Francis Group, LLC.
PY - 2022
Y1 - 2022
N2 - Temperature is calculated as a function of gas gravity and pressure using an exponential function with two constant parameters, a and b. To obtain the best prediction model of gas hydrate formation, the behavior of these parameters in response to changes in gas gravity is monitored. Methane-ethane, methane-propane, ethane-propane, and ethane-carbon dioxide are among the binary components to which the suggested model is applied. The suggested predictive model outperforms the existing correlation approaches, such as Hammerschmidt, Motiee, and Ghiasi correlations, according to statistical analysis. The type of gases that make up the hydrate has a big impact on the gas hydrate equilibrium line, and the predictive model's constant values are different for each binary component. As a result, this study indicates that rather than constructing an empirical correlation-based on the assumption that the specific gas gravity is a general characteristic independent of the kind of gas hydrate mixture, a predictive model should be established for each gas hydrate mixture.
AB - Temperature is calculated as a function of gas gravity and pressure using an exponential function with two constant parameters, a and b. To obtain the best prediction model of gas hydrate formation, the behavior of these parameters in response to changes in gas gravity is monitored. Methane-ethane, methane-propane, ethane-propane, and ethane-carbon dioxide are among the binary components to which the suggested model is applied. The suggested predictive model outperforms the existing correlation approaches, such as Hammerschmidt, Motiee, and Ghiasi correlations, according to statistical analysis. The type of gases that make up the hydrate has a big impact on the gas hydrate equilibrium line, and the predictive model's constant values are different for each binary component. As a result, this study indicates that rather than constructing an empirical correlation-based on the assumption that the specific gas gravity is a general characteristic independent of the kind of gas hydrate mixture, a predictive model should be established for each gas hydrate mixture.
KW - Binary components
KW - clean energy
KW - gray wolf optimizer
KW - hydrate formation
KW - predictive analytics
KW - thermodynamic
UR - http://www.scopus.com/inward/record.url?scp=85125175826&partnerID=8YFLogxK
U2 - 10.1080/10916466.2022.2034854
DO - 10.1080/10916466.2022.2034854
M3 - Article
AN - SCOPUS:85125175826
SN - 1091-6466
VL - 40
SP - 2025
EP - 2037
JO - Petroleum Science and Technology
JF - Petroleum Science and Technology
IS - 16
ER -