Towards the behavior analysis of chemical reactors utilizing data-driven trend analysis and machine learning techniques

Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design

“…One possibility is to use supervised ML as a powerful tool for making a variety of predictions for catalyst and reactor properties, [ 84 , 85 , 86 , 87 , 88 , 89 , 90 ] where relevant features in catalyst and reactor design (i.e., electronegativity, band center, surface area, reaction conditions, and reactor geometry/topology) can serve as input features for training a model on desired outputs (i.e., product formation rate, selectivity, stability, and efficiency). [ 91 , 92 , 93 ] Within photocatalysis, ML has been successfully employed, for example, to predict perovskite materials (using features including from electronegativity, light intensity, photocatalyst quantity, and calcination temperature) [ 94 ] and layered double hydroxides (using elemental and structural features generated from external packages [ 95 , 96 ] and based on local chemical hardness) for water splitting, organic heterojunction photocatalysts (using electronic descriptors such as electron affinity and reorganization energy) for hydrogen production, [ 97 ] and optimal reaction conditions (flow rate and reactor temperature) for the degradation of dyes. [ 98 ] In addition, models have also been built based on literature data for establishing trends and connections for products, catalysts, and material properties for CO 2 photocatalysis in both the gas and liquid phases.…”