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Full Description
Optimal performance of chemical processes requires both optimized operating conditions and carefully selected molecules such as solvents. However, the search for optimal molecules and process concepts often has a limited focus: Either processes are optimized using a pre-defined set of molecules or molecules are selected for novel applications based on simplified process indicators. At the same time, the search for optimal molecules often relies on strongly simplified thermodynamic models that require experimentally determined group interaction parameters and confine the molecular design space. Overall, current design approaches often do not capture complex process trade-offs and are limited to prescriptive sets of molecules which likely results in suboptimal choices.
To address the challenge of identifying optimal processes and molecules, this thesis presents an integrated computer-aided molecular and process design (CAMPD) approach. The design approach uses quantum mechanics (QM)-based property prediction by COSMO-RS and is thus independent of experimental determined group interaction parameters while not relying on group additivity. For reliable and fast evaluation of complex processes, advanced pinch-based process models are employed. These pinch-based process models account for the inherent trade-off in molecular properties while being both computationally efficient and accurate in comparison to rigorous process models. The integrated design approach in this thesis is stepwise extended from process-level molecular screenings towards molecular design for separation and reaction-separation processes. The application of the presented integrated design approach is illustrated for various examples of solvent selection and process optimization. In particular, process concepts and solvents are investigated for the purification of bio-based platform chemicals as well as the production of CO from CO2. Overall, this thesis successfully integrates COSMO-RS property prediction in CAMPD and thus significantly expands the range and applicability of current CAMPD approaches.
