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Integration of modern computational chemistry and ASPEN PLUS for chemical process design
Author(s) -
Tsai ChangChe,
Lin ShiangTai
Publication year - 2020
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16987
Subject(s) - acentric factor , thermodynamics , chemistry , vapor pressure , enthalpy of vaporization , boiling point , vaporization , enthalpy , ideal gas , equation of state , process (computing) , process engineering , organic chemistry , computer science , physics , operating system , engineering
Thermodynamic properties and fluid phase equilibria are crucial for the design and development of a chemical process. However, such data may not always be available, particularly for fine or specialty chemicals. In this work, we evaluate the reliability of using modern computational chemistry combined with recently developed predictive thermodynamic models to provide all the thermodynamic properties required in process design with ASPEN PLUS. Specifically, the G3 method is used for the ideal gas heat capacities and properties of formation, and the PR+COSMOSAC equation of state and COSMO‐SAC activity coefficient model are utilized for the properties and phase behaviors of pure and mixture fluids. These methods are chosen because they do not require any species‐dependent parameters and can, in principle, be applied to any chemical species. For a set of 972 chemicals, it is found that most properties can be predicted with a satisfactory accuracy (less than 10%: critical temperature [5%], critical pressure [10%], critical volume [5%], constant pressure ideal gas heat capacity [5%], and heat of vaporization [10%], except for the acentric factor [33%] and vapor pressure [73%]). Furthermore, the predicted results show little bias suggesting that these theoretically based methods are reliable for new chemicals for which experimental data are not yet available. Our analyses show that better accuracy in the prediction of vapor pressure and formation enthalpy and free energy is necessary for the design of chemical processes without relying on any experimental input. Nonetheless, these methods often provide reliable relative property values (e.g., relative value of normal boiling temperature can be predicted with 94% accuracy), making it possible to screen for new chemicals for improving existing processes.