The kinetics of chemical reactions in the liquid phase
are often
strongly determined by the reaction solvent. Consequently, the choice
of the optimal solvent is an important task in chemical process design.
Because of the vast number of potential solvents, experimental testing
of all candidates is infeasible. To explore the design space of possible
reaction solvents, computer-aided molecular design (CAMD) methods
have been developed. However, state-of-the-art CAMD methods for reaction
solvent design consider usually only a limited molecular design space
and rely on simplified models fitted to experimental data to predict
solvent performance. To overcome these limitations, we here propose
Rx-COSMO-CAMD as the method for the design of reaction solvents. Rx-COSMO-CAMD
combines CAMD using the genetic optimization algorithm LEA3D with
sound prediction of reaction kinetics based on transition-state theory
and advanced quantum chemical methods. Thereby, no experimental data
are required. The predictions are shown to be computationally efficient
and not limited to certain structural groups. Thus, large and diverse
molecular design spaces can be explored. To demonstrate the proposed
Rx-COSMO-CAMD method, we successfully design solvents, enhancing the
reaction kinetics of a Menschutkin reaction and a chain propagation
reaction for the production of polymers and microgels. The method
is shown to identify promising solvents for significant enhancement
of reaction rates. Rx-COSMO-CAMD is therefore a powerful, fully predictive
tool for the identification of optimal reaction solvents.
The chemistry of urethanes plays a key role in important industrial processes. Although catalysts are often used, the study of the reactions without added catalysts provides the basis for a deeper understanding. For the non‐catalytic urethane formation and cleavage reactions, the dominating reaction mechanism has long been debated. To our knowledge, the reaction kinetics have not been predicted quantitatively so far. Therefore, we report a new computational study of urethane formation and cleavage reactions. To analyze various potential reaction mechanisms and to predict the reaction rate constants quantum chemistry and transition state theory were employed. For validation, experimental data from literature and from own experiments were used. Quantitative agreement of experiments and predictions could be demonstrated. The calculations confirm earlier assumptions that urethane formation reactions proceed via mechanisms where alcohol molecules act as auto‐catalysts. Our results show that it is essential to consider several transition states corresponding to different reaction orders to enable agreement with experimental observations. Urethane cleavage seems to be catalyzed by an isourethane, leading to an observed 2nd‐order dependence of the reaction rate on the urethane concentration. The results of our study support a deeper understanding of the reactions as well as a better description of reaction kinetics and will therefore help in catalyst development and process optimization.
The capture of carbon dioxide and its utilization as a building block in chemical synthesis aim at reducing the depletion of fossil resources and the emission of greenhouse gases. The project Carbon2Chem®‐L5 is dedicated to the development of processes using CO2 for the production of polymer building blocks for polyurethanes. In this work, a process concept is presented for the CO2‐based synthesis of diisocyanates, the main starting material in polyurethane manufacture. Key parameters for process performance are discussed.
Solvents strongly affect reaction‐based chemical processes. Process design, therefore, needs to integrate solvent design. For this purpose, the integrated computer‐aided molecular and process design (CAMPD) method Rx‐COSMO‐CAMPD is proposed. It employs a hybrid optimization scheme combining a genetic algorithm to explore the molecular design space with gradient‐based optimization of the process. To overcome limitations of molecular design based on group‐contribution methods, reaction kinetics and thermodynamic properties are predicted using advanced quantum‐chemical methods. Rx‐COSMO‐CAMPD is demonstrated in a case study of a carbamate‐cleavage process where promising solvents are designed efficiently. The results show that the integrated solvent and process design with Rx‐COSMO‐CAMPD outperforms computer‐aided molecular design without process optimization in the identification of solvents that enable optimal process performance.
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