An Experimental Assessment of Model-Based Solvent Selection for Enhancing Reaction Kinetics

Scientific advances in the fields of chemistry and engineering have established the critical impact of solvents 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 such solvent selection currently. In this work, a methodology was developed to identify suitable solvents that will enhance the reaction rate of an industrial process at minimum experimental effort. For the rapid data generation and quantification, a modular continuous reactor coupled with real-time in situ analytics was set up. The data generation is guided by a computational method that consists of two steps: the formulation of a model identification problem based on the solvatochromic equation and the computer-aided screening of a solvent database that provides a list of the most promising solvents. The methodology is applied to the amination of ethyl trichloroacetate with liquefied ammonia, a reaction of industrial interest. Two of the predicted promising solvents are verified experimentally, demonstrating the predictive power of the methodology. Thus, a systematic model-based solvent selection methodology was demonstrated successfully to an industrially relevant reaction problem.