Intensification of catalytic reactors: A synergic effort of Multiscale Modeling, Machine Learning and Additive Manufacturing

The intensification of catalytic reactors is expected to play a crucial role to address the challenges that the chemical industry is facing in the transition to more sustainable productions. An advanced design paradigm is necessary to develop customized and process-tailored reactor solutions able to provide the optimal operating conditions, transport properties and geometry. This can be achieved by a detailed understanding of the catalyst functionality in the reactive environment. Multiscale Modeling provides such in-depth insights into the complex physical-chemical phenomena enabling to achieve a first-principles-based understanding and design of the most suitable reactor geometry and configuration. To overcome the intrinsic complexity of the approach, Machine Learning can be synergically employed to reduce the computational cost fostering the inclusion of detailed numerical simulations since the early stage of the design process. Moreover, hybrid machine learning models trained with the data and enforced by the physics are envisioned to assist the work of designers facilitating the development of disruptive intensified solutions. The manufacturing of these unconventional systems requires adequate techniques. Additive Manufacturing is showing enormous potential in this direction and their future developments are expected to make it possible to routinely fabricate intensified reactors.