Prediction of mixtures in computer-aided molecular and process design
Final Report Abstract
Computer-aided molecular and process design (CAMPD) aims to find the optimal processing materials together with the optimal process configuration. Examples for processing materials that significantly influence the performance of the process but do not appear in feed or product streams are the working fluids in heat pumps, refrigerants, and solvents in processes like gas absorption. To be able to optimize the molecules in such processes, predictive models for thermodynamic properties are required that are applicable to the entire molecular design space. In this project, two modelling approaches, homosegmented and heterosegmented group contribution methods, are investigated and enhanced with their applicability to for CAMPD of mixtures. For homosegmented group contribution methods, the challenge addressed is the prediction of binary interaction parameters needed to accurately predict the thermodynamic behavior of mixtures. Heterosegmented group contribution methods promise a better description of mixtures as the heterogeneity of molecules is resolved on a finer level. The challenge here is the representation of the discrete molecular structure in an optimization problem. Both modelling approaches are implemented in a CAMPD framework that can solve the integrated design problem for arbitrary process models. As demonstration, the framework is used to find optimal mixed working fluids for an organic Rankine cycle. Overall, this project contributes to improving the thermodynamic foundations of integrated molecular and process optimizations for fluid mixtures with the aspiration to help identify more efficient and thus more sustainable process solutions.
Publications
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Molecule superstructures for the integrated design of processes and molecules, Thermodynamik-Kolloquium 2022, Chemnitz
P. Rehner, J. Schilling & A. Bardow
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FeOs: An Open-Source Framework for Equations of State and Classical Density Functional Theory. Industrial & Engineering Chemistry Research, 62(12), 5347-5357.
Rehner, Philipp; Bauer, Gernot & Gross, Joachim
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From molecules to phase diagrams: Three ways of predicting mixture properties using PC-SAFT, 16th International Conference on Properties and Phase Equilibria for Product and Process Design (PPEPPD) 2023
P. Rehner
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Large-scale parametrization of binary VLE using PC- SAFT, Thermodynamik-Kolloquium 2023, Hannover
P. Rehner, A. Bardow & J. Gross
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Modeling Mixtures with PCP-SAFT: Insights from Large-Scale Parametrization and Group-Contribution Method for Binary Interaction Parameters. International Journal of Thermophysics, 44(12).
Rehner, Philipp; Bardow, André & Gross, Joachim
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Molecule design beyond group counts – Integrated design of processes and molecule superstructures, 33rd European Symposium on Computer-Aided Process Engineering (ESCAPE) 2023
P. Rehner, J. Schilling & A. Bardow
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Molecule superstructures for computer-aided molecular and process design. Molecular Systems Design & Engineering, 8(4), 488-499.
Rehner, Philipp; Schilling, Johannes & Bardow, André
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Speaking the language of molecules: How natural language processing can predict PC-SAFT parameters, 16th International Conference on Properties and Phase Equilibria for Product and Process Design (PPEPPD) 2023
B. Winter, P. Rehner, J. Schilling & A. Bardow
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Computer-aided mixture design using molecule superstructures. Computers & Chemical Engineering, 201, 109232.
Rehner, Philipp; Schilling, Johannes & Bardow, André
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Understanding the language of molecules: predicting pure component parameters for the PC-SAFT equation of state from SMILES. Digital Discovery, 4(5), 1142-1157.
Winter, Benedikt; Rehner, Philipp; Esper, Timm; Schilling, Johannes & Bardow, André
