Intensification of Kinetic Studies for a Multi-step Reaction in a Milli-structured Plate Reactor by using Model-based Design of Experiments

In the context of process intensification, milli-structured plate reactors provide significant advantages over conventional reactors in terms of heat and mass transfer as well as process safety. The ART® plate reactor PR37 of Ehrfeld Mikrotechnik GmbH offers excellent heat transfer, narrow residence time distributions and high mixing efficiency, while simultaneously allowing an effective scale-up to industrial applications due to its modular set up. This does not only enable the realization of novel process windows exceeding the limits of conventional reactors, but also provides optimal prerequisites for kinetic modelling due to the well-defined process conditions, providing key information regarding process design and optimization. The integration of the ART PR37 with Model-based Design of Experiments (MBDoE) allows for an intensification of kinetic studies, combining the well-defined operating conditions with a rapid and targeted identification of kinetic models. In the current study this combination is applied to successfully identify the kinetics of a multi-step aromatic nucleophilic substitution reaction with low experimental effort, saving time and resources compared to conventional factorial Design of Experiments

Topology-Based Initialization for the Optimization-Based Design of Heteroazeotropic Distillation Processes

Distillation-based separation processes, such as extractive or heteroazeotropic distillation, present important processes for separating azeotropic mixtures in the chemical and biochemical industry. However, heteroazeotropic distillation has received much less attention than extractive distillation, which can be attributed to multiple reasons. The phase equilibrium calculations require a correct evaluation of phase stability, while the topology of the heterogeneous mixtures is generally more complex, comprising multiple azeotropes and distillation regions, resulting in an increased modeling complexity. Due to the integration of distillation columns and a decanter, even the simulation of these processes is considered more challenging, while an optimal process design should include the selection of a suitable solvent, considering the performance of the integrated hybrid process. Yet, the intricate mixture topologies largely impede the use of simplified criteria for solvent selection. To overcome these limitations and allow for a process-based screening of potential solvents, the current work presents a topology-based initialization and optimization approach for designing heteroazeotropic distillation processes. The systematic initialization enables an efficient evaluation of different solvents with different mixture topologies, which is further exploited for optimization-based sensitivity analysis and multi-objective optimization. Three case studies are analyzed with about 170 individually optimized process designs, including stage numbers, feed locations, phase ratios, and heat duties

Using Adsorption Energy Distribution for Parameter Estimation of Competitive Cofactor Coupled Enzyme Reaction

The chemical and biotechnology industries are facing new challenges in the use of renewable resources. The complex nature of these materials requires the use of advanced techniques to understand the kinetics of reactions in this context. This study presents an interdisciplinary approach to analyze cofactor coupled enzymatic two-substrate kinetics and competitive two-substrate kinetics in a fast and efficient manner. By studying the adsorption energy distribution (AED), it is possible to determine the individual parameters of the reaction kinetics. In the case of a single alcohol reaction, the AED is able to identify parameters in agreement with the literature with few experimental data points compared to classical methods. In the case of a competitive reaction, AED analysis can automatically determine the number of competing substrates, whereas traditional nonlinear regression requires prior knowledge of this information for parameter identification