液化气脱硫脱硫醇反应动力学模型构建与验证

Key Points

• A high-precision kinetic model for desulfurization demonstrates no systematic bias, ensuring accuracy in predicting outcomes.
• Data from various temperatures, pressures, and oxidant concentrations were used to derive conversion rates crucial to the model.
• The model combines traditional rate equations with neural networks for enhanced parameter estimation effectiveness.
• Findings address potential error sources, such as mass transfer limitations, providing foundational data for future industrial applications.

Abstract