Computing Entropy for Long-Chain Alkanes Using Linear Regression: Application to Hydroisomerization

Area of Science:

• Chemical thermodynamics
• Catalysis
• Computational chemistry

Background:

• Accurate thermochemical properties are crucial for chemical process design.
• Existing methods for calculating alkane entropies have limitations for longer chains.
• Zeolite-catalyzed hydroisomerization is an important industrial process.

Purpose of the Study:

• To compute entropies for alkane isomers longer than C10 using a novel linear regression model.
• To investigate entropy production and heat input during zeolite-catalyzed hydroisomerization.
• To assess the impact of chain length and temperature on these properties.

Main Methods:

• Developed and applied a second-order group contribution-based linear regression model for thermochemical properties.
• Calculated entropy production and heat input for hydroisomerization of C7 isomers in various zeolites at 500 K.
• Studied the effect of chain length (C7-C14) and temperature on hydroisomerization in MTW-type zeolite.

Main Results:

• Computed alkane entropies showed excellent agreement with experimental data and established correlations.
• Entropy production and heat input varied slightly across different zeolite structures.
• Both heat input and entropy production increased with longer alkane chains and higher temperatures.

Conclusions:

• The linear regression model provides accurate entropy calculations for alkanes.
• Findings offer valuable insights for optimizing zeolite-catalyzed hydroisomerization processes.
• Understanding thermochemical properties is key for designing efficient catalytic systems.