Abstract
The combinatorial approach is widely used for homogeneous and heterogeneous catalyst development. The main key technologies are “combinatorial chemistry (CC)” for material preparation and “high-throughput screening (HTS)” for rapid assay using automated and/or robotic equipment. A HTS reactor with 96 parallel lines was designed and manufactured to optimize the Cu-Zn catalyst for methanol synthesis. A neural network (NN) was constructed from the “catalyst composition-activity” dataset obtained by the HTS reactor. The catalyst composition was optimized by a genetic algorithm combined with the trained NN. Active Cu-Zn catalysts for methanol synthesis under CO2 rich syngas were discovered by these combinatorial tools.