Development of on-demand critically evaluated thermophysical properties data in process simulation

Abstract

Accurate thermophysical properties are essential to the development of high-quality process simulation models of chemical processes. Therefore, process-modeling software (simulator) must provide accurate, reliable, and easily accessible property data and models to enable efficient and robust process design. Property data and parameters for components of interest are generally available in the databases of the simulator. For components that are not in the databases, their property data must be supplied by the user. The number of components available in a typical simulator is about 1700. The number and types of components available in the simulator limit the scope and accuracy of process models that can be developed. In this paper, we review past practice in obtaining the necessary property data required in developing a process model and describe a new methodology that can be used to overcome the shortcomings of the current method. The new method is based on the dynamic data evaluation concept that combines the experimental data obtained from a comprehensive electronic database with structure-based property estimation system and data analysis and regression programs to generate critically evaluated property data. The concept and necessary software have been implemented in a process simulator, resulting in a new workflow that enables high-fidelity process models to be developed more easily and efficiently.