This project seeks to identify optimal thermodynamic models through systematic comparison and improvement, targeting industrial application for enhanced efficiency and effectiveness.
This project addresses the persistent challenge in thermodynamic modeling: identifying the most effective model for diverse systems.
Despite numerous models available, determining the optimal one for specific conditions remains unresolved.
The hypothesis is that a comprehensive comparison of models can elucidate their performance and suitability across various systems.
This gap in understanding is particularly evident in scenarios where traditional models fail to predict phenomena like azeotropes formed when esters or alcohols are added to separate propane from light hydrocarbons in the petrochemical industry.
Such failures increase costs due to the need for extensive trial and error experimentation or result in products not meeting purity requirements.
The project's significance lies at the intersection of academia and industry, catering to sectors increasingly inclined towards sustainability.
The lack of clarity on effective thermodynamic models impedes progress in achieving sustainability goals.
Bridging this gap by providing simulation and optimization models’ whitebook aims to promote molecular thermodynamic models’ widespread adoption in industrial applications.
Presently, the absence of such comparative analyses makes it challenging to integrate sophisticated modeling techniques into practical industrial operations smoothly.
The project's objectives include a comprehensive comparison between thermodynamic models to realize weaknesses and characterization of domain factors in special conditions.
Fundamental improvements of some derivations like dP/dV, dP/dT, dP/dV lead to accurate estimations of first and second-order properties such as heat capacity, speed of sound, and surface tension.
By systematically comparing models and developing predictive techniques, the project aims to enhance industrial applications' efficiency and effectiveness in thermodynamic modeling.
Main supervisor:
Assoc. Prof., Xiaodong Liang
Co- supervisor:
Prof., Georgios Kontogeorgis