Integrated Design of Solvents and Processes based on Reaction Kinetics from Quantum Chemical Prediction Methods

Abstract

The choice of the employed solvent often strongly influences the performance of chemical processes. To obtain optimal process designs, we propose a method for the integrated in silico design of solvents and reaction-based processes. The search space of possible solvent molecules is explored by a genetic optimization algorithm which is directly linked to gradient-based process optimization. Thereby, the process performance of the designed solventis evaluated. While most approaches for such integrated design problems are based on group contribution methods and limited to equilibrium properties, we here propose a quantum mechanics-based approach to capture reaction kinetics. The integrated design method is successfully applied to the design of solvent and process for a carbamate cleavage reaction. The presented method allows for efficient design of a large number of promising solvents within the integrated reaction solvent and process design.