Biochemical Engineering: Molecular Thermodynamics in Insights from SAFT Equations of State

Thermodynamic modeling is foundational to biochemical engineering, enabling the prediction and optimization of complex fluid behavior, phase equilibria, and molecular interactions in systems ranging from protein solutions to industrial bioprocesses. The Statistical Associating Fluid Theory (SAFT) equation of state (EOS) and its variants, including PC-SAFT, SAFT-VR, SAFT-y-Mie, CPA, and PCP-SAFT, have emerged as powerful tools for modeling the intricate thermodynamics of biochemical systems. This review comprehensively examines the theoretical underpinnings of SAFT, its evolution through various formulations, and its application to biochemical engineering challenges such as protein phase separation, biomolecular interactions, and bioprocess design. Recent advancements, including machine learning-driven parameterization, surrogate modeling, and multiscale approaches, are highlighted alongside open-source computational tools. Case studies illustrate SAFT's utility in modeling protein solutions, amino acids, peptides, and pharmaceutical compounds. The review concludes by discussing current challenges, benchmarking practices, and future research directions, emphasizing the need for improved parameterization, uncertainty quantification, and integration with molecular simulations to further advance biochemical thermodynamic modeling.

کلیدواژه ها:

SAFT ، Molecular Thermodynamics ، Biochemical Engineering ، Equations of State ، PC-SAFT