Integrated Symbolic Framework for Yang-Frotscher-Richter Model
Project conducted under Chemineers Society, IIT Kanpur
Project Mentors - Shaurya Srivastava & Tanvi Manhas
The pursuit of high-fidelity thermophysical property modeling has long encountered a significant bottleneck in the accurate prediction of liquid phase densities through cubic equations of state (EoS). While historically preferred in industrial applications due to their computational efficiency and mathematical simplicity, traditional cubic EoS such as the Soave-Redlich-Kwong (SRK) and Peng-Robinson (PR) models often exhibit substantial systematic errors when applied to the liquid phase. These deviations are particularly problematic in the design and optimization of advanced energy systems, such as refrigeration cycles and lubricant-heavy industrial processes, where precise density and caloric data are essential for characterizing energy transfer and component efficiency. The emergence of the Yang-Frotscher-Richter (YFR) EoS represents a significant advancement in this domain, leveraging the power of symbolic regression to overcome the rigidity of fixed-parameter cubic models. By transforming the traditionally constant parameters of the Patel-Teja (PT) functional form into temperature-dependent variables, the YFR model achieves a level of volumetric accuracy previously reserved for far more complex multi-parameter models.
Based on implementation of - https://assets-eu.researchsquare.com/files/rs-5447725/v1_covered_31f105fd-397a-4a5d-8f9c-55cc4c85a617.pdf?c=1738598964