2023/11/20 by Philipp Rehner, André Bardow, Joachim Groß +1
Chemistry · Engineering · Mathematics · #Binary number #Bubble #Bubble point #Chemical Thermodynamics and Molecular Structure #Component (thermodynamics) #Critical point (mathematics) #Dew point #Group contribution method #Intermolecular force #Materials science #Mathematical analysis #Mathematics #Mechanics #Molecule #Parametrization (atmospheric modeling) #Phase (matter) #Phase Equilibria and Thermodynamics #Phase equilibrium #Physics #Process Optimization and Integration #Statistical physics #Thermodynamics #Work (physics)
paper · pdf · doi:10.1007/s10765-023-03290-3
crossref issued 2023/11/20 · crossref published 2023/11/20 · crossref published-online 2023/11/20 · openalex publication_date 2023/11/20 · crossref created 2023/11/20 · crossref deposited 2023/11/30 · crossref published-print 2023/12/01 · openalex created_date 2025/10/10 · crossref indexed 2026/08/05 · openalex updated_date 2026/08/06
Abstract Vapor/liquid equilibria of mixtures are of utmost importance in the design of chemical processes. Because of the combinatorial complexity of mixtures, the available experimental data are small when considering the large molecular space. To fill this knowledge gap, molecular equations of state like PCP-SAFT show promise due to their explicit consideration of intermolecular interactions that can be transferred to mixtures. In this work, we comprehensively assess and exploit PCP-SAFT for modeling phase equilibria of mixtures. First, we provide binary interaction parameters for 7861 binary systems for which pure-component parameters and experimental data are available. Bubble and dew point pressures are described with a median deviation of 2.3 %. Secondly, we adjust a matrix of binary group/group interaction parameters for the homosegmented and heterosegmented group-contribution (GC) methods for PCP-SAFT. Among 1389 mixtures that can be described with the GC methods, the median deviation in bubble and dew point pressures are 6.4 % for the homosegmented approach and 5.1 % for the heterosegmented approach. The detailed analysis shows the importance of hydrogen bonds in mixtures of non-self-associating components with self-associating components. The parametrization is only possible by introducing a fast numerical method to calculate the derivative of bubble and dew point pressures with respect to arbitrarily many model parameters. The approach leverages reverse mode automatic differentiation (backpropagation), the same method used in machine learning to regress millions of model parameters to large datasets.