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Dissertation, RWTH Aachen University, 2018; Aachen 1 Online-Ressource (XIII, 146 Seiten) : Illustrationen, Diagramme (2018). doi:10.18154/RWTH-2018-223767 = Dissertation, RWTH Aachen University, 2018, Scientific advances in the field of chemistry have established that solvents have a critical impact on the rate of a wide array of chemical reactions. This has triggered the interest of academia and industry for the search of solvents that optimize reaction kinetics. Yet, there are only a few systematic approaches to guide solvent selection in this direction and to date, a systematic model-based approach that considers direct applicability to industrial reaction problems has not been reported. In this study, a hybrid experimental/model-based solvent selection methodology built around the solvatochromic equation is established for the prediction of the best solvent(s) for an industrial chemical process at minimum experimental effort. For the rapid data generation and quantification, a modular continuous reactor coupled with real-time analytics has been set up. The solvatochromic equation was used to model the solvent effects on the reaction rates. The solvents that were experimentally investigated were selected from a solvent database consisting of known organic solvents and structures generated with the aid of CAMD techniques which was further refined to meet the specific reaction requirements, environmental and health constraints and equipment limitations. The selection was diversity-oriented, aiming at the acquisition of the maximum possible information at the least experimental effort. The methodology was applied on the amination of ethyl trichloroacetate with liquefied ammonia, a reaction of industrial interest. Two of the predicted promising solvents were verified experimentally, demonstrating the predictive ability of the methodology. The established methodology can be used as a starting point for further improvement. Inclusion of ionic liquids, supercritical fluids and structures generated with the aid of Computer Aided Molecular Design (CAMD) techniques in the solvent database, may reveal new promising solvent candidates, opening new windows in chemical synthesis. Coupling with model identification and discrimination techniques can further minimize the experimental effort involved. The current approach deals with the enhancement of the rate of the reaction leading to the desired products and the reduction of the rate of the side reactions as two independent objectives. Future research could be dedicated into treating the two objectives as a unified one., Published by Aachen