Cryogenic Process Optimization for Natural Gas Purification: Predictive Modeling of Methane-CO₂ Solid-Vapor Phase...

Area of Science:

• Chemical Engineering
• Industrial Engineering
• Thermodynamics

Background:

• Natural gas processing requires efficient carbon dioxide (CO2) separation.
• Cryogenic conditions present unique challenges for CO2 capture due to phase behavior.
• Accurate phase equilibrium data is crucial for designing effective industrial separation processes.

Purpose of the Study:

• To develop and validate a predictive model for Solid-Vapor (S-V) phase equilibrium in methane-carbon dioxide (CH4-CO2) systems under cryogenic conditions.
• To enhance operational insights for industrial CO2 capture processes.
• To predict process conditions for achieving high methane (CH4) purity.

Main Methods:

• Application of Response Surface Methodology (RSM) for phase behavior modeling.
• Investigation of the Solid-Vapor (S-V) phase equilibrium in a CH4-CO2 system.
• Validation of the developed RSM model using experimental data.

Main Results:

• A robust RSM model was developed to predict phase behavior in cryogenic CO2 capture.
• The model accurately predicts mole fractions of CH4 and CO2 in the S-V region.
• Validated model shows good agreement with previous experimental studies.

Conclusions:

• The validated RSM model provides valuable predictions for cryogenic CO2 capture processes.
• The model aids in optimizing industrial gas separation by forecasting unknown conditions.
• This research advances the understanding and optimization of natural gas processing.