Catalyst design using artificial intelligence: SO2 to SO3 case study

Catalyst design is key to the improvement of chemical process efficiency. The required work for the development of new catalysts can be supported through the proper application of artificial intelligence to identify optimal compositions. A generic methodology for the application of machine learning to catalysis research is therefore outlined in this work. The catalytic oxidation of SO₂ was used to exemplify the first iteration of this methodology. 1784 data points from 31 published papers were compiled into a databank. The inlet SO₂ concentration ranged from 0 to 66 mol%. An artificial neural network (ANN) was trained on the databank in order to predict SO₂ conversion based on the catalyst composition and the reactor operating conditions (temperature, pressure, catalyst mass: volumetric flowrate ratio (w/v), and feed composition). The model achieved a root-mean-square error of 6.6%. A preliminary screening step identified 3:1 V-Mg/SiO₂ catalysts as exhibiting high conversion at 648 K. A multi-objective optimization was then performed on a single catalyst to identify solutions exhibiting high conversion and high productivity at 648 K while minimizing the catalyst cost. The optimal solution was predicted to be a 2.9 wt% V/0.2 wt% Mg/SiO₂ catalyst operating at a w/v of 7.49 kg-cat · s/m³ STP, achieving 100% SO₂ conversion with a material cost among the bottom third of cost values. Artificial intelligence can then be employed to extract useful knowledge from published catalytic data and orient future search for novel catalyst development.