Next Generation of Thermodynamic Models and their Impact on Process Modeling and Simulation

Process industries are always looking for ways to improve design and operation mainly in terms of profitability and safety while maintaining certain constraints which are typically process specific. Rigorous process optimization requires high-fidelity predictive mathematical models based on first-principles. Accurate thermodynamic predictions are a key requirement for high-fidelity predictive mathematical models. In additional to this, high degree of predictability and robustness, perceived in this case as the ability to perform reliable calculations over a range of thermodynamic conditions, is imperative for process safety analysis.

Recent advances in thermodynamic property prediction have led to the development of methodologies that can be reliably applied to the modeling of complex, highly non-ideal systems and over a wide range of conditions. As an example of such a methodology, in this presentation we focus on the implementation of the SAFT- Mie group contribution approach within an advanced process modeling platform. The physical basis of this methodology lends SAFT- Mie with the ability to accurately describe properties of highly non-ideal pure components and mixtures, and the formulation within the scope of group-contribution allows for property prediction where little, or no, experimental data is available. In this presentation, the fundamentals of this thermodynamic methodology and its implementation within an advanced process modeling environment will be briefly discussed. The performance of the theory in describing the properties of several examples of complex molecules will be demonstrated. Finally, the benefits of integrating this approach within a process simulation environment will be illustrated through different case studies.