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Full Description
Diffusion and liquid-liquid equilibria (LLE) data are of major importance for the design of chemical processes as, e.g., extraction. Unfortunately, predictive equations for diffusion and LLE are not yet sufficiently accurate. The development of predictive equations suffers both from complex molecular interactions that are not fully understood and from a lack of experimental data that would be needed for validation. The reason for the lack of experimental data is that diffusion and LLE measurements are burdened with large experimental effort and high sample consumption.
In this work, we address the need for improved diffusion predictions and experiments as well as for improved LLE experiments. For this purpose, a model-based approach is combined with computer experiments and the development of new measurement methods. The work was conducted in strong collaborations with TU Delft, FAU Erlangen-N¨urnberg, and RWTH Aachen University.
The integration of the model-based approach into the development of measurement methods allows for the thorough evaluation of the quality of experimental results. Thereby, necessary improvements in model and experiment are identified and implemented. As a result, effective and consistent diffusion experiments for liquids and gases are developed. The new experiments allow for a significant reduction of measurement times and sample consumption by an order of magnitude.
In addition, optimal experimental designs (OED) for diffusion and LLE experiments are investigated. OED identifies experiments that provide most information on the property of interest. Thereby, the number of experiments can be reduced without losing accuracy. We identify OEDs for diffusion and LLE experiments that can reduce the experimental effort by an order of magnitude. In addition, we identify optimal combinations of diffusion and LLE experiments for the economic design of extraction processes.
Thus, the contribution of this thesis is two-fold: On the one hand, efficient experimental designs and setups are developed that allow for a significant reduction of experimental effort. On the other hand, the demonstration of the successful application of the model-based approach can serve as a motivation for an increased integration of models and experimental setups in future developments of experiments.
